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+Add a support-mix bake-off (tools/bakeoff_us_support_mix.py, us_runtime/support_mix.py): CPS years vs ACS rows vs CPS location clones at fixed household budgets, calibrated with the production solver and scored on the hash-split holdout; memo in docs/us-support-mix-bakeoff.md.
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+# US support mix: CPS years vs ACS rows at a fixed household budget
+
+Status: the bake-off ran from 2026-09-28 to 2026-09-30 on main `bda72cb02`,
+policyengine-us 2.2.1 and the pinned Chronicle feed `b8543739` (32,842
+targets), and scored all 60 calibrations (30 arms × 2 products). Receipts:
+[`experiments/us-support-mix-bakeoff-20260930/`](../experiments/us-support-mix-bakeoff-20260930/).
+Tool: [`tools/bakeoff_us_support_mix.py`](../tools/bakeoff_us_support_mix.py);
+primitives and invariants:
+[`us_runtime/support_mix.py`](../packages/microcosm-build/src/microcosm/build/us_runtime/support_mix.py).
+
+## Question
+
+Max, 2026-09-27: the ACS matters mainly as variation in the support, meaning
+more distinct households for calibration to choose among. Pooling more CPS
+years adds support another way, at full CPS richness. Cloning CPS households to
+new locations adds rows that differ only in geography. At a household budget we
+can afford to build and serve, which mix gives the best held-out accuracy, for
+the national compact file and for the CD and county local file?
+
+## Verdict
+
+The memo ranks arms by the release's own objective on held-out targets: the
+loss-weighted capped relative error over every held-out CPS-native target
+(income components, tax items, program receipt) at a level, with the release's
+fixed concept-budget weights. Per-dimension columns show the trade-offs.
+
+- **National compact file: the newest three CPS years plus ACS to 300,000
+  source households.** With 2022–2024 that comes to 168,852 CPS households plus
+  131,148 ACS households, 484,080 physical rows (+37% over the three-year CPS
+  file's 352,932). Against the CPS-only file, as means over three ACS draws:
+  - held-out state CPS-native error falls from 0.119 to 0.113 (draw SD
+    0.0003) and district error from 0.353 to 0.291;
+  - state income components improve (0.250 to 0.224), state tax items get
+    worse (0.103 to 0.113), and state program receipt does not measurably
+    change (0.208 against 0.198, draw SD 0.014);
+  - every ACS-native level improves (district 0.236 to 0.188, county 0.307
+    to 0.259).
+
+  The recommendation stops at 300k to hold down build and serving cost. Larger
+  ACS fills keep lowering district error (0.291, 0.266, 0.244 at 300k, 600k,
+  1.2M). They leave state error flat at 300k and 1.2M (0.113) and slightly
+  worse at 600k (0.115), edge state tax items up (0.113, 0.116, 0.118) and do
+  not help state program receipt (0.198, 0.225, 0.239; the 600k and 1.2M arms
+  each have one draw, and only the 1.2M gap exceeds draw noise). The 600k 50/50
+  fill scores best at state level (0.111; income 0.213, tax items 0.112,
+  program receipt 0.187) with 1,019,084 rows, 2.1 times the recommendation's.
+  Against one CPS year plus ACS to 300k, three years buy state income (0.249 to
+  0.224) and tax items (0.130 to 0.113) with ACS-native accuracy (state 0.086
+  to 0.110, district 0.166 to 0.188). The recommendation puts the CPS-native
+  gains first; if ACS-native accuracy matters as much for the national file,
+  one year plus ACS offers the alternative. If state tax items and serving cost
+  outweigh everything else, the three-year CPS-only file wins.
+
+- **CD and county local file: the newest three CPS years plus ACS households to
+  1.2M source households, with no CPS location clones.** That comes to
+  1,384,080 physical rows, 13% fewer than today's 1,588,854-row ACS local file.
+  At 1.2M:
+  - held-out district CPS-native error ties the clones (0.169 against 0.170) at
+    55% of their rows, and state error falls below theirs (0.112 against
+    0.114);
+  - ACS-native error runs 44% below the clones' in districts (0.141 against
+    0.253) and 31% below in counties (0.174 against 0.253);
+  - the clones, and the CPS-only file, do better on state program receipt
+    (0.218 and 0.220 against 0.256) and state tax items (0.096 and 0.101
+    against 0.117). The 50/50 fill at 600k splits the difference (program
+    receipt 0.243, tax items 0.096, district ACS-native 0.209).
+
+  Against an ACS-only file of the same size (today's local file holds 57,240
+  CPS rows plus every ACS household), the three CPS years lower state
+  CPS-native error from 0.120 to 0.112, state program receipt from 0.318 to
+  0.256 and state tax items from 0.127 to 0.117, and do not raise ACS-native
+  error. One CPS year instead of three saves 123k rows (9%) at 1.2M. There the
+  data do not separate them on CPS-native measures: district error ties (0.169
+  each), and the state gaps (0.116 against 0.112; tax items 0.123 against
+  0.117; program receipt 0.278 against 0.256, single draws) sit within the
+  one-year arms' draw noise, or for state error at its edge. One year does
+  better on ACS-native state and district measures (0.072 and 0.136 against
+  0.078 and 0.141). Three years win at 300k, where each has three draws:
+  clearly on state error (0.119 against 0.112) and tax items (0.137 against
+  0.103), and just beyond draw noise on program receipt (0.288 against 0.237).
+  The three-year choice rests on that, on the national evidence below, and on
+  the local file sharing the national file's CPS support.
+
+- **More CPS years improve CPS-native measures and cost ACS-native ones.**
+  Within a budget, each CPS year displaces ACS households. In the national file
+  at 300k, one year against three (three draws each): state income 0.249
+  against 0.224 and tax items 0.130 against 0.113, both well beyond draw noise;
+  program receipt 0.241 against 0.198 falls within the one-year arms' draw
+  noise (SD 0.041), and so does the loss-weighted state error (0.117 against
+  0.113, SD 0.002). ACS-native error rises (state 0.086 to 0.110, district
+  0.166 to 0.188; county level at 0.255 and 0.259). The local file at 300k
+  shows the same trade more strongly: tax items (0.137 to 0.103) and program
+  receipt (0.288 to 0.237) improve, state income holds level, and ACS-native
+  error rises by a third or more (state 0.080 to 0.110, district 0.153 to
+  0.204). At 1.2M the trade shrinks (see above). Income years 2020 and 2021
+  pass the response-quality check, so the next arm to run pools five years;
+  this bake-off did not run it (see Follow-ups).
+
+- **Clones add geography and little else.** In the national file they leave
+  every state measure where the CPS-only file had it. In both files they
+  improve district measures slowly (national-file district CPS-native error
+  0.353, then 0.332 at 300k) and household ACS-native measures barely
+  (local-file county 0.299, then 0.263 and 0.253 at 600k and 1.2M), at 1.3 to
+  2.0 times the physical rows of an ACS fill of the same budget (1.3 to 1.8
+  for three-year clones).
+
+This matches the design note's reasoning. ACS rows carry real variation in
+what the ACS observes, so income components (the ACS records wages,
+self-employment, Social Security, retirement and interest, dividend and rental
+income), housing and local geography improve as ACS rows enter. ACS rows
+draw program receipt from CPS donors: beside three CPS years, state program
+receipt error holds level at 131k ACS households, turns clearly worse by 1.2M
+(0.239), and reaches 0.303 for ACS alone at 300k.
+
+## Results
+
+Every column reports capped relative error on held-out targets, `min(|est −
+target| / max(|target|, 1), 1)`; lower is better. The "CPS-native" columns take
+the loss-weighted mean over every held-out CPS-native target at the level; the
+per-dimension columns take plain means. "ACS-native" averages households, owner
+and renter households, contract rent, real-estate taxes and household
+population against the ACS 2024 1-year tables;
+`acs_native_by_level_measure.csv` adds age bands, which count group-quarters
+people only the ACS rows have. The Rows column counts physical household rows
+(a CPS household carries its PUF tax-detail clone rows); ESS takes Kish's
+formula over distinct households (each ASEC housing unit counts once across
+rotation years, PUF rows and clones). The "3 draws" rows average three disjoint
+ACS draws; each other row comes from one draw. `memo_tables.py` builds the
+tables from `dimensions_by_arm.csv`.
+
+### National product (4,828 train-role national and state targets)
+
+| Arm | Rows | ESS | CPS-native, state | CPS-native, CD | Income, state | Tax items, state | Program receipt, state | Age (PEP), state | ACS-native state / CD / county |
+|---|---|---|---|---|---|---|---|---|---|
+| CPS 2022–24 only | 352,932 | 44,943 | 0.119 | 0.353 | 0.250 | 0.103 | 0.208 | 0.169 | 0.133 / 0.236 / 0.307 |
+| CPS 2022–24 + clones to 300k | 627,089 | 44,984 | 0.119 | 0.332 | 0.250 | 0.103 | 0.208 | 0.169 | 0.133 / 0.226 / 0.283 |
+| CPS 2024 + ACS to 300k (3 draws) | 361,228 | 64,090 | 0.117 | 0.300 | 0.249 | 0.130 | 0.241 | 0.159 | 0.086 / 0.166 / 0.255 |
+| CPS 2023–24 + ACS to 300k | 422,543 | 65,908 | 0.119 | 0.288 | 0.236 | 0.124 | 0.217 | 0.171 | 0.097 / 0.175 / 0.251 |
+| **CPS 2022–24 + ACS to 300k (3 draws)** | 484,080 | 69,353 | 0.113 | 0.291 | 0.224 | 0.113 | 0.198 | 0.163 | 0.110 / 0.188 / 0.259 |
+| ACS only, 300k | 300,000 | 60,492 | 0.132 | 0.289 | 0.261 | 0.151 | 0.303 | 0.176 | 0.096 / 0.172 / 0.269 |
+| CPS 2022–24 + 50/50 to 600k | 1,019,084 | 77,830 | 0.111 | 0.272 | 0.213 | 0.112 | 0.187 | 0.163 | 0.113 / 0.182 / 0.232 |
+| CPS 2022–24 + ACS to 600k | 784,080 | 113,041 | 0.115 | 0.266 | 0.221 | 0.116 | 0.225 | 0.159 | 0.097 / 0.163 / 0.227 |
+| CPS 2022–24 + ACS to 1.2M | 1,384,080 | 222,920 | 0.113 | 0.244 | 0.217 | 0.118 | 0.239 | 0.166 | 0.081 / 0.135 / 0.189 |
+| SD across 3 draws (300k, 2022–24) | | | 0.000 | 0.002 | 0.007 | 0.002 | 0.014 | 0.005 | 0.001 / 0.001 / 0.002 |
+
+### Local product (adds the 27,148 district-classified targets and district household population)
+
+| Arm | Rows | ESS | CPS-native, state | CPS-native, CD | Income, state | Tax items, state | Program receipt, state | Age (PEP), state | ACS-native state / CD / county |
+|---|---|---|---|---|---|---|---|---|---|
+| CPS 2022–24 only | 352,932 | 52,074 | 0.116 | 0.209 | 0.217 | 0.101 | 0.220 | 0.154 | 0.134 / 0.263 / 0.299 |
+| CPS 2022–24 + clones to 600k | 1,254,039 | 54,305 | 0.114 | 0.179 | 0.211 | 0.097 | 0.221 | 0.175 | 0.134 / 0.256 / 0.263 |
+| CPS 2022–24 + clones to 1.2M | 2,508,197 | 54,292 | 0.114 | 0.170 | 0.214 | 0.096 | 0.218 | 0.183 | 0.135 / 0.253 / 0.253 |
+| CPS 2022–24 + 50/50 to 600k | 1,019,084 | 91,100 | 0.111 | 0.173 | 0.226 | 0.096 | 0.243 | 0.176 | 0.113 / 0.209 / 0.232 |
+| CPS 2022–24 + ACS to 600k | 784,080 | 150,595 | 0.115 | 0.179 | 0.228 | 0.109 | 0.267 | 0.168 | 0.092 / 0.166 / 0.207 |
+| **CPS 2022–24 + ACS to 1.2M** | 1,384,080 | 276,474 | 0.112 | 0.169 | 0.206 | 0.117 | 0.256 | 0.184 | 0.078 / 0.141 / 0.174 |
+| CPS 2024 + ACS to 1.2M | 1,261,228 | 281,840 | 0.116 | 0.169 | 0.204 | 0.123 | 0.278 | 0.188 | 0.072 / 0.136 / 0.174 |
+| ACS only, 1.2M | 1,200,000 | 272,607 | 0.120 | 0.170 | 0.206 | 0.127 | 0.318 | 0.193 | 0.084 / 0.146 / 0.180 |
+| SD across 3 draws (300k, 2022–24) | | | 0.001 | 0.002 | 0.007 | 0.002 | 0.006 | 0.002 | 0.000 / 0.002 / 0.000 |
+
+We read differences under about twice the draw SD as noise. The one-year arms
+vary more across draws than the three-year arms (program receipt SD 0.041
+national and 0.024 local at 300k; `replicate_spread.csv`). Too few to rank
+arms, the 36 national-level held-out targets stay out of the tables;
+`holdout_by_dimension_level.csv` has them.
+
+**Calibration worsens held-out state age cells, in every arm.** Across all 60
+calibrations, the mean capped error on held-out Census PEP state age bands
+rises from 0.066 at the starting weights to 0.168 after calibration (0.131 to
+0.193). The design weights already carry Census age controls, and the solve
+trades them away for its training targets. The tables report the age column but
+rank no arm on it: it does separate arms (national 0.159 to 0.176, draw SD
+0.005), but every arm pays the trade, and its size says more about the
+calibration objective than about the support (see Follow-ups).
+
+**Report only: SPM poverty** (no arm calibrates to it or gates on it). In the
+national product the SPM rate comes to 12.8% for the three-year CPS file, 12.2%
+for the recommended 300k mix (seed 0) and 12.3% for ACS only, and child SPM
+poverty to 14.7%, 13.6% and 12.3%. These rates include the ACS group-quarters
+persons still in the support, whom Census places outside the SPM universe; the
+tool now leaves them out, but these rates predate that fix. ACS rows' SPM
+resources rest on imputed CPS-only inputs, so the differences reflect the
+imputation as much as the support.
+
+## Cost
+
+| | Measured |
+|---|---|
+| Engine pass (all 419 concepts), wall time per household on the shared host | CPS parts 7.7 to 57 ms (median 26); ACS parts 7.5 to 226 ms (median 39); 5,000-household engine batches |
+| Engine pass peak RSS | 50,000-household CPS shards 5.8 to 15.6 GB; up to 22.7 GB per ACS process (a running maximum over its parts) |
+| Calibration, national recommendation (484k rows, 1,500 epochs) | 22 min, 6.2 GB peak |
+| Calibration, local recommendation (1.38M rows, 1,500 epochs) | 36 min, 15.9 GB peak |
+| Calibration, local clones to 1.2M (2.51M rows) | 55 to 210 min, 18 to 28 GB peak |
+| Serving | proportional to physical rows: 484k national (+37% over CPS only), 1.38M local (−13% against today's local file) |
+
+We measured wall times with the host at load 10 to 400, so they compare only
+within this run. Training loss still fell a median 5.2% of its epoch-1,000
+value between epochs 1,000 and 1,500 (`arms.csv` has `loss_1000` and
+`loss_final`); every arm had the same 1,500-epoch budget. The tool first
+scored 22 arms before scoring covered every level; for those, `arms.csv`
+carries the solve peak from the grid log and no wall time (`runtime_restored`
+names the log line). We added each arm receipt's `inputs` digests after its
+solve, once the tool recorded them, from the files then in place, and each
+receipt's `inputs_note` said so. We likewise added source-h5, concept-spec
+and null-fill-summary digests afterwards to the work copies of the
+concept-part receipts, from `shard.json`, `concept_specs.pkl` and the
+`partcheck` below (`*_note` fields). The copies under `materialize/` predate
+those digests; on 2026-09-30 their numerical fields matched the work
+copies'.
+
+## Design
+
+### Arms
+
+Budgets count **source households**, as in the design note: each ASEC
+household-year or ACS household counts once, so the 2022–2024 ASEC pool holds
+168,852. A CPS source household keeps its PUF tax-detail clone rows from the
+Route A pipeline, so it spans two or three physical rows. The tables report
+physical rows separately, and physical rows drive engine and serving cost.
+
+Each arm keeps every household of its CPS income years and fills the rest of
+the budget with:
+
+- **ACS** households (`acs100`): the first *n* in a salted hash order of
+  `SERIALNO`, so a larger arm's ACS households always include a smaller arm's;
+- **CPS location clones** (`acs000`): copies of the arm's own CPS households,
+  each physical row drawing a new (district, county) within its state in
+  proportion to ACS household weight (leaving out group-quarters rows), the
+  way Route A draws rows independently. A clone keeps its source row's engine
+  values, so anything that varies by county within a state (ACA benchmark
+  premiums by rating area) comes from the origin county;
+- a 50/50 mix (`acs050`, at 600k only).
+
+| Budget | CPS years | Fills |
+|---|---|---|
+| natural size | 2024; 2023–24; 2022–24 | none |
+| 300k, 600k, 1.2M | 2024; 2023–24; 2022–24 | ACS; clones |
+| 300k, 600k, 1.2M | none | ACS only |
+| 600k | 2024; 2022–24 | 50/50 |
+| 300k | 2024; 2022–24 | ACS, replicates 1 and 2 |
+
+That makes 30 arms. Each arm gets two calibrations, one per product:
+
+- **national compact**: the release's `national_state` surface (5,683
+  usable national and state targets, 4,828 of them train-role after the
+  split), with loss weights the tool computes on that surface as the release
+  does;
+- **local**: the full surface, which adds 27,148 targets the release
+  classifies as district targets (24,340 district-level, plus 2,808 state
+  and national totals from the SOI district file), plus district household
+  population (ACS B25008). The ACS local release trains district population
+  from its PUMA ladder instead; this bake-off uses household population so
+  that the district total neither counts nor moves group-quarters rows, which
+  only the ACS has.
+
+Replicate arms take the next disjoint blocks of the one fixed ACS order, so
+they draw independently; seed-0 arms nest across budgets. Group-quarters
+households (11.96% of ACS records) stay in the ACS side, as in the ACS local
+release; the CPS has none. The bake-off drops the 5,246 ACS records (0.34%, all
+group quarters) whose SPM unit has no member the pinned engine counts as an
+adult: policyengine-us 2.2.1 refuses them, and Census puts ACS group quarters
+outside the SPM universe (`us_runtime/spm_universe_source.py`). `shard` and
+`spm-flags` compute the flag with the engine's rule (`spm_flags.json`).
+
+**This bake-off ran no five-year arm.** Income years 2020 and 2021 pass the
+response-quality check below, but main pinned ASEC role and person sources only
+for 2022–2024 when this ran, and the base and release stages that would enrich
+two more years peaked at 72–80 GB in recorded runs, beyond this task's 60 GB
+ceiling, while a Route A release held the machine. The tool takes more years as
+more rows, so a rerun can add a five-year arm once a Route A export with those
+years exists (see Follow-ups). The same holds for the newest three years
+(2023–2025): main pinned the 2025 ASEC on 2026-09-29
+(PolicyEngine/microcosm#1044), after the Route A export these CPS rows come
+from.
+
+### Superpool: one engine pass, many arms
+
+Rerunning the pipeline per arm would cost hours and 70–94 GB each. Instead
+the bake-off computes every household's target contributions once and
+reweights subsets:
+
+- **CPS rows**: the Route A 310842b release export (352,932 physical rows:
+  income years 2022–2024, PUF clones, all enrichment stages), with design
+  weights from its base. The run failed only its post-export smoke gate.
+- **ACS rows**: the 2026-09-23 ACS local staging (1,531,614 ACS 2024 1-year
+  households, whose CPS-only inputs the staging transferred from the
+  57,240-household receipt-qualified donor spine). The bake-off leaves out
+  the staging's own 57,240 donor rows. The staging carries
+  `is_spm_independent_minor_role` as all-null on ACS rows; the tool drops the
+  column so the engine derives the role from its formula
+  (`is_household_head | is_household_spouse`), as the production ACS local
+  build did.
+
+Both carry the same PolicyEngine input schema (141 and 140 person inputs).
+Imputations stay the same across arms: an ACS row's CPS-only variables come
+from the same donor spine whatever CPS years the arm pools. The bake-off
+therefore measures the support effect while holding donor quality constant;
+in a real rebuild, a one-year arm would also have a thinner donor pool.
+
+**Concepts.** The release materializer builds each state or district
+target's household column as a geography-free value times an indicator of
+the household's own state or district
+(`_base_simulation_household_columns`). `compile` groups the 32,842 targets
+into 419 such concepts, and `materialize` runs the release tool's own
+`_materialize_target_frame` on them in household shards. The tool then
+assembles every arm's target matrix, sparse, as concept value × geography
+mask. `diffcheck` verifies the identity on real rows (see Evidence). The 11
+JCT tax-expenditure targets need reform simulations, so every arm leaves them
+out.
+
+### Holdout
+
+The US holdout ("Port the UK holdout to US release targets") had not merged
+when this ran. The bake-off mirrors its pending spec: it hashes
+`salt + "\x1f" + key` with SHA-256, seals a group whose hash on the sealed
+salt falls below 0.05, and otherwise holds the group out if its hash on the
+holdout salt falls below 0.10. Tests check the hash against draws from the
+port branch's own implementation (`PORT_BRANCH_FIXTURES`); the group-key and
+role rules follow the port session's written spec, since that session had
+not yet written its module. District children take their state parent's
+key, so holding out a state total holds out all its districts; the split
+draws national and state targets independently, so trained state targets
+can still pin a held-out national total. Keys drop vintage tokens. The
+bake-off does not mirror the spec's pins, which force some groups to train,
+so this split differs from the eventual release split for pinned groups. No
+arm trains on or scores a sealed target.
+
+On the pinned feed: 27,711 train, 3,470 holdout (36 national, 787 state,
+2,647 district), 1,661 sealed.
+
+**ACS-native truth.** The registry has no housing targets and no county
+targets. The bake-off scores ACS-native dimensions against the ACS 2024
+1-year table-based summary file (B25003 tenure, B25060 and B25065 aggregate
+rent, B25090 aggregate real-estate taxes, B25008 and B01003 population,
+B01001 age and sex) at national, state, district (119th Congress) and county
+(the 850 counties the 1-year file covers). No arm calibrates to them, except
+to district household population (B25008) in the local product. These
+tables come from the same ACS year as the ACS rows; the PUMS subsamples the
+published sample, and ACS rows take district and county as draws within
+their PUMA, so ACS rows do not reproduce the tables by construction, but
+they share the published sample's sampling error. ACS-native scores
+therefore measure consistency with the published ACS.
+
+### Calibration
+
+Every arm uses the production optimizer, `microcosm.calibrate.solve._optimize`,
+with the fiscal release's optimizer settings: Adam on log-weights, learning
+rate 0.02, capped relative error (cap 1.0), conserved mass, max weight ratio 5,
+and the release's concept-budget loss weights, which the tool computes once per
+surface (the national_state surface for the national product, the full registry
+for the local one) before the split, so train and holdout keep fixed weights;
+district household-population rows weigh 1 each; no L0 pruning, so the row
+budget stays fixed. A sparse operator with a precomputed transpose replaces the
+torch sparse-CSR backward pass, which had bottlenecked the solve; the objective
+and projections stay the same.
+
+Starting weights: each CPS income year gets an equal share of the CPS mass;
+a household with *k* location copies splits its weight over 1 + *k* rows;
+CPS and ACS sides share mass in proportion to source households; then the
+release's population mass repair rescales to its 334.2M-person benchmark.
+
+**Invariants** (`test_us_support_mix.py` tests each as a property): an arm's
+CPS, ACS and clone households sum to its budget; ACS selections nest and do not
+depend on input order; clone copies differ by at most one; starting weights
+stay positive, sum to the requested mass, give each CPS year an equal share and
+split CPS and ACS in proportion to households; distinct-household ESS never
+exceeds row ESS or the distinct-unit count; the holdout role depends only on
+the group key and never moves with a vintage token. **Differential**: the
+sparse target matrix equals a naive dense construction, including cells that
+carry several targets; `diffcheck` compares production per-target
+materialization with concept × mask on real rows.
+
+### Scoring
+
+- **Registry holdout**: capped relative error by dimension (CPS-native:
+  income components, tax items, program receipt; ACS-native: PEP
+  demographics) and level, as a plain and a loss-weighted mean. A target no
+  row in the arm can move scores as a miss (error 1), so every arm's average
+  covers the same held-out set; a common-support table covers only targets
+  every arm supports. Two held-out targets lack support in every arm, and 22
+  in the one-year CPS-only arm.
+- **ACS-native**: households, tenure, aggregate rent (the model's
+  `pre_subsidy_rent`, against both contract rent B25060 and gross rent
+  B25065) and real-estate taxes, household population (B25008), and total
+  population, age and sex (B01003, B01001), by level including county.
+  Household measures exclude group-quarters rows.
+- **ESS** over distinct households: Kish on each household unit's total
+  weight; also the median across districts.
+- **Cost**: calibration wall time and peak RSS per arm; engine seconds per
+  household from the materialization receipts; physical rows as the serving
+  cost driver.
+- **Report only**: SPM poverty rate, all persons and children.
+
+## CPS year response quality
+
+In `census_cps_YYYY.h5`, YYYY gives the income year; Census collects that
+year's ASEC in YYYY+1. `asec_quality.py` computes the table from the processed
+files and the Census public-use archives; the response rates come from Census.
+
+| Income year (ASEC) | Households | Combined response rate | Earners, whole record imputed | Earners, items allocated | Homeownership | UC recipients |
+|---|---|---|---|---|---|---|
+| 2018 (2019) | 68,345 | 67.6% | 24.2% | 21.3% | 64.5% | 1,723 |
+| 2019 (2020) | 60,460 | 61.1% | 23.8% | 21.3% | 66.4% | 1,563 |
+| 2020 (2021) | 62,850 | 65.0% | 22.4% | 22.6% | 65.8% | 10,915 |
+| 2021 (2022) | 59,148 | 61.4% | 22.4% | 21.8% | 65.4% | 4,065 |
+| 2022 (2023) | 56,839 | 59.6% | 21.9% | 21.7% | 65.9% | 1,225 |
+| 2023 (2024) | 56,251 | 59.3% | 20.7% | 21.0% | 65.6% | 1,360 |
+| 2024 (2025) | 55,762 | 60.1% | 19.3% | 21.5% | 65.1% | 1,400 |
+
+Combined response multiplies basic-CPS and ASEC household response, from each
+year's CPS ASEC Source and Accuracy statement. Imputation shares weight wage
+earners by `A_FNLWGT` (`I_ERNVAL` 9 marks a whole imputed record; 1–8 an
+allocated item). Household weights have a coefficient of variation of 0.598 to
+0.610 and a Kish share of 0.729 to 0.737 in every year. The public-use archives
+give whole-supplement imputation (`FL_665`) of 20.5%, 19.9%, 19.2%, 17.8% and
+16.7% for income years 2021 to 2025.
+
+Income years 2020 and 2021 **pass**: their response rates exceed the pinned
+years'; their whole-record imputation shares sit between the pre-pandemic
+years' and the pinned years'; their item-allocation shares, the highest of
+the seven years (22.6% and 21.8%), stay within 1.6 points of every other
+year; and homeownership and weight dispersion show no break.
+Three caveats:
+
+- Census asks users to take care comparing data years 2019, 2020 and 2021
+  with other years, and documents that nonrespondents in the 2020–2026
+  surveys differ more from respondents than before (2026 CPS ASEC technical
+  documentation). That caution covers the pinned 2022–2024 years too.
+- Their income years carry pandemic program receipt: 10,915 and 4,065
+  unemployment-compensation recipients, against 1,225 to 1,723 in other
+  years. That matters for program-receipt targets.
+- About a third of each file's households repeat in the neighbouring year
+  (32.5% to 37.8%).
+
+Income year 2019 (ASEC 2020, which Census collected as COVID hit) fails:
+homeownership jumps 1.9 points (64.5% to 66.4%) and falls back the next year,
+and the sample has 11.5% fewer households than the year before (60,460
+against 68,345).
+
+Rotation overlap: a housing unit appears in at most two adjacent files, so
+2022–2024 has 130,294 distinct units in 168,852 households (77.2%), and
+2020–2024 would have 211,834 in 290,850 (72.8%).
+
+## Caveats
+
+- **The CPS rows cover income years 2022–2024**, from the Route A export
+  available when this ran. The recommendation names "the newest three
+  years"; a rerun would use the 2023–2025 export.
+- **Imputations stay the same across arms.** ACS rows' CPS-only inputs come
+  from one 57,240-household donor spine in every arm. A production one-year build
+  would also impute from a thinner donor pool.
+- **Clones keep their origin row's engine values**, including anything that
+  varies by county within a state.
+- **The holdout mirrors the unmerged US split without its pins.** Scores
+  compare across arms, but the eventual release split would give different
+  ones.
+- **ACS-native scores use the published ACS 2024 1-year tables**, from the same
+  survey year as the ACS rows, so they measure consistency with the published
+  ACS.
+- **Nothing uprates older CPS years**, as in Route A; calibration absorbs the
+  2022–2024 nominal income gap.
+- **The ACS support leaves out 5,246 group-quarters records with no SPM
+  adult**, pending the engine's SPM-universe input (policyengine-us#9462).
+- **Every arm leaves out the JCT tax-expenditure targets**, which need reform
+  simulations.
+
+## Follow-ups
+
+- Rerun on the newest three CPS years (2023–2025) from a Route A export of main
+  at or after `5187fce25`. The run's work directory no longer exists, so a
+  rerun takes every step under Reproduce below, with `--newest-year 2025` on
+  `run-grid` and `report`. Within a rerun, `materialize` refuses a concept part
+  that lacks a receipt or whose receipt names another source h5, other concept
+  specs or another null-fill summary, unless run with `--replace-stale`, and an
+  arm's receipt counts as current only while every input the arm reads stays
+  the same: the targets, truth and epoch count, and, for each source it draws
+  on, the row index, the concept parts, the concept specs and the measure list.
+- Add five-year arms (2020–2024 or 2021–2025) once those years have Route A
+  enrichment.
+- When `microcosm.build.us_runtime.target_split` merges, swap it in and
+  **re-solve** every arm: a new split changes the training set.
+- Find out why calibration worsens held-out state age cells 2.5-fold.
+
+## Evidence
+
+- Differential: production per-target materialization against concept × mask,
+  4,000 households from each source, 349 state and district targets across nine
+  family-level groups: maximum scaled difference 0.0 for both
+  (`diffcheck.json`). The tool wrote the ACS half on 2026-09-29; the CPS half
+  reproduces the tool's own log line from 2026-09-28, which we copied into the
+  file after removing the CPS shard files to free disk.
+- Stored parts against the final concept specs: `partcheck` re-materialized
+  200 households from each of the 8 CPS parts and 8 of the 62 ACS parts
+  (both rank ranges) with the final specs and the branch's code, and compared
+  every column with the stored values: maximum scaled difference 5.8e-8,
+  float32 rounding (`partcheck_cps.json`, `partcheck_acs.json`). Three CPS
+  parts predate the last `compile`, which added loss weights only; this check
+  confirms their columns. For the check we rebuilt the CPS shards from the
+  same h5, and their row index equals the one the arms used.
+- On 2026-09-30, rerunning `report` on the 60 arm receipts reproduced every
+  committed table byte for byte. Those receipts (10–15 MB each, with every
+  held-out target's estimate) and the saved weights stayed in the run's work
+  directory, which no longer exists, so the committed tables now carry the
+  record.
+- Tests: `test_us_support_mix.py`, 22 tests (properties, the dense
+  differential and the port-branch hash fixtures), and
+  `test_bakeoff_us_support_mix_tool.py`, 24 tests of the driver's SPM flag,
+  staleness guards, `partcheck` rules, grid and report.
+- After the run, the tool gained four guards a final review asked for:
+  receipt digests also cover the concept specs and the measure list,
+  `partcheck` fails a column with any nonfinite value and refuses an unknown
+  or empty part selection, and `--allow-stale` alone now writes a report.
+  The recorded checks had finite differences and matched every part they
+  named, so no committed number moves.
+- Receipts in `experiments/us-support-mix-bakeoff-20260930/`: the report
+  tables, `compile.json`, `diffcheck.json`, `partcheck_*.json`,
+  `spm_flags.json`, `asec_quality.json` (with its script), `acs_truth.json`,
+  and the per-part materialization receipts under `materialize/`.
+
+Reproduce: `compile`, `shard` (both sources), `truth`, `materialize` (both
+sources; ACS by rank range), `diffcheck`, `partcheck` (both sources),
+`run-grid --products national` and `--products local`, `report --truth`, then
+`memo_tables.py`. Each step writes a receipt with wall time and peak RSS.
diff --git a/experiments/us-support-mix-bakeoff-20260930/README.md b/experiments/us-support-mix-bakeoff-20260930/README.md
new file mode 100644
index 0000000000..291991c8d2
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/README.md
@@ -0,0 +1,32 @@
+# Support-mix bake-off receipts (2026-09-30)
+
+Outputs of `tools/bakeoff_us_support_mix.py report` over the 60 arm receipts
+(30 arms × national and local products), plus the compile and differential
+receipts. [`docs/us-support-mix-bakeoff.md`](../../docs/us-support-mix-bakeoff.md)
+gives the design, verdict and caveats.
+
+| File | What |
+|---|---|
+| `dimensions_by_arm.csv` | One row per arm and product: rows, distinct-household ESS, held-out error by dimension and level, ACS-native error by level |
+| `headline.md` | The same, as markdown tables by product |
+| `arms.csv` | Per-arm counts, ESS variants, training loss, solve time, peak RSS, report-only SPM rates |
+| `holdout_by_dimension_level.csv` | Held-out error by product, arm, dimension group, dimension and level (unsupported targets score as misses) |
+| `holdout_by_dimension_level_common_support.csv` | The same over targets every arm supports |
+| `holdout_unsupported.csv` | Held-out targets no row in an arm can move |
+| `replicate_spread.csv` | SD across three disjoint ACS draws (300k arms) |
+| `replicate_mean.csv` | Mean across the same three draws |
+| `acs_native_by_level_measure.csv` | ACS-native error by product, arm, level and measure |
+| `compile.json` | Registry compile: 32,842 specs, 419 concepts, split roles by level |
+| `diffcheck.json` | Production per-target columns vs concept × mask on 4,000 households per source: max scaled difference 0.0 |
+| `acs_truth.json` | ACS 2024 1-year summary-file cells and their SHA-256 |
+| `partcheck_cps.json`, `partcheck_acs.json` | Stored concept parts against a fresh materialization, under the final concept specs, of 200 sampled households per part: max scaled difference 5.8e-8 |
+| `spm_flags.json` | ACS group-quarters share and the SPM zero-adult records the support leaves out (`spm-flags`) |
+| `materialize/` | Per-part engine-pass receipts (households, wall time, peak RSS) and the measure list |
+| `asec_quality.py`, `asec_quality.json` | CPS year response-quality check (script and its output) |
+| `memo_tables.py` | Builds the memo's results tables from these files |
+
+The folder holds no per-arm receipts (10–15 MB each, with every held-out
+target's estimate) and no saved weights. Both stayed in the run's work
+directory, which no longer exists. The `materialize/` receipts predate the
+source-h5, concept-spec and null-fill-summary digests the memo describes; on
+2026-09-30 their numerical fields matched the work copies'.
diff --git a/experiments/us-support-mix-bakeoff-20260930/acs_native_by_level_measure.csv b/experiments/us-support-mix-bakeoff-20260930/acs_native_by_level_measure.csv
new file mode 100644
index 0000000000..0d80fcb4a8
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/acs_native_by_level_measure.csv
@@ -0,0 +1,2851 @@
+product,arm,level,measure,geos,mean_rel,median_rel,mean_rel_initial
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diff --git a/experiments/us-support-mix-bakeoff-20260930/acs_truth.json b/experiments/us-support-mix-bakeoff-20260930/acs_truth.json
new file mode 100644
index 0000000000..9d34511f96
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/acs_truth.json
@@ -0,0 +1,71 @@
+{
+ "cells": {
+  "households": [
+   "B25003_E001"
+  ],
+  "owner": [
+   "B25003_E002"
+  ],
+  "persons_0_17": [
+   "B01001_E003",
+   "B01001_E004",
+   "B01001_E005",
+   "B01001_E006",
+   "B01001_E027",
+   "B01001_E028",
+   "B01001_E029",
+   "B01001_E030"
+  ],
+  "persons_65p": [
+   "B01001_E020",
+   "B01001_E021",
+   "B01001_E022",
+   "B01001_E023",
+   "B01001_E024",
+   "B01001_E025",
+   "B01001_E044",
+   "B01001_E045",
+   "B01001_E046",
+   "B01001_E047",
+   "B01001_E048",
+   "B01001_E049"
+  ],
+  "persons_in_households": [
+   "B25008_E001"
+  ],
+  "persons_male": [
+   "B01001_E002"
+  ],
+  "persons_total": [
+   "B01003_E001"
+  ],
+  "real_estate_taxes_aggregate": [
+   "B25090_E001"
+  ],
+  "rent_contract_monthly_aggregate": [
+   "B25060_E001"
+  ],
+  "rent_gross_monthly_aggregate": [
+   "B25065_E001"
+  ],
+  "renter": [
+   "B25003_E003"
+  ]
+ },
+ "files_sha256": {
+  "acsdt1y2024-b01001.dat": "84db6a3a8eda54d882b9a5e6a5bb48864fcbb573a262127ed026ae5fb6d6953e",
+  "acsdt1y2024-b01003.dat": "3f04d8c2ad77dab54085f014b795c33c19463eede4539e110247bb329d1025fd",
+  "acsdt1y2024-b25003.dat": "944b9d7aa807cc32c50a50b37af0afac50db914f02dc68bafa628e4227a496ad",
+  "acsdt1y2024-b25008.dat": "6e8e40d7679c47b90df6504c449bde6b28c4384c329b68af0dd436c6943418ca",
+  "acsdt1y2024-b25060.dat": "aa6d70941599febec9c35e60d8692df6bbc2c01146d2d18d912d9bd24dbfd457",
+  "acsdt1y2024-b25065.dat": "e17f8ebae8941b0f7466d0e3cdad5cca6425b000ab88d001ddac192bce540f0e",
+  "acsdt1y2024-b25090.dat": "60f95c0745c34d9bd9d4dfca86d09947f463ef61c76d0e525b0b4b80a018810b"
+ },
+ "rows_by_level": {
+  "cd": 5232,
+  "county": 10200,
+  "national": 12,
+  "state": 612
+ },
+ "source": "ACS 2024 1-year table-based summary file (Census), https://www2.census.gov/programs-surveys/acs/summary_file/2024/table-based-SF/"
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/arms.csv b/experiments/us-support-mix-bakeoff-20260930/arms.csv
new file mode 100644
index 0000000000..94712e66ad
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/arms.csv
@@ -0,0 +1,61 @@
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+b1200k.cps2022-2023-2024.acs100.s0,local,1200000,3,1.0,0,1031148,0,168852,1031148,352932,1384080,False,0.06497742980718613,0.06211160123348236,1161442,755.6606664571596,564.4997279533579,276474.1544207648,634911.9042601348,324232.6203824569,701288.0088424678,297379.04862366506,0.06211160123348236,28120,1500,15.86,2143.4,4,2173.3,10270918.428785808,44123094.02452402,0.13101462183085877,0.13108506908279016,,,
+b1200k.cps2023-2024.acs000.s0,local,1200000,2,0.0,0,0,1087987,112013,0,2512844,2512844,False,0.053092144429683685,0.050769366323947906,92547,627.771262112879,427.73007308594583,38732.49652443986,62206.50135809787,636276.3483267163,1762923.5167594533,441457.4362367083,0.050769366323947906,28120,1500,17.97,12600.2,6,12789.2,11759675.23438855,46790443.02373776,0.1535449563501897,0.13889392986163127,,,
+b1200k.cps2023-2024.acs100.s0,local,1200000,2,1.0,0,1087987,0,112013,1087987,234556,1322543,False,0.06953004002571106,0.06619999557733536,1180534,723.5489724300639,531.0370034191171,278972.2214834125,640683.4177700127,309737.8669671637,678604.2884228699,290788.2528424138,0.06619999557733536,28120,1500,15.18,2220.5,4,2252.5,10119732.913427562,43951757.3943063,0.1286390438138022,0.13061483508748364,,,
+b1200k.cps2024.acs000.s0,local,1200000,1,0.0,0,0,1144238,55762,0,2517604,2517604,True,0.05791730061173439,0.05482560396194458,55762,452.07332576470174,290.42941934839746,23842.90159991112,40937.95655924153,655826.3274084858,1762585.2056385933,452019.63633752876,0.05482560396194458,28117,1500,27.0,8391.4,6,,11640442.073896125,47028176.19078167,0.15159195490070432,0.1396951194421348,20.77,171.0,solve peak from grid-local.log line at 2026-09-29 06:33:24; solve wall time not recorded
+b1200k.cps2024.acs100.s0,local,1200000,1,1.0,0,1144238,0,55762,1144238,116990,1261228,False,0.07439278066158295,0.07089728862047195,1200000,683.0307460946972,493.1350930635777,281840.1947789175,646750.5618705589,291688.996097182,657982.5655365874,281840.1947789174,0.07089728862047195,28120,1500,14.55,2318.2,4,2346.2,9932355.443103073,43795380.26192823,0.1257795622778522,0.13021968662598798,,,
+b300k.cps0.acs100.s0,local,300000,0,1.0,0,300000,0,0,300000,0,300000,False,0.10084562748670578,0.09662018716335297,300000,208.76683350509938,152.5010010892675,88739.42584862383,159507.19151006124,88739.42584862383,159507.19151006124,88739.42584862383,0.09662018716335297,28111,1500,6.01,1131.6,4,1151.0,9994140.472411707,45838969.392857686,0.12793070598915623,0.13651280143787808,,,
+b300k.cps2022-2023-2024.acs000.s0,local,300000,3,0.0,0,0,131148,168852,0,627089,627089,True,0.06197219714522362,0.05896691977977753,130294,293.1242316066317,234.0589912011114,53840.515783674804,86558.24850894253,152426.09046800868,406235.7429873301,105855.91877918108,0.05896691977977753,28120,1500,9.2,1258.7,6,,11728490.304637432,47140068.62695289,0.15267437847183218,0.1395504285318954,8.02,50.4,solve peak from grid-local.log line at 2026-09-29 02:01:01; solve wall time not recorded
+b300k.cps2022-2023-2024.acs100.s0,local,300000,3,1.0,0,131148,0,168852,131148,352932,484080,False,0.05747527256608009,0.054382581263780594,261442,267.98911404640273,199.8776713284787,83424.32729710112,156488.83620453524,118261.9895609146,249684.87617608104,96319.21710963502,0.054382581263780594,28120,1500,7.62,2123.3,4,2171.7,11010424.81658316,45877035.76649492,0.14210509344328076,0.136025536219776,,,
+b300k.cps2022-2023-2024.acs100.s1,local,300000,3,1.0,1,131148,0,168852,131148,352932,484080,False,0.058328188955783844,0.05536476522684097,261442,270.8175557388307,207.4290743654512,84651.23842388003,156197.67624045914,120786.86390138796,248944.47269304335,97885.88459997045,0.05536476522684097,28120,1500,7.71,748.4,4,764.3,11251992.610887837,46439322.54574382,0.14546151210114885,0.1377226526604065,,,
+b300k.cps2022-2023-2024.acs100.s2,local,300000,3,1.0,2,131148,0,168852,131148,352932,484080,False,0.056938815861940384,0.05362273007631302,261442,266.85349799871017,198.15991625881577,83676.01853855501,156281.15768732992,119274.72231531299,249156.59368635973,96718.89670479513,0.05362273007631302,28120,1500,7.73,1147.5,4,1173.5,11318567.330588162,46255362.98305111,0.1463442417427448,0.13725096388199526,,,
+b300k.cps2023-2024.acs000.s0,local,300000,2,0.0,0,0,187987,112013,0,628217,628217,True,0.06602660566568375,0.06245909258723259,92547,288.65011252571776,236.55103167715947,39843.02064573599,62206.501358097885,164053.0018089385,425920.40023749234,112941.6994813622,0.06245909258723259,28120,1500,9.4,1418.3,6,,11696127.455528975,47240268.85376501,0.15196137143398145,0.13985880127297973,7.77,62.9,solve peak from grid-local.log line at 2026-09-29 01:36:46; solve wall time not recorded
+b300k.cps2023-2024.acs100.s0,local,300000,2,1.0,0,187987,0,112013,187987,234556,422543,False,0.06609753519296646,0.0621991790831089,280534,258.28781500238244,188.41344471630833,85272.64611894479,162363.95916515277,111345.61534092923,209930.75528175358,93972.5567800582,0.0621991790831089,28120,1500,7.24,1701.6,4,1742.3,10819954.753928734,45818397.78469345,0.13921452614647642,0.13595159977380228,,,
+b300k.cps2024.acs000.s0,local,300000,1,0.0,0,0,244238,55762,0,629368,629368,True,0.07355623692274094,0.06928373873233795,55762,272.12073195851656,210.86143249640017,25941.420332837297,40937.956559241524,180320.44405138228,437374.19822877704,121687.38177596574,0.06928373873233795,28117,1500,9.4,1381.5,6,,11658871.663017869,47383894.11827624,0.150164421240316,0.1401801960058113,8.14,49.9,solve peak from grid-local.log line at 2026-09-29 01:11:15; solve wall time not recorded
+b300k.cps2024.acs100.s0,local,300000,1,1.0,0,244238,0,55762,244238,116990,361228,False,0.08230788260698318,0.07791590690612793,300000,247.76764484201772,178.74622000515888,93725.70814697331,168415.93548620763,104383.5510354031,181311.31435542594,93725.70814697334,0.07791590690612793,28120,1500,2.36,2042.0,4,2192.1,10475134.279326146,45962042.86457038,0.1336279078690531,0.13643628189887907,,,
+b300k.cps2024.acs100.s1,local,300000,1,1.0,1,244238,0,55762,244238,116990,361228,False,0.08203397691249847,0.07790804654359818,300000,250.67828327126307,182.75149307418616,94328.32142441272,168324.0567321345,105202.23781161537,181204.8313243854,94328.32142441267,0.07790804654359818,28120,1500,6.68,655.0,4,666.3,10887165.394025274,46695876.58953652,0.13899435387194597,0.13871900849756835,,,
+b300k.cps2024.acs100.s2,local,300000,1,1.0,2,244238,0,55762,244238,116990,361228,False,0.08394557982683182,0.07939135283231735,300000,254.46022594661866,179.49914879266868,96019.8351263429,168066.56509829275,107165.45812145126,180906.45837064926,96019.8351263429,0.07939135283231735,28118,1500,6.67,556.7,4,567.3,10752683.877311276,46458386.26648162,0.1371049151150891,0.1378664031982895,,,
+b600k.cps0.acs100.s0,local,600000,0,1.0,0,600000,0,0,600000,0,600000,False,0.08315679430961609,0.07882141321897507,600000,364.9598893722962,261.3860070074219,155143.02481544143,319190.3362905997,155143.02481544143,319190.3362905997,155143.02481544143,0.07882141321897507,28117,1500,8.3,1078.2,4,1094.6,10009604.216547772,45125376.066546336,0.12705498826495873,0.13430688708821398,,,
+b600k.cps2022-2023-2024.acs000.s0,local,600000,3,0.0,0,0,431148,168852,0,1254039,1254039,True,0.054585784673690796,0.05234654247760773,130294,500.7993519200179,395.0783737978023,54304.648500122195,86558.24850894253,310443.028954894,865150.5896769182,215764.97453751817,0.05234654247760773,28120,1500,14.7,2287.2,6,,11912006.84935975,47202039.79063299,0.1554219572278197,0.13999598485576026,8.85,117.4,solve peak from grid-local.log line at 2026-09-29 04:10:34; solve wall time not recorded
+b600k.cps2022-2023-2024.acs050.s0,local,600000,3,0.5,0,215574,215574,168852,215574,803510,1019084,False,0.05014830082654953,0.047680094838142395,345868,462.3112487701133,351.84198407069437,91100.40128899252,170471.51209940494,224994.0707954942,533833.497843238,182205.06479474533,0.047680094838142395,28120,1500,13.13,1731.8,4,1756.4,11369900.929462682,45904557.96444516,0.14713191895172575,0.13634912759357135,,,
+b600k.cps2022-2023-2024.acs100.s0,local,600000,3,1.0,0,431148,0,168852,431148,352932,784080,False,0.06163938716053963,0.05774381011724472,561442,448.63763079873604,329.9962314886902,150595.0505577627,315955.71431784425,194306.5847555616,389301.29107731656,168316.10695033797,0.05774381011724472,28120,1500,10.46,1478.4,4,1499.8,10694379.845899804,45186803.03944273,0.13732852485974276,0.13419978680180616,,,
+b600k.cps2023-2024.acs000.s0,local,600000,2,0.0,0,0,487987,112013,0,1256347,1256347,True,0.0583859421312809,0.055385854095220566,92547,452.2526460360601,345.8264044377955,39229.840755440826,62206.501358097914,322095.43995491986,876506.2056315144,222908.5410778321,0.055385854095220566,28120,1500,15.5,2367.6,6,,11769767.391806126,47163669.81806761,0.15344127483400752,0.13986043582834667,13.67,71.9,solve peak from grid-local.log line at 2026-09-29 03:28:42; solve wall time not recorded
+b600k.cps2023-2024.acs100.s0,local,600000,2,1.0,0,487987,0,112013,487987,234556,722543,False,0.06882898509502411,0.0653921589255333,580534,432.5169083712294,320.2611921732755,155524.49115215163,322031.188009703,185081.49131379533,362791.050186207,166213.91257173623,0.0653921589255333,28120,1500,9.91,1561.6,4,1590.8,10497679.488581907,44969203.40838291,0.13413985166711637,0.1335468745502098,,,
+b600k.cps2024.acs000.s0,local,600000,1,0.0,0,0,544238,55762,0,1258830,1258830,True,0.0645085871219635,0.060912467539310455,55762,373.88410477679975,263.72638535233403,24855.053519465826,40937.956559241546,343332.58508595044,880334.3437453978,234488.23139657328,0.060912467539310455,28117,1500,15.6,2523.8,6,,11614913.053483784,47234704.62444273,0.15061274517588585,0.1400389874165899,8.07,98.2,solve peak from grid-local.log line at 2026-09-29 02:46:27; solve wall time not recorded
+b600k.cps2024.acs050.s0,local,600000,1,0.5,0,272119,272119,55762,272119,687926,960045,False,0.054635610431432724,0.051492102444171906,327881,406.59225511494594,318.3977985048655,55461.583853714335,114749.21018201848,221123.9520490093,489715.30208010477,183622.0879160021,0.051492102444171906,28120,1500,10.57,1785.4,4,1813.3,11054844.387464229,45850319.01790954,0.14283335729350186,0.13626589274397086,,,
+b600k.cps2024.acs100.s0,local,600000,1,1.0,0,544238,0,55762,544238,116990,661228,False,0.07838045060634613,0.07427364587783813,600000,406.1440568158041,294.3440927223149,161804.4264631515,327923.64802453225,172362.61011996653,339683.81864361133,161804.42646315147,0.07427364587783813,28120,1500,8.08,1789.4,4,1817.9,10317732.631550636,45084822.43033208,0.13127018767687001,0.13403165416587537,,,
+natural.cps2022-2023-2024.acs100.s0,local,0,3,1.0,0,0,0,168852,0,352932,352932,True,0.06911672651767731,0.06600123643875122,130294,190.7662588538194,155.42287055966597,52073.705552568616,86558.24850894253,94025.73865812777,248529.95030437905,64952.22984653852,0.06600123643875122,28120,1500,6.4,774.9,6,,11418350.796070099,46569084.67845273,0.14802659835552862,0.13765586462752047,4.71,34.5,solve peak from grid-local.log line at 2026-09-29 00:44:58; solve wall time not recorded
+natural.cps2023-2024.acs100.s0,local,0,2,1.0,0,0,0,112013,0,234556,234556,True,0.08473091572523117,0.08072442561388016,92547,141.04816128725298,114.23123748491122,38975.87635266753,62206.501358097885,68731.28222973633,164873.26090208263,46551.89785019205,0.08072442561388016,28103,1500,5.4,699.6,6,,11429484.050330639,46866444.97094107,0.1470708857488013,0.13833162882321093,5.16,18.5,solve peak from grid-local.log line at 2026-09-29 00:28:06; solve wall time not recorded
+natural.cps2024.acs100.s0,local,0,1,1.0,0,0,0,55762,0,116990,116990,True,0.11794913560152054,0.11376915127038956,55762,84.95913408732991,67.1108572841234,26267.168734799732,40937.95655924153,40526.9309224063,81941.59918861713,26267.168734799732,0.11376915127038956,27984,1500,4.3,365.6,6,,11688267.860393524,47292341.423835754,0.147717267270404,0.139334032941409,3.25,67.5,solve peak from grid-local.log line at 2026-09-29 00:13:00; solve wall time not recorded
+b1200k.cps0.acs100.s0,national,1200000,0,1.0,0,1200000,0,0,1200000,0,1200000,False,0.03872077912092209,0.03640918433666229,1200000,543.912125861377,394.5499851383779,233507.8983854827,637875.3901337923,233507.8983854827,637875.3901337923,233507.8983854827,0.03640918433666229,4825,1500,10.01,1180.4,4,1202.2,9591966.75317985,41048256.57024999,0.12247718332286353,0.12096273503666487,,,
+b1200k.cps2022-2023-2024.acs000.s0,national,1200000,3,0.0,0,0,1031148,168852,0,2508197,2508197,True,0.02433282695710659,0.022894863039255142,130294,683.0389799169452,523.1623581302406,45160.28249329324,86558.24850894252,573925.0839945227,1763224.2814842558,395171.5229703896,0.022894863039255142,4828,1500,18.3,6435.3,6,,11355374.259827435,43616010.283807576,0.1465062564067782,0.12760347727080348,15.04,252.2,solve peak from grid-national.log line at 2026-09-29 06:47:59; solve wall time not recorded
+b1200k.cps2022-2023-2024.acs100.s0,national,1200000,3,1.0,0,1031148,0,168852,1031148,352932,1384080,False,0.043582621961832047,0.04171165078878403,1161442,634.7976658792697,486.8391155777681,222919.7725749223,634911.9042601348,275179.2243128017,701288.0088424678,245283.75333559135,0.04171165078878403,4828,1500,11.28,1434.0,4,1464.8,10313150.380732972,41392224.84187276,0.13183373401652163,0.12156959046638176,,,
+b1200k.cps2023-2024.acs000.s0,national,1200000,2,0.0,0,0,1087987,112013,0,2512844,2512844,True,0.025224192067980766,0.024002769961953163,92547,555.4792248827257,415.19865534973974,31904.85055324262,62206.50135809787,580498.744451579,1762923.5167594533,400010.2992546738,0.024002769961953163,4828,1500,17.1,3054.9,6,,11382479.562117398,43869199.15454179,0.14649550773967193,0.1283199041914545,12.98,162.0,solve peak from grid-national.log line at 2026-09-29 04:45:05; solve wall time not recorded
+b1200k.cps2023-2024.acs100.s0,national,1200000,2,1.0,0,1087987,0,112013,1087987,234556,1322543,False,0.04984426498413086,0.0478442944586277,1180534,609.1911053311032,465.1256245917265,230272.02491931393,640683.4177700127,264712.2175271768,678604.2884228699,243196.91667149164,0.0478442944586277,4828,1500,10.96,1377.3,4,1406.4,10113787.189620176,41133426.019427866,0.1292435570215891,0.12094641834021345,,,
+b1200k.cps2024.acs000.s0,national,1200000,1,0.0,0,0,1144238,55762,0,2517604,2517604,True,0.028063807636499405,0.026821570470929146,55762,389.9251916514208,271.21734318759843,18675.74777381174,40937.95655924153,585223.1069359366,1762585.2056385933,401730.2326073889,0.026821570470929146,4825,1500,18.3,3011.8,6,,11369278.149812996,44416300.947438374,0.146897055231995,0.12986795044057076,14.43,139.6,solve peak from grid-national.log line at 2026-09-29 03:50:41; solve wall time not recorded
+b1200k.cps2024.acs100.s0,national,1200000,1,1.0,0,1144238,0,55762,1144238,116990,1261228,False,0.05713044852018356,0.05501589924097061,1200000,579.9641480581517,426.03935885991893,239079.8688512916,646750.5618705589,250350.08231436514,657982.5655365874,239079.86885129148,0.05501589924097061,4828,1500,10.55,1437.8,4,1461.2,9896746.1838122,41104424.76558627,0.12642986740365167,0.12098644519345499,,,
+b300k.cps0.acs100.s0,national,300000,0,1.0,0,300000,0,0,300000,0,300000,False,0.0425904281437397,0.040227945894002914,300000,143.7717104162282,101.51991639123493,60492.10872186916,159507.19151006124,60492.10872186916,159507.19151006124,60492.10872186916,0.040227945894002914,4825,1500,5.01,1087.3,4,1114.9,9641597.450175056,41796155.872448415,0.12305950584913734,0.12306176471961369,,,
+b300k.cps2022-2023-2024.acs000.s0,national,300000,3,0.0,0,0,131148,168852,0,627089,627089,True,0.02439185604453087,0.023094959557056427,130294,251.6042025696189,212.24511310208516,44983.64315566284,86558.24850894253,131797.69918104677,406235.7429873301,90821.32297287899,0.023094959557056427,4828,1500,6.8,923.5,6,,11374899.039060831,43807871.84648985,0.14672511505628388,0.128145449001727,5.9,42.8,solve peak from grid-national.log line at 2026-09-29 01:27:56; solve wall time not recorded
+b300k.cps2022-2023-2024.acs100.s0,national,300000,3,1.0,0,131148,0,168852,131148,352932,484080,False,0.0213861595839262,0.02002881094813347,261442,215.23450369169024,159.42105617247114,69064.95015142651,156488.83620453524,95019.59244673677,249684.87617608104,78646.03072777389,0.02002881094813347,4828,1500,6.2,1295.1,4,1336.5,10584715.73811039,41685376.53160183,0.1358619284794174,0.12216750220843431,,,
+b300k.cps2022-2023-2024.acs100.s1,national,300000,3,1.0,1,131148,0,168852,131148,352932,484080,False,0.02272064797580242,0.02108600363135338,261442,218.29003440104339,162.42046647139554,69333.01365841262,156197.67624045914,95821.48676280671,248944.47269304335,79062.22527509344,0.02108600363135338,4828,1500,6.2,612.2,4,624.9,11021438.86652263,42455789.65204207,0.14160616590720937,0.1244076265597218,,,
+b300k.cps2022-2023-2024.acs100.s2,national,300000,3,1.0,2,131148,0,168852,131148,352932,484080,False,0.02167442999780178,0.020376406610012054,261442,216.55363338989582,166.09240250416968,69661.27215264387,156281.15768732992,96148.07779713064,249156.59368635973,79390.61168351417,0.020376406610012054,4828,1500,5.92,585.4,4,600.5,10861576.97654386,42038924.56430028,0.14008589708860206,0.12330797192083484,,,
+b300k.cps2023-2024.acs000.s0,national,300000,2,0.0,0,0,187987,112013,0,628217,628217,True,0.025224341079592705,0.024160226806998253,92547,246.6306178320836,202.3404539529643,31712.95601798473,62206.501358097885,139424.29536662524,425920.40023749234,96140.03694340873,0.024160226806998253,4828,1500,7.1,943.7,6,,11432612.989863634,44080022.004617095,0.14709869508613466,0.1288981482812852,5.65,56.0,solve peak from grid-national.log line at 2026-09-29 01:10:13; solve wall time not recorded
+b300k.cps2023-2024.acs100.s0,national,300000,2,1.0,0,187987,0,112013,187987,234556,422543,False,0.02435688115656376,0.022902067750692368,280534,196.92729743904806,140.65406142695804,65907.76439890644,162363.95916515277,84145.60518735563,209930.75528175358,71894.01417896678,0.022902067750692368,4828,1500,5.83,614.0,4,635.7,10581508.900618356,41651178.46120613,0.13537529690507638,0.12209002420026782,,,
+b300k.cps2024.acs000.s0,national,300000,1,0.0,0,0,244238,55762,0,629368,629368,True,0.0282257292419672,0.02702220343053341,55762,219.88090668456755,169.45317852319738,18566.939722103863,40937.956559241524,144526.3683928753,437374.19822877704,99170.14517170061,0.02702220343053341,4825,1500,7.1,906.6,6,,11388008.222690105,44598437.878023386,0.14715074007796508,0.13038711883029216,6.19,43.2,solve peak from grid-national.log line at 2026-09-29 00:51:34; solve wall time not recorded
+b300k.cps2024.acs100.s0,national,300000,1,1.0,0,244238,0,55762,244238,116990,361228,False,0.039431530982255936,0.0373963788151741,300000,171.18927088274563,120.72213545828625,63296.633070639335,168415.93548620763,72515.77093181499,181311.31435542594,63296.63307063933,0.0373963788151741,4828,1500,1.41,802.3,4,1030.1,10294694.07570061,41673720.0904639,0.13138742782002952,0.12218612824893674,,,
+b300k.cps2024.acs100.s1,national,300000,1,1.0,1,244238,0,55762,244238,116990,361228,False,0.03853805735707283,0.03637304902076721,300000,170.33106956800424,126.28911015883793,63661.665857208754,168324.0567321345,72997.50108399754,181204.8313243854,63661.665857208776,0.03637304902076721,4828,1500,5.48,446.1,4,457.7,10461103.222164685,42055014.77905263,0.13405258593041433,0.12348572402303655,,,
+b300k.cps2024.acs100.s2,national,300000,1,1.0,2,244238,0,55762,244238,116990,361228,False,0.03928820788860321,0.036883339285850525,300000,173.70731044745685,129.131949389442,65310.50293694685,168066.56509829275,75001.11849491969,180906.45837064926,65310.50293694685,0.036883339285850525,4828,1500,5.36,412.3,4,422.8,10375724.657162812,42051714.29241819,0.13290495142731462,0.12331530934208841,,,
+b600k.cps0.acs100.s0,national,600000,0,1.0,0,600000,0,0,600000,0,600000,False,0.04054223746061325,0.03836475685238838,600000,275.2207044872443,200.5672447286007,119288.30412767422,319190.3362905997,119288.30412767422,319190.3362905997,119288.30412767422,0.03836475685238838,4825,1500,6.55,856.6,4,870.6,9725483.486029487,41574001.55281273,0.12417225908539854,0.12247343785129945,,,
+b600k.cps2022-2023-2024.acs000.s0,national,600000,3,0.0,0,0,431148,168852,0,1254039,1254039,True,0.024382643401622772,0.022988269105553627,130294,454.7753961817672,365.9812050151028,45050.38874639817,86558.24850894253,280281.331022451,865150.5896769182,193068.26427151615,0.022988269105553627,4828,1500,10.8,1486.9,6,,11371756.135731876,43750251.109125644,0.14667396210625794,0.12798974699289783,8.44,101.0,solve peak from grid-national.log line at 2026-09-29 02:56:54; solve wall time not recorded
+b600k.cps2022-2023-2024.acs050.s0,national,600000,3,0.5,0,215574,215574,168852,215574,803510,1019084,False,0.02069520577788353,0.019354643300175667,345868,397.18834157306765,317.194185879134,77830.18085078364,170471.51209940494,193136.0566511478,533833.497843238,157550.8032433334,0.019354643300175667,4828,1500,9.32,1204.5,4,1228.1,10818248.952591147,42031960.58418137,0.1390709414567029,0.1231790923431293,,,
+b600k.cps2022-2023-2024.acs100.s0,national,600000,3,1.0,0,431148,0,168852,431148,352932,784080,False,0.025421563535928726,0.023899151012301445,561442,343.98726405602747,264.11520392021356,113040.62619633827,315955.71431784425,151507.81294054587,389301.29107731656,128327.01916547072,0.023899151012301445,4828,1500,7.93,996.9,4,1024.0,10726110.603100806,42260022.086715564,0.1373884972456171,0.12397400507814195,,,
+b600k.cps2023-2024.acs000.s0,national,600000,2,0.0,0,0,487987,112013,0,1256347,1256347,True,0.02516903541982174,0.024079127237200737,92547,407.432608967661,318.0259011306876,31780.55376763681,62206.501358097914,287428.0976136814,876506.2056315144,198151.41973040573,0.024079127237200737,4828,1500,10.6,1609.5,6,,11412792.855736494,43997041.19816533,0.14684513849505035,0.12867326104459959,9.43,59.0,solve peak from grid-national.log line at 2026-09-29 02:28:51; solve wall time not recorded
+b600k.cps2023-2024.acs100.s0,national,600000,2,1.0,0,487987,0,112013,487987,234556,722543,False,0.037997834384441376,0.0358051136136055,580534,324.10843024060046,251.381966421416,114433.20966057874,322031.188009703,142440.39644767783,362791.050186207,124304.35082513398,0.0358051136136055,4828,1500,7.49,1327.1,4,1354.9,10440162.816235783,41550040.864334404,0.13339031800201104,0.12193002120303904,,,
+b600k.cps2024.acs000.s0,national,600000,1,0.0,0,0,544238,55762,0,1258830,1258830,True,0.0281167421489954,0.026919201016426086,55762,310.47344920072624,225.91142117354306,18612.508981182837,40937.956559241546,291487.9745290404,880334.3437453978,200108.9125813431,0.026919201016426086,4825,1500,11.0,1621.4,6,,11372131.305653334,44538257.58970815,0.1469275383118905,0.13021938841996092,7.47,85.3,solve peak from grid-national.log line at 2026-09-29 01:59:14; solve wall time not recorded
+b600k.cps2024.acs050.s0,national,600000,1,0.5,0,272119,272119,55762,272119,687926,960045,False,0.022764919325709343,0.021330222487449646,327881,343.8278869112812,268.51730477671015,45347.92882155529,114749.21018201848,184474.2557012649,489715.30208010477,155148.95997585973,0.021330222487449646,4828,1500,9.15,1195.0,4,1224.6,10719044.044821162,42154177.302658916,0.1377625434927752,0.12352896615205017,,,
+b600k.cps2024.acs100.s0,national,600000,1,1.0,0,544238,0,55762,544238,116990,661228,False,0.05047328770160675,0.04838469624519348,600000,308.4210871605093,227.44736055047443,121891.03572378987,327923.64802453225,132440.09677401997,339683.81864361133,121891.03572378986,0.04838469624519348,4828,1500,7.0,1435.0,4,1457.6,10107732.423939351,41379590.19807629,0.12916048039766112,0.12163801582352916,,,
+natural.cps2022-2023-2024.acs100.s0,national,0,3,1.0,0,0,0,168852,0,352932,352932,True,0.024450428783893585,0.02311604470014572,130294,162.07307651019124,135.8825931190194,44942.90129089897,86558.24850894253,80524.24306066195,248529.95030437905,55412.73795665963,0.02311604470014572,4828,1500,5.1,494.9,6,,11386375.854607224,43843039.51912677,0.14687699979485672,0.1282397736539901,3.98,30.9,solve peak from grid-national.log line at 2026-09-29 00:33:39; solve wall time not recorded
+natural.cps2023-2024.acs100.s0,national,0,2,1.0,0,0,0,112013,0,234556,234556,True,0.025277292355895042,0.02424626424908638,92547,110.2017029609713,90.97968053735669,31671.325578832886,62206.501358097885,53851.125087605615,164873.26090208263,37114.08063691554,0.02424626424908638,4828,1500,4.6,461.8,6,,11443507.481101513,44138225.16565478,0.1471996237820945,0.12904456215569654,4.56,16.7,solve peak from grid-national.log line at 2026-09-29 00:21:38; solve wall time not recorded
+natural.cps2024.acs100.s0,national,0,1,1.0,0,0,0,55762,0,116990,116990,True,0.028257571160793304,0.027136798948049545,55762,56.46558618528475,44.74623298966674,18525.796531333322,40937.95655924153,27014.978824456142,81941.59918861713,18525.796531333322,0.027136798948049545,4825,1500,4.0,268.8,6,,11400368.045695305,44687541.00171065,0.147337092895218,0.13063829061507248,3.36,64.2,solve peak from grid-national.log line at 2026-09-29 00:11:23; solve wall time not recorded
diff --git a/experiments/us-support-mix-bakeoff-20260930/asec_quality.json b/experiments/us-support-mix-bakeoff-20260930/asec_quality.json
new file mode 100644
index 0000000000..ccd2aa52be
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/asec_quality.json
@@ -0,0 +1,159 @@
+{
+ "processed_h5_by_income_year": [
+  {
+   "income_year": 2018,
+   "survey_year": 2019,
+   "households": 68345,
+   "distinct_h_idnum": 68345,
+   "share_households_in_prior_file": null,
+   "household_weight_total_millions": 128.67448709,
+   "household_weight_cv": 0.6025358837852475,
+   "household_weight_kish_share": 0.7336490761489285,
+   "homeownership_weighted": 0.6447575960568765,
+   "unemployment_compensation_recipients": 1723,
+   "earners_whole_record_imputed_weighted": 0.242330658014494,
+   "earners_item_allocated_weighted": 0.21330270102200033
+  },
+  {
+   "income_year": 2019,
+   "survey_year": 2020,
+   "households": 60460,
+   "distinct_h_idnum": 60460,
+   "share_households_in_prior_file": 0.3777704267284155,
+   "household_weight_total_millions": 128.58819972,
+   "household_weight_cv": 0.6099413348000816,
+   "household_weight_kish_share": 0.7288478698774934,
+   "homeownership_weighted": 0.6639244541559711,
+   "unemployment_compensation_recipients": 1563,
+   "earners_whole_record_imputed_weighted": 0.2381596300674719,
+   "earners_item_allocated_weighted": 0.2125500232438193
+  },
+  {
+   "income_year": 2020,
+   "survey_year": 2021,
+   "households": 62850,
+   "distinct_h_idnum": 62850,
+   "share_households_in_prior_file": 0.32513922036595067,
+   "household_weight_total_millions": 130.0231343,
+   "household_weight_cv": 0.5979707232435548,
+   "household_weight_kish_share": 0.7366108171432274,
+   "homeownership_weighted": 0.6575969944911564,
+   "unemployment_compensation_recipients": 10915,
+   "earners_whole_record_imputed_weighted": 0.2243862480395746,
+   "earners_item_allocated_weighted": 0.22633534476314407
+  },
+  {
+   "income_year": 2021,
+   "survey_year": 2022,
+   "households": 59148,
+   "distinct_h_idnum": 59148,
+   "share_households_in_prior_file": 0.34988503415161964,
+   "household_weight_total_millions": 131.31471499,
+   "household_weight_cv": 0.607226259555364,
+   "household_weight_kish_share": 0.7306076294779332,
+   "homeownership_weighted": 0.6544510986947999,
+   "unemployment_compensation_recipients": 4065,
+   "earners_whole_record_imputed_weighted": 0.22397363113999202,
+   "earners_item_allocated_weighted": 0.21762532030886778
+  },
+  {
+   "income_year": 2022,
+   "survey_year": 2023,
+   "households": 56839,
+   "distinct_h_idnum": 56839,
+   "share_households_in_prior_file": 0.34770140220623164,
+   "household_weight_total_millions": 131.66090909,
+   "household_weight_cv": 0.599646329065834,
+   "household_weight_kish_share": 0.7355235793912631,
+   "homeownership_weighted": 0.6592966302599605,
+   "unemployment_compensation_recipients": 1225,
+   "earners_whole_record_imputed_weighted": 0.2186550960443649,
+   "earners_item_allocated_weighted": 0.2167998316454706
+  },
+  {
+   "income_year": 2023,
+   "survey_year": 2024,
+   "households": 56251,
+   "distinct_h_idnum": 56251,
+   "share_households_in_prior_file": 0.339407299425788,
+   "household_weight_total_millions": 132.39192545,
+   "household_weight_cv": 0.5979118733636303,
+   "household_weight_kish_share": 0.7366490056630862,
+   "homeownership_weighted": 0.6563332517043623,
+   "unemployment_compensation_recipients": 1360,
+   "earners_whole_record_imputed_weighted": 0.2070184806918598,
+   "earners_item_allocated_weighted": 0.20988846004956252
+  },
+  {
+   "income_year": 2024,
+   "survey_year": 2025,
+   "households": 55762,
+   "distinct_h_idnum": 55762,
+   "share_households_in_prior_file": 0.34909077866647537,
+   "household_weight_total_millions": 134.96389280000002,
+   "household_weight_cv": 0.6017557642286367,
+   "household_weight_kish_share": 0.7341550977232084,
+   "homeownership_weighted": 0.6511854704001246,
+   "unemployment_compensation_recipients": 1400,
+   "earners_whole_record_imputed_weighted": 0.1932919011043821,
+   "earners_item_allocated_weighted": 0.215243237218826
+  }
+ ],
+ "pools": {
+  "2022-2023-2024": {
+   "households": 168852,
+   "distinct_h_idnum": 130294
+  },
+  "2020-2021-2022-2023-2024": {
+   "households": 290850,
+   "distinct_h_idnum": 211834
+  }
+ },
+ "public_use_archives_by_survey_year": [
+  {
+   "survey_year": 2022,
+   "income_year": 2021,
+   "persons": 152732,
+   "whole_supplement_imputed_weighted": 0.2046639035877903,
+   "bachelors_or_higher_25plus_weighted": 0.376611355994884
+  },
+  {
+   "survey_year": 2023,
+   "income_year": 2022,
+   "persons": 146133,
+   "whole_supplement_imputed_weighted": 0.1994541476607985,
+   "bachelors_or_higher_25plus_weighted": 0.3834978999842217
+  },
+  {
+   "survey_year": 2024,
+   "income_year": 2023,
+   "persons": 144265,
+   "whole_supplement_imputed_weighted": 0.19164278967548057,
+   "bachelors_or_higher_25plus_weighted": 0.38686143531005024
+  },
+  {
+   "survey_year": 2025,
+   "income_year": 2024,
+   "persons": 142125,
+   "whole_supplement_imputed_weighted": 0.1782697407007112,
+   "bachelors_or_higher_25plus_weighted": 0.39343967585037093
+  },
+  {
+   "survey_year": 2026,
+   "income_year": 2025,
+   "persons": 134729,
+   "whole_supplement_imputed_weighted": 0.16678632658230516,
+   "bachelors_or_higher_25plus_weighted": 0.4024180140213014
+  }
+ ],
+ "combined_unweighted_response_rate_by_survey_year": {
+  "source": "CPS ASEC technical documentation, Source and Accuracy statements (combined basic-CPS x ASEC household nonresponse), transcribed 2026-09-28",
+  "2019": 0.676,
+  "2020": 0.611,
+  "2021": 0.65,
+  "2022": 0.614,
+  "2023": 0.596,
+  "2024": 0.593,
+  "2025": 0.601
+ }
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/asec_quality.py b/experiments/us-support-mix-bakeoff-20260930/asec_quality.py
new file mode 100644
index 0000000000..bba6dfc23a
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/asec_quality.py
@@ -0,0 +1,111 @@
+"""CPS ASEC response and processing quality by income year (receipt script).
+
+Writes ``asec_quality.json`` next to this file. Two local sources:
+
+- ``census_cps_{2018..2024}.h5`` (policyengine-us-data processed ASEC, keyed by
+  income year): households, survey year, distinct and repeated ``H_IDNUM``,
+  household-weight dispersion, homeownership, unemployment-compensation
+  recipients, and the earnings imputation flag ``I_ERNVAL`` (9 = whole record
+  imputed, 1-8 = item allocated) among wage earners.
+- the Census public-use ASEC CSV archives ``asecpub{22..26}csv.zip`` (income
+  years 2021-2025): the whole-supplement imputation flag ``FL_665`` (persons,
+  ``MARSUPWT``-weighted) and the weighted bachelor's-or-higher share among
+  adults 25 and over (``A_HGA`` >= 43).
+
+Run: ``uv run python experiments/us-support-mix-bakeoff-20260930/asec_quality.py``
+"""
+
+from __future__ import annotations
+
+import json
+import zipfile
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+STORAGE = Path("/Users/maxghenis/PolicyEngine/policyengine-us-data/policyengine_us_data/storage")
+ZIPS = Path("/Users/maxghenis/PolicyEngine/_buildm-runtime/inputs/asec_education")
+OUT = Path(__file__).resolve().parent / "asec_quality.json"
+
+
+def h5_metrics(income_year: int, previous_ids: set | None) -> tuple[dict, set]:
+    path = STORAGE / f"census_cps_{income_year}.h5"
+    with pd.HDFStore(path, "r") as store:
+        hh = store["household"][["H_IDNUM", "H_YEAR", "H_TENURE", "HSUP_WGT"]]
+        person = store["person"][["A_FNLWGT", "I_ERNVAL", "UC_VAL", "WSAL_VAL"]]
+    w = hh["HSUP_WGT"].to_numpy(np.float64) / 100
+    ids = set(hh["H_IDNUM"].astype(str))
+    earners = person[person["WSAL_VAL"] > 0]
+    ew = earners["A_FNLWGT"].to_numpy(np.float64)
+    flag = earners["I_ERNVAL"].to_numpy()
+    metrics = {
+        "income_year": income_year,
+        "survey_year": int(hh["H_YEAR"].iloc[0]),
+        "households": int(len(hh)),
+        "distinct_h_idnum": len(ids),
+        "share_households_in_prior_file": (
+            None if previous_ids is None else len(ids & previous_ids) / len(ids)
+        ),
+        "household_weight_total_millions": float(w.sum() / 1e6),
+        "household_weight_cv": float(w.std() / w.mean()),
+        "household_weight_kish_share": float(w.sum() ** 2 / np.square(w).sum() / len(w)),
+        "homeownership_weighted": float(w[hh["H_TENURE"].to_numpy() == 1].sum() / w.sum()),
+        "unemployment_compensation_recipients": int((person["UC_VAL"] > 0).sum()),
+        "earners_whole_record_imputed_weighted": float(ew[flag == 9].sum() / ew.sum()),
+        "earners_item_allocated_weighted": float(ew[(flag >= 1) & (flag <= 8)].sum() / ew.sum()),
+    }
+    return metrics, ids
+
+
+def zip_metrics(survey_year: int) -> dict:
+    path = ZIPS / f"asecpub{survey_year % 100:02d}csv.zip"
+    with zipfile.ZipFile(path) as archive:
+        name = next(n for n in archive.namelist() if n.lower().startswith("pppub") and n.lower().endswith(".csv"))
+        with archive.open(name) as handle:
+            person = pd.read_csv(handle, usecols=["FL_665", "MARSUPWT", "A_AGE", "A_HGA"])
+    w = person["MARSUPWT"].to_numpy(np.float64)
+    adults = person["A_AGE"].to_numpy() >= 25
+    return {
+        "survey_year": survey_year,
+        "income_year": survey_year - 1,
+        "persons": int(len(person)),
+        "whole_supplement_imputed_weighted": float(w[person["FL_665"].to_numpy() != 1].sum() / w.sum()),
+        "bachelors_or_higher_25plus_weighted": float(
+            w[adults & (person["A_HGA"].to_numpy() >= 43)].sum() / w[adults].sum()
+        ),
+    }
+
+
+def main() -> None:
+    files, previous = [], None
+    for year in range(2018, 2025):
+        metrics, previous = h5_metrics(year, previous)
+        files.append(metrics)
+    pools = {}
+    for years in ((2022, 2023, 2024), (2020, 2021, 2022, 2023, 2024)):
+        households, distinct = 0, set()
+        for year in years:
+            with pd.HDFStore(STORAGE / f"census_cps_{year}.h5", "r") as store:
+                ids = store["household"]["H_IDNUM"].astype(str)
+            households += len(ids)
+            distinct |= set(ids)
+        pools["-".join(map(str, years))] = {"households": households, "distinct_h_idnum": len(distinct)}
+    archives = [zip_metrics(year) for year in range(2022, 2027) if (ZIPS / f"asecpub{year % 100:02d}csv.zip").exists()]
+    payload = {
+        "processed_h5_by_income_year": files,
+        "pools": pools,
+        "public_use_archives_by_survey_year": archives,
+        "combined_unweighted_response_rate_by_survey_year": {
+            "source": "CPS ASEC technical documentation, Source and Accuracy statements "
+            "(combined basic-CPS x ASEC household nonresponse), transcribed 2026-09-28",
+            "2019": 0.676, "2020": 0.611, "2021": 0.650, "2022": 0.614,
+            "2023": 0.596, "2024": 0.593, "2025": 0.601,
+        },
+    }
+    OUT.write_text(json.dumps(payload, indent=1))
+    print(json.dumps(payload, indent=1))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/us-support-mix-bakeoff-20260930/compile.json b/experiments/us-support-mix-bakeoff-20260930/compile.json
new file mode 100644
index 0000000000..7a5e756b1e
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/compile.json
@@ -0,0 +1,57 @@
+{
+ "dimension_counts": {
+  "acs_native/demographics_pep/national": 18,
+  "acs_native/demographics_pep/state": 918,
+  "cps_native/income_components/cd": 8900,
+  "cps_native/income_components/national": 102,
+  "cps_native/income_components/state": 3054,
+  "cps_native/program_receipt/national": 17,
+  "cps_native/program_receipt/state": 467,
+  "cps_native/tax_items/cd": 15440,
+  "cps_native/tax_items/national": 362,
+  "cps_native/tax_items/state": 3563,
+  "other/wealth/national": 1
+ },
+ "excluded": {
+  "excluded:reform_simulation": 11
+ },
+ "feed": "/Users/maxghenis/PolicyEngine/_buildh-runtime/inputs/consumer_facts_us_c5e5bf8.jsonl",
+ "feed_sha256": "b85437390021777e746f507c5890305496baf5fc7f2c78ba08ddb090f4839801",
+ "medicaid_substitutions": 1,
+ "n_concepts": 419,
+ "n_report_concepts": 2,
+ "n_specs": 32842,
+ "national_state_surface": {
+  "calibrated_targets": 5694,
+  "compiled_targets": 32842,
+  "dropped_congressional_district_targets": 27148,
+  "mode": "national_state"
+ },
+ "notes": {},
+ "peak_rss_gb": 3.76,
+ "registry_version": "a0698bc97ff9",
+ "roles": {
+  "holdout": 3470,
+  "sealed": 1661,
+  "train": 27711
+ },
+ "roles_by_level": {
+  "cd": {
+   "holdout": 2647,
+   "sealed": 1242,
+   "train": 20451
+  },
+  "national": {
+   "holdout": 36,
+   "sealed": 24,
+   "train": 440
+  },
+  "state": {
+   "holdout": 787,
+   "sealed": 395,
+   "train": 6820
+  }
+ },
+ "split_note": "Mirrors the unmerged us-target-roles-holdout split (salts, \\x1f hash, group keys) without its pins; see support_mix.py.",
+ "wall_s": 22.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/diffcheck.json b/experiments/us-support-mix-bakeoff-20260930/diffcheck.json
new file mode 100644
index 0000000000..dc5463c363
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/diffcheck.json
@@ -0,0 +1,43 @@
+{
+ "peak_rss_gb": 7.76,
+ "results": {
+  "acs": {
+   "households": 4000,
+   "max_scaled_abs_diff": 0.0,
+   "per_family_level": {
+    "census_population/state": 0.0,
+    "cms_aca/state": 0.0,
+    "cms_medicaid/state": 0.0,
+    "hhs_acf_tanf/state": 0.0,
+    "irs_soi/cd": 0.0,
+    "irs_soi/state": 0.0,
+    "ssa/state": 0.0,
+    "state_income_tax/state": 0.0,
+    "usda_snap/state": 0.0
+   },
+   "specs_checked": 349,
+   "specs_skipped": 0
+  },
+  "cps": {
+   "households": 4000,
+   "logged_at": "2026-09-28 23:41:21",
+   "max_scaled_abs_diff": 0.0,
+   "per_family_level": {
+    "census_population/state": 0.0,
+    "cms_aca/state": 0.0,
+    "cms_medicaid/state": 0.0,
+    "hhs_acf_tanf/state": 0.0,
+    "irs_soi/cd": 0.0,
+    "irs_soi/state": 0.0,
+    "ssa/state": 0.0,
+    "state_income_tax/state": 0.0,
+    "usda_snap/state": 0.0
+   },
+   "source": "diffcheck.log (run 2026-09-28, before the CPS shard pickles were removed to free disk)",
+   "specs_checked": 349,
+   "specs_skipped": 0
+  }
+ },
+ "sample_size": 349,
+ "wall_s": 2157.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/dimensions_by_arm.csv b/experiments/us-support-mix-bakeoff-20260930/dimensions_by_arm.csv
new file mode 100644
index 0000000000..092b25ace9
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/dimensions_by_arm.csv
@@ -0,0 +1,61 @@
+product,arm,budget,cps_years,acs_fill_share,seed,rows,ess_distinct,cps_native_weighted.state,cps_native_weighted.cd,income_components.state,income_components.cd,tax_items.state,tax_items.cd,program_receipt.state,demographics_pep.state,acs_native.state,acs_native.cd,acs_native.county
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diff --git a/experiments/us-support-mix-bakeoff-20260930/headline.md b/experiments/us-support-mix-bakeoff-20260930/headline.md
new file mode 100644
index 0000000000..ae9def5bda
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/headline.md
@@ -0,0 +1,69 @@
+### local product
+
+| Arm | Rows | Distinct hh | ESS (distinct) | CPS-native nat | state | CD | PEP demog | ACS-native state | CD | county | Solve min | Peak GB |
+|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| natural.cps2024.acs100.s0 | 116,990 | 55,762 | 26,267 | 0.356 | 0.199 | 0.341 | 0.130 | 0.131 | 0.278 | 0.342 | 6.1 | 4.3 |
+| natural.cps2023-2024.acs100.s0 | 234,556 | 92,547 | 38,976 | 0.299 | 0.176 | 0.279 | 0.143 | 0.133 | 0.266 | 0.316 | 11.7 | 5.4 |
+| natural.cps2022-2023-2024.acs100.s0 | 352,932 | 130,294 | 52,074 | 0.265 | 0.161 | 0.258 | 0.152 | 0.134 | 0.263 | 0.299 | 12.9 | 6.4 |
+| b300k.cps0.acs100.s0 | 300,000 | 300,000 | 88,739 | 0.375 | 0.212 | 0.264 | 0.162 | 0.082 | 0.151 | 0.224 | 18.9 | 6.0 |
+| b300k.cps2024.acs000.s0 | 629,368 | 55,762 | 25,941 | 0.301 | 0.185 | 0.242 | 0.149 | 0.131 | 0.265 | 0.279 | 23.0 | 9.4 |
+| b300k.cps2024.acs100.s0 | 361,228 | 300,000 | 93,726 | 0.380 | 0.186 | 0.242 | 0.153 | 0.080 | 0.155 | 0.223 | 34.0 | 2.4 |
+| b300k.cps2024.acs100.s1 | 361,228 | 300,000 | 94,328 | 0.337 | 0.194 | 0.248 | 0.167 | 0.081 | 0.152 | 0.220 | 10.9 | 6.7 |
+| b300k.cps2024.acs100.s2 | 361,228 | 300,000 | 96,020 | 0.368 | 0.183 | 0.252 | 0.151 | 0.077 | 0.151 | 0.219 | 9.3 | 6.7 |
+| b300k.cps2023-2024.acs000.s0 | 628,217 | 92,547 | 39,843 | 0.276 | 0.169 | 0.234 | 0.162 | 0.133 | 0.261 | 0.278 | 23.6 | 9.4 |
+| b300k.cps2023-2024.acs100.s0 | 422,543 | 280,534 | 85,273 | 0.338 | 0.180 | 0.243 | 0.155 | 0.096 | 0.184 | 0.235 | 28.4 | 7.2 |
+| b300k.cps2022-2023-2024.acs000.s0 | 627,089 | 130,294 | 53,841 | 0.262 | 0.159 | 0.237 | 0.160 | 0.134 | 0.260 | 0.281 | 21.0 | 9.2 |
+| b300k.cps2022-2023-2024.acs100.s0 | 484,080 | 261,442 | 83,424 | 0.304 | 0.172 | 0.236 | 0.162 | 0.110 | 0.205 | 0.251 | 35.4 | 7.6 |
+| b300k.cps2022-2023-2024.acs100.s1 | 484,080 | 261,442 | 84,651 | 0.337 | 0.167 | 0.240 | 0.165 | 0.110 | 0.205 | 0.250 | 12.5 | 7.7 |
+| b300k.cps2022-2023-2024.acs100.s2 | 484,080 | 261,442 | 83,676 | 0.310 | 0.165 | 0.239 | 0.165 | 0.110 | 0.202 | 0.251 | 19.1 | 7.7 |
+| b600k.cps0.acs100.s0 | 600,000 | 600,000 | 155,143 | 0.371 | 0.192 | 0.238 | 0.180 | 0.081 | 0.145 | 0.198 | 18.0 | 8.3 |
+| b600k.cps2024.acs000.s0 | 1,258,830 | 55,762 | 24,855 | 0.293 | 0.179 | 0.222 | 0.161 | 0.131 | 0.260 | 0.265 | 42.1 | 15.6 |
+| b600k.cps2024.acs050.s0 | 960,045 | 327,881 | 55,462 | 0.352 | 0.171 | 0.218 | 0.167 | 0.102 | 0.198 | 0.226 | 29.8 | 10.6 |
+| b600k.cps2024.acs100.s0 | 661,228 | 600,000 | 161,804 | 0.367 | 0.180 | 0.225 | 0.176 | 0.072 | 0.137 | 0.192 | 29.8 | 8.1 |
+| b600k.cps2023-2024.acs000.s0 | 1,256,347 | 92,547 | 39,230 | 0.278 | 0.164 | 0.214 | 0.171 | 0.133 | 0.257 | 0.264 | 39.5 | 15.5 |
+| b600k.cps2023-2024.acs100.s0 | 722,543 | 580,534 | 155,524 | 0.366 | 0.177 | 0.220 | 0.171 | 0.081 | 0.150 | 0.198 | 26.0 | 9.9 |
+| b600k.cps2022-2023-2024.acs000.s0 | 1,254,039 | 130,294 | 54,305 | 0.264 | 0.156 | 0.217 | 0.173 | 0.134 | 0.256 | 0.263 | 38.1 | 14.7 |
+| b600k.cps2022-2023-2024.acs050.s0 | 1,019,084 | 345,868 | 91,100 | 0.295 | 0.163 | 0.217 | 0.174 | 0.113 | 0.209 | 0.232 | 28.9 | 13.1 |
+| b600k.cps2022-2023-2024.acs100.s0 | 784,080 | 561,442 | 150,595 | 0.360 | 0.173 | 0.220 | 0.166 | 0.092 | 0.166 | 0.207 | 24.6 | 10.5 |
+| b1200k.cps0.acs100.s0 | 1,200,000 | 1,200,000 | 272,607 | 0.364 | 0.176 | 0.210 | 0.191 | 0.084 | 0.146 | 0.180 | 31.1 | 13.8 |
+| b1200k.cps2024.acs000.s0 | 2,517,604 | 55,762 | 23,843 | 0.286 | 0.179 | 0.206 | 0.172 | 0.132 | 0.258 | 0.257 | 139.9 | 27.0 |
+| b1200k.cps2024.acs100.s0 | 1,261,228 | 1,200,000 | 281,840 | 0.357 | 0.170 | 0.209 | 0.187 | 0.072 | 0.136 | 0.174 | 38.6 | 14.6 |
+| b1200k.cps2023-2024.acs000.s0 | 2,512,844 | 92,547 | 38,732 | 0.275 | 0.162 | 0.202 | 0.179 | 0.133 | 0.254 | 0.254 | 210.0 | 18.0 |
+| b1200k.cps2023-2024.acs100.s0 | 1,322,543 | 1,180,534 | 278,972 | 0.353 | 0.169 | 0.209 | 0.185 | 0.073 | 0.136 | 0.173 | 37.0 | 15.2 |
+| b1200k.cps2022-2023-2024.acs000.s0 | 2,508,197 | 130,294 | 54,292 | 0.263 | 0.156 | 0.206 | 0.181 | 0.135 | 0.253 | 0.253 | 54.8 | 27.9 |
+| b1200k.cps2022-2023-2024.acs100.s0 | 1,384,080 | 1,161,442 | 276,474 | 0.346 | 0.166 | 0.208 | 0.182 | 0.078 | 0.141 | 0.174 | 35.7 | 15.9 |
+
+### national product
+
+| Arm | Rows | Distinct hh | ESS (distinct) | CPS-native nat | state | CD | PEP demog | ACS-native state | CD | county | Solve min | Peak GB |
+|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| natural.cps2024.acs100.s0 | 116,990 | 55,762 | 18,526 | 0.244 | 0.189 | 0.487 | 0.166 | 0.129 | 0.271 | 0.381 | 4.5 | 4.0 |
+| natural.cps2023-2024.acs100.s0 | 234,556 | 92,547 | 31,671 | 0.251 | 0.173 | 0.428 | 0.168 | 0.129 | 0.242 | 0.331 | 7.7 | 4.6 |
+| natural.cps2022-2023-2024.acs100.s0 | 352,932 | 130,294 | 44,943 | 0.236 | 0.175 | 0.406 | 0.167 | 0.133 | 0.236 | 0.307 | 8.2 | 5.1 |
+| b300k.cps0.acs100.s0 | 300,000 | 300,000 | 60,492 | 0.251 | 0.210 | 0.356 | 0.174 | 0.096 | 0.172 | 0.269 | 18.1 | 5.0 |
+| b300k.cps2024.acs000.s0 | 629,368 | 55,762 | 18,567 | 0.243 | 0.189 | 0.372 | 0.166 | 0.129 | 0.227 | 0.283 | 15.1 | 7.1 |
+| b300k.cps2024.acs100.s0 | 361,228 | 300,000 | 63,297 | 0.274 | 0.193 | 0.363 | 0.158 | 0.084 | 0.168 | 0.252 | 13.4 | 1.4 |
+| b300k.cps2024.acs100.s1 | 361,228 | 300,000 | 63,662 | 0.253 | 0.193 | 0.370 | 0.165 | 0.087 | 0.167 | 0.258 | 7.4 | 5.5 |
+| b300k.cps2024.acs100.s2 | 361,228 | 300,000 | 65,311 | 0.254 | 0.185 | 0.371 | 0.150 | 0.085 | 0.164 | 0.254 | 6.9 | 5.4 |
+| b300k.cps2023-2024.acs000.s0 | 628,217 | 92,547 | 31,713 | 0.251 | 0.173 | 0.379 | 0.168 | 0.129 | 0.223 | 0.281 | 15.7 | 7.1 |
+| b300k.cps2023-2024.acs100.s0 | 422,543 | 280,534 | 65,908 | 0.215 | 0.180 | 0.354 | 0.169 | 0.097 | 0.175 | 0.251 | 10.2 | 5.8 |
+| b300k.cps2022-2023-2024.acs000.s0 | 627,089 | 130,294 | 44,984 | 0.236 | 0.175 | 0.383 | 0.167 | 0.133 | 0.226 | 0.283 | 15.4 | 6.8 |
+| b300k.cps2022-2023-2024.acs100.s0 | 484,080 | 261,442 | 69,065 | 0.192 | 0.163 | 0.352 | 0.168 | 0.111 | 0.188 | 0.257 | 21.6 | 6.2 |
+| b300k.cps2022-2023-2024.acs100.s1 | 484,080 | 261,442 | 69,333 | 0.278 | 0.172 | 0.356 | 0.157 | 0.110 | 0.190 | 0.258 | 10.2 | 6.2 |
+| b300k.cps2022-2023-2024.acs100.s2 | 484,080 | 261,442 | 69,661 | 0.171 | 0.170 | 0.355 | 0.160 | 0.109 | 0.187 | 0.261 | 9.8 | 5.9 |
+| b600k.cps0.acs100.s0 | 600,000 | 600,000 | 119,288 | 0.285 | 0.200 | 0.330 | 0.181 | 0.092 | 0.156 | 0.232 | 14.3 | 6.5 |
+| b600k.cps2024.acs000.s0 | 1,258,830 | 55,762 | 18,613 | 0.243 | 0.189 | 0.355 | 0.166 | 0.129 | 0.220 | 0.262 | 27.0 | 11.0 |
+| b600k.cps2024.acs050.s0 | 960,045 | 327,881 | 45,348 | 0.250 | 0.170 | 0.328 | 0.160 | 0.104 | 0.174 | 0.230 | 19.9 | 9.2 |
+| b600k.cps2024.acs100.s0 | 661,228 | 600,000 | 121,891 | 0.255 | 0.185 | 0.329 | 0.167 | 0.074 | 0.143 | 0.217 | 23.9 | 7.0 |
+| b600k.cps2023-2024.acs000.s0 | 1,256,347 | 92,547 | 31,781 | 0.251 | 0.173 | 0.357 | 0.168 | 0.129 | 0.215 | 0.259 | 26.8 | 10.6 |
+| b600k.cps2023-2024.acs100.s0 | 722,543 | 580,534 | 114,433 | 0.219 | 0.177 | 0.327 | 0.158 | 0.086 | 0.152 | 0.218 | 22.1 | 7.5 |
+| b600k.cps2022-2023-2024.acs000.s0 | 1,254,039 | 130,294 | 45,050 | 0.235 | 0.175 | 0.356 | 0.166 | 0.133 | 0.218 | 0.261 | 24.8 | 10.8 |
+| b600k.cps2022-2023-2024.acs050.s0 | 1,019,084 | 345,868 | 77,830 | 0.206 | 0.162 | 0.328 | 0.161 | 0.113 | 0.182 | 0.232 | 20.1 | 9.3 |
+| b600k.cps2022-2023-2024.acs100.s0 | 784,080 | 561,442 | 113,041 | 0.244 | 0.170 | 0.326 | 0.157 | 0.097 | 0.163 | 0.227 | 16.6 | 7.9 |
+| b1200k.cps0.acs100.s0 | 1,200,000 | 1,200,000 | 233,508 | 0.267 | 0.190 | 0.300 | 0.182 | 0.092 | 0.147 | 0.205 | 19.7 | 10.0 |
+| b1200k.cps2024.acs000.s0 | 2,517,604 | 55,762 | 18,676 | 0.243 | 0.189 | 0.344 | 0.166 | 0.129 | 0.217 | 0.252 | 50.2 | 18.3 |
+| b1200k.cps2024.acs100.s0 | 1,261,228 | 1,200,000 | 239,080 | 0.258 | 0.182 | 0.299 | 0.173 | 0.075 | 0.134 | 0.191 | 24.0 | 10.6 |
+| b1200k.cps2023-2024.acs000.s0 | 2,512,844 | 92,547 | 31,905 | 0.251 | 0.172 | 0.345 | 0.168 | 0.129 | 0.212 | 0.248 | 50.9 | 17.1 |
+| b1200k.cps2023-2024.acs100.s0 | 1,322,543 | 1,180,534 | 230,272 | 0.260 | 0.174 | 0.299 | 0.166 | 0.074 | 0.131 | 0.187 | 23.0 | 11.0 |
+| b1200k.cps2022-2023-2024.acs000.s0 | 2,508,197 | 130,294 | 45,160 | 0.235 | 0.176 | 0.342 | 0.166 | 0.133 | 0.213 | 0.247 | 107.3 | 18.3 |
+| b1200k.cps2022-2023-2024.acs100.s0 | 1,384,080 | 1,161,442 | 222,920 | 0.261 | 0.171 | 0.301 | 0.164 | 0.081 | 0.135 | 0.189 | 23.9 | 11.3 |
diff --git a/experiments/us-support-mix-bakeoff-20260930/holdout_by_dimension_level.csv b/experiments/us-support-mix-bakeoff-20260930/holdout_by_dimension_level.csv
new file mode 100644
index 0000000000..f725098840
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/holdout_by_dimension_level.csv
@@ -0,0 +1,601 @@
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diff --git a/experiments/us-support-mix-bakeoff-20260930/holdout_by_dimension_level_common_support.csv b/experiments/us-support-mix-bakeoff-20260930/holdout_by_dimension_level_common_support.csv
new file mode 100644
index 0000000000..37b88def40
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/holdout_by_dimension_level_common_support.csv
@@ -0,0 +1,601 @@
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diff --git a/experiments/us-support-mix-bakeoff-20260930/holdout_unsupported.csv b/experiments/us-support-mix-bakeoff-20260930/holdout_unsupported.csv
new file mode 100644
index 0000000000..a7ea5371f3
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/holdout_unsupported.csv
@@ -0,0 +1,61 @@
+product,arm,0
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diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_000.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_000.r0-750000.json
new file mode 100644
index 0000000000..92a25dbb53
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_000.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24008,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 216.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 24008
+ },
+ "households": 24008,
+ "missing_measures": [],
+ "nnz": 321574,
+ "peak_rss_gb": 7.57,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_000.pkl",
+ "wall_s": 1210.0
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_000.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_000.r750000-1210000.json
new file mode 100644
index 0000000000..0ccdc6ad60
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_000.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
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+  "population_aggregate_variables_checked": [
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+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 186.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 14494
+ },
+ "households": 14494,
+ "missing_measures": [],
+ "nnz": 194475,
+ "peak_rss_gb": 9.36,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_000.pkl",
+ "wall_s": 375.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_001.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_001.r0-750000.json
new file mode 100644
index 0000000000..71cbae95d4
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_001.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 23816,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 214.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 23816
+ },
+ "households": 23816,
+ "missing_measures": [],
+ "nnz": 318729,
+ "peak_rss_gb": 7.72,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_001.pkl",
+ "wall_s": 1210.1
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_001.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_001.r750000-1210000.json
new file mode 100644
index 0000000000..09c450602b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_001.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14504,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 162.7,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 14504
+ },
+ "households": 14504,
+ "missing_measures": [],
+ "nnz": 192938,
+ "peak_rss_gb": 8.16,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_001.pkl",
+ "wall_s": 1659.8
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_002.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_002.r0-750000.json
new file mode 100644
index 0000000000..fd54114d0e
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_002.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 23719,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 384.4,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 23719
+ },
+ "households": 23719,
+ "missing_measures": [],
+ "nnz": 319847,
+ "peak_rss_gb": 10.35,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_002.pkl",
+ "wall_s": 1848.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_002.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_002.r750000-1210000.json
new file mode 100644
index 0000000000..a71023c9be
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_002.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14616,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 310.4,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 14616
+ },
+ "households": 14616,
+ "missing_measures": [],
+ "nnz": 194501,
+ "peak_rss_gb": 10.63,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_002.pkl",
+ "wall_s": 239.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_003.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_003.r0-750000.json
new file mode 100644
index 0000000000..e84b5f7ae2
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_003.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24202,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 427.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36121
+ },
+ "households": 24202,
+ "missing_measures": [],
+ "nnz": 438523,
+ "peak_rss_gb": 10.74,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_003.pkl",
+ "wall_s": 1957.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_003.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_003.r750000-1210000.json
new file mode 100644
index 0000000000..a8d7661844
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_003.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14835,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 306.0,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 21837
+ },
+ "households": 14835,
+ "missing_measures": [],
+ "nnz": 265990,
+ "peak_rss_gb": 13.65,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_003.pkl",
+ "wall_s": 1304.8
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_004.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_004.r0-750000.json
new file mode 100644
index 0000000000..0c98e3fa9a
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_004.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24118,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 703.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58026
+ },
+ "households": 24118,
+ "missing_measures": [],
+ "nnz": 645906,
+ "peak_rss_gb": 11.5,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_004.pkl",
+ "wall_s": 1792.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_004.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_004.r750000-1210000.json
new file mode 100644
index 0000000000..556d46ded8
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_004.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15253,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 467.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36788
+ },
+ "households": 15253,
+ "missing_measures": [],
+ "nnz": 409314,
+ "peak_rss_gb": 16.34,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_004.pkl",
+ "wall_s": 232.9
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_005.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_005.r0-750000.json
new file mode 100644
index 0000000000..cc3aa493ac
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_005.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24361,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 747.0,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58545
+ },
+ "households": 24361,
+ "missing_measures": [],
+ "nnz": 652456,
+ "peak_rss_gb": 11.65,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_005.pkl",
+ "wall_s": 1704.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_005.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_005.r750000-1210000.json
new file mode 100644
index 0000000000..6d4328f721
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_005.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15178,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 298.7,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36586
+ },
+ "households": 15178,
+ "missing_measures": [],
+ "nnz": 405978,
+ "peak_rss_gb": 14.64,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_005.pkl",
+ "wall_s": 543.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_006.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_006.r0-750000.json
new file mode 100644
index 0000000000..766c6fb174
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_006.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24499,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1044.0,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58930
+ },
+ "households": 24499,
+ "missing_measures": [],
+ "nnz": 658167,
+ "peak_rss_gb": 12.44,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_006.pkl",
+ "wall_s": 1504.8
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_006.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_006.r750000-1210000.json
new file mode 100644
index 0000000000..6e81eddd40
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_006.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15018,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 614.5,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36230
+ },
+ "households": 15018,
+ "missing_measures": [],
+ "nnz": 401773,
+ "peak_rss_gb": 17.46,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_006.pkl",
+ "wall_s": 226.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_007.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_007.r0-750000.json
new file mode 100644
index 0000000000..4e8aa8a654
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_007.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24365,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1080.6,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58485
+ },
+ "households": 24365,
+ "missing_measures": [],
+ "nnz": 653135,
+ "peak_rss_gb": 12.55,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_007.pkl",
+ "wall_s": 1471.5
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_007.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_007.r750000-1210000.json
new file mode 100644
index 0000000000..b19bfc7b3b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_007.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14914,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 445.8,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35824
+ },
+ "households": 14914,
+ "missing_measures": [],
+ "nnz": 401258,
+ "peak_rss_gb": 17.47,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_007.pkl",
+ "wall_s": 180.7
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_008.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_008.r0-750000.json
new file mode 100644
index 0000000000..f60919e62e
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_008.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24450,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1332.5,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58996
+ },
+ "households": 24450,
+ "missing_measures": [],
+ "nnz": 660030,
+ "peak_rss_gb": 13.4,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_008.pkl",
+ "wall_s": 1464.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_008.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_008.r750000-1210000.json
new file mode 100644
index 0000000000..a212ac41d2
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_008.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14939,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 792.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35816
+ },
+ "households": 14939,
+ "missing_measures": [],
+ "nnz": 401461,
+ "peak_rss_gb": 18.83,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_008.pkl",
+ "wall_s": 265.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_009.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_009.r0-750000.json
new file mode 100644
index 0000000000..1deb7f0f1b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_009.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24446,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1370.0,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58836
+ },
+ "households": 24446,
+ "missing_measures": [],
+ "nnz": 652976,
+ "peak_rss_gb": 13.2,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_009.pkl",
+ "wall_s": 1499.1
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_009.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_009.r750000-1210000.json
new file mode 100644
index 0000000000..080cae87d2
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_009.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14900,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 588.2,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35910
+ },
+ "households": 14900,
+ "missing_measures": [],
+ "nnz": 400342,
+ "peak_rss_gb": 18.31,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_009.pkl",
+ "wall_s": 226.8
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_010.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_010.r0-750000.json
new file mode 100644
index 0000000000..77576b0cc0
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_010.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24309,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1640.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58246
+ },
+ "households": 24309,
+ "missing_measures": [],
+ "nnz": 651279,
+ "peak_rss_gb": 13.4,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_010.pkl",
+ "wall_s": 5502.1
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_010.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_010.r750000-1210000.json
new file mode 100644
index 0000000000..70a1246045
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_010.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15058,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 966.9,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36166
+ },
+ "households": 15058,
+ "missing_measures": [],
+ "nnz": 403602,
+ "peak_rss_gb": 19.53,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_010.pkl",
+ "wall_s": 243.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_011.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_011.r0-750000.json
new file mode 100644
index 0000000000..636ece4e3e
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_011.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24478,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1679.4,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58887
+ },
+ "households": 24478,
+ "missing_measures": [],
+ "nnz": 659237,
+ "peak_rss_gb": 13.2,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_011.pkl",
+ "wall_s": 2248.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_011.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_011.r750000-1210000.json
new file mode 100644
index 0000000000..9295eb94a6
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_011.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14992,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 742.7,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36043
+ },
+ "households": 14992,
+ "missing_measures": [],
+ "nnz": 401622,
+ "peak_rss_gb": 19.09,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_011.pkl",
+ "wall_s": 234.0
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_012.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_012.r0-750000.json
new file mode 100644
index 0000000000..86ae82270b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_012.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24417,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1976.7,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58840
+ },
+ "households": 24417,
+ "missing_measures": [],
+ "nnz": 654129,
+ "peak_rss_gb": 14.48,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_012.pkl",
+ "wall_s": 1007.5
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_012.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_012.r750000-1210000.json
new file mode 100644
index 0000000000..d4b55bcc3c
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_012.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15110,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1155.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36286
+ },
+ "households": 15110,
+ "missing_measures": [],
+ "nnz": 403765,
+ "peak_rss_gb": 20.3,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_012.pkl",
+ "wall_s": 262.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_013.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_013.r0-750000.json
new file mode 100644
index 0000000000..dabd58bb39
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_013.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24450,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1986.5,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58957
+ },
+ "households": 24450,
+ "missing_measures": [],
+ "nnz": 656744,
+ "peak_rss_gb": 13.2,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_013.pkl",
+ "wall_s": 1142.0
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_013.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_013.r750000-1210000.json
new file mode 100644
index 0000000000..46f75c193a
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_013.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15079,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 933.7,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36075
+ },
+ "households": 15079,
+ "missing_measures": [],
+ "nnz": 406006,
+ "peak_rss_gb": 19.43,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_013.pkl",
+ "wall_s": 276.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_014.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_014.r0-750000.json
new file mode 100644
index 0000000000..47e68a0879
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_014.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24444,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2320.4,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58476
+ },
+ "households": 24444,
+ "missing_measures": [],
+ "nnz": 655994,
+ "peak_rss_gb": 14.48,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_014.pkl",
+ "wall_s": 2684.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_014.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_014.r750000-1210000.json
new file mode 100644
index 0000000000..bb4c4e7892
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_014.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15010,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1331.2,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35829
+ },
+ "households": 15010,
+ "missing_measures": [],
+ "nnz": 400557,
+ "peak_rss_gb": 20.65,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_014.pkl",
+ "wall_s": 215.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_015.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_015.r0-750000.json
new file mode 100644
index 0000000000..7c0f6224f6
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_015.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24731,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2330.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 59184
+ },
+ "households": 24731,
+ "missing_measures": [],
+ "nnz": 660186,
+ "peak_rss_gb": 13.2,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_015.pkl",
+ "wall_s": 1280.5
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_015.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_015.r750000-1210000.json
new file mode 100644
index 0000000000..bc6c233257
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_015.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14923,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1094.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35618
+ },
+ "households": 14923,
+ "missing_measures": [],
+ "nnz": 398516,
+ "peak_rss_gb": 19.88,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_015.pkl",
+ "wall_s": 224.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_016.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_016.r0-750000.json
new file mode 100644
index 0000000000..cfea31ad35
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_016.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24430,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2649.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58804
+ },
+ "households": 24430,
+ "missing_measures": [],
+ "nnz": 654620,
+ "peak_rss_gb": 14.94,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_016.pkl",
+ "wall_s": 1810.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_016.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_016.r750000-1210000.json
new file mode 100644
index 0000000000..26e1edb621
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_016.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15019,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1488.5,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35883
+ },
+ "households": 15019,
+ "missing_measures": [],
+ "nnz": 403174,
+ "peak_rss_gb": 21.02,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_016.pkl",
+ "wall_s": 223.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_017.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_017.r0-750000.json
new file mode 100644
index 0000000000..b5f7f6916f
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_017.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24731,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2674.7,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 59030
+ },
+ "households": 24731,
+ "missing_measures": [],
+ "nnz": 663577,
+ "peak_rss_gb": 15.69,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_017.pkl",
+ "wall_s": 1446.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_017.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_017.r750000-1210000.json
new file mode 100644
index 0000000000..4baec13fec
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_017.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14836,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1275.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36014
+ },
+ "households": 14836,
+ "missing_measures": [],
+ "nnz": 399792,
+ "peak_rss_gb": 20.34,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_017.pkl",
+ "wall_s": 263.0
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_018.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_018.r0-750000.json
new file mode 100644
index 0000000000..d66bc9dac1
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_018.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24492,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2958.0,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58744
+ },
+ "households": 24492,
+ "missing_measures": [],
+ "nnz": 657118,
+ "peak_rss_gb": 16.31,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_018.pkl",
+ "wall_s": 1152.1
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_018.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_018.r750000-1210000.json
new file mode 100644
index 0000000000..07f4f5e24a
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_018.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15021,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1643.6,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35969
+ },
+ "households": 15021,
+ "missing_measures": [],
+ "nnz": 401656,
+ "peak_rss_gb": 21.26,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_018.pkl",
+ "wall_s": 233.7
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_019.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_019.r0-750000.json
new file mode 100644
index 0000000000..e170ae7fcf
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_019.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24518,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 3016.5,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58645
+ },
+ "households": 24518,
+ "missing_measures": [],
+ "nnz": 658818,
+ "peak_rss_gb": 15.69,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_019.pkl",
+ "wall_s": 2873.8
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_019.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_019.r750000-1210000.json
new file mode 100644
index 0000000000..a3e6f9bcbc
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_019.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15003,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1429.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36035
+ },
+ "households": 15003,
+ "missing_measures": [],
+ "nnz": 402583,
+ "peak_rss_gb": 20.53,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_019.pkl",
+ "wall_s": 201.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_020.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_020.r0-750000.json
new file mode 100644
index 0000000000..26ea479a07
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_020.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24527,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 3288.1,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 59016
+ },
+ "households": 24527,
+ "missing_measures": [],
+ "nnz": 661106,
+ "peak_rss_gb": 16.31,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_020.pkl",
+ "wall_s": 1497.7
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_020.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_020.r750000-1210000.json
new file mode 100644
index 0000000000..aecac6b616
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_020.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15182,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1781.2,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36499
+ },
+ "households": 15182,
+ "missing_measures": [],
+ "nnz": 406928,
+ "peak_rss_gb": 21.53,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_020.pkl",
+ "wall_s": 215.1
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_021.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_021.r0-750000.json
new file mode 100644
index 0000000000..ba86d63994
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_021.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24528,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 3360.2,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 59039
+ },
+ "households": 24528,
+ "missing_measures": [],
+ "nnz": 658964,
+ "peak_rss_gb": 15.69,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_021.pkl",
+ "wall_s": 1462.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_021.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_021.r750000-1210000.json
new file mode 100644
index 0000000000..46bf0d52d4
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_021.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14990,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1592.4,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36189
+ },
+ "households": 14990,
+ "missing_measures": [],
+ "nnz": 401645,
+ "peak_rss_gb": 20.8,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_021.pkl",
+ "wall_s": 239.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_022.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_022.r0-750000.json
new file mode 100644
index 0000000000..f5ed8c2b4b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_022.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24336,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 3610.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58431
+ },
+ "households": 24336,
+ "missing_measures": [],
+ "nnz": 651484,
+ "peak_rss_gb": 16.31,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_022.pkl",
+ "wall_s": 1308.7
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_022.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_022.r750000-1210000.json
new file mode 100644
index 0000000000..396633a205
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_022.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14883,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1915.8,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35846
+ },
+ "households": 14883,
+ "missing_measures": [],
+ "nnz": 397527,
+ "peak_rss_gb": 21.78,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_022.pkl",
+ "wall_s": 193.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_023.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_023.r0-750000.json
new file mode 100644
index 0000000000..f84a447d23
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_023.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24363,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 3676.2,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58225
+ },
+ "households": 24363,
+ "missing_measures": [],
+ "nnz": 654120,
+ "peak_rss_gb": 18.17,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_023.pkl",
+ "wall_s": 1186.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_023.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_023.r750000-1210000.json
new file mode 100644
index 0000000000..8ae8ccb9cb
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_023.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15123,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1731.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36268
+ },
+ "households": 15123,
+ "missing_measures": [],
+ "nnz": 404461,
+ "peak_rss_gb": 20.92,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_023.pkl",
+ "wall_s": 224.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_024.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_024.r0-750000.json
new file mode 100644
index 0000000000..b308730412
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_024.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24498,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 3856.7,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 59066
+ },
+ "households": 24498,
+ "missing_measures": [],
+ "nnz": 657380,
+ "peak_rss_gb": 16.72,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_024.pkl",
+ "wall_s": 868.9
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_024.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_024.r750000-1210000.json
new file mode 100644
index 0000000000..6e128c8ce8
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_024.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14929,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2045.5,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35850
+ },
+ "households": 14929,
+ "missing_measures": [],
+ "nnz": 399688,
+ "peak_rss_gb": 22.09,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_024.pkl",
+ "wall_s": 155.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_025.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_025.r0-750000.json
new file mode 100644
index 0000000000..f8264e1e3b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_025.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24556,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 4005.5,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 59209
+ },
+ "households": 24556,
+ "missing_measures": [],
+ "nnz": 657005,
+ "peak_rss_gb": 19.1,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_025.pkl",
+ "wall_s": 1279.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_025.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_025.r750000-1210000.json
new file mode 100644
index 0000000000..ddebde6831
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_025.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14859,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1859.9,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36090
+ },
+ "households": 14859,
+ "missing_measures": [],
+ "nnz": 400977,
+ "peak_rss_gb": 21.4,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_025.pkl",
+ "wall_s": 200.1
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_026.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_026.r0-750000.json
new file mode 100644
index 0000000000..72cb300909
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_026.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24396,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 4169.4,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58633
+ },
+ "households": 24396,
+ "missing_measures": [],
+ "nnz": 657190,
+ "peak_rss_gb": 17.61,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_026.pkl",
+ "wall_s": 2054.9
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_026.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_026.r750000-1210000.json
new file mode 100644
index 0000000000..6e5d55d837
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_026.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15071,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2167.7,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36483
+ },
+ "households": 15071,
+ "missing_measures": [],
+ "nnz": 403221,
+ "peak_rss_gb": 22.46,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_026.pkl",
+ "wall_s": 130.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_027.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_027.r0-750000.json
new file mode 100644
index 0000000000..d618d0d3c8
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_027.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24777,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 4319.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 59228
+ },
+ "households": 24777,
+ "missing_measures": [],
+ "nnz": 665835,
+ "peak_rss_gb": 19.1,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_027.pkl",
+ "wall_s": 1300.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_027.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_027.r750000-1210000.json
new file mode 100644
index 0000000000..0f49490151
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_027.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 3,
+  "group_nesting_verified": true,
+  "households": 14953,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 1990.8,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 35948
+ },
+ "households": 14953,
+ "missing_measures": [],
+ "nnz": 400581,
+ "peak_rss_gb": 21.65,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_027.pkl",
+ "wall_s": 151.0
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_028.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_028.r0-750000.json
new file mode 100644
index 0000000000..def36673d1
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_028.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24475,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 283.0,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 58424
+ },
+ "households": 24475,
+ "missing_measures": [],
+ "nnz": 659290,
+ "peak_rss_gb": 12.4,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_028.pkl",
+ "wall_s": 823.7
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_028.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_028.r750000-1210000.json
new file mode 100644
index 0000000000..edbe00e485
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_028.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15105,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2274.3,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36079
+ },
+ "households": 15105,
+ "missing_measures": [],
+ "nnz": 403961,
+ "peak_rss_gb": 22.72,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_028.pkl",
+ "wall_s": 113.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_029.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_029.r0-750000.json
new file mode 100644
index 0000000000..b3e7f4c343
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_029.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 5,
+  "group_nesting_verified": true,
+  "households": 24599,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 4632.0,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 59066
+ },
+ "households": 24599,
+ "missing_measures": [],
+ "nnz": 660416,
+ "peak_rss_gb": 19.1,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_029.pkl",
+ "wall_s": 1318.3
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_029.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_029.r750000-1210000.json
new file mode 100644
index 0000000000..1f460d72d3
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_029.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15063,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2129.8,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 36491
+ },
+ "households": 15063,
+ "missing_measures": [],
+ "nnz": 402516,
+ "peak_rss_gb": 21.97,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_029.pkl",
+ "wall_s": 146.1
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_030.r0-750000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_030.r0-750000.json
new file mode 100644
index 0000000000..b5b90d6a4e
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_030.r0-750000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 4,
+  "group_nesting_verified": true,
+  "households": 15426,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 198.0,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 37138
+ },
+ "households": 15426,
+ "missing_measures": [],
+ "nnz": 414033,
+ "peak_rss_gb": 12.0,
+ "rank_range": [
+  0,
+  750000
+ ],
+ "shard": "shard_030.pkl",
+ "wall_s": 664.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_030.r750000-1210000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_030.r750000-1210000.json
new file mode 100644
index 0000000000..d881d1ed09
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/acs/shard_030.r750000-1210000.json
@@ -0,0 +1,34 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 2,
+  "group_nesting_verified": true,
+  "households": 9578,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 2357.8,
+ "formula_derived_inputs": {
+  "person.is_spm_independent_minor_role": 22938
+ },
+ "households": 9578,
+ "missing_measures": [],
+ "nnz": 255123,
+ "peak_rss_gb": 22.72,
+ "rank_range": [
+  750000,
+  1210000
+ ],
+ "shard": "shard_030.pkl",
+ "wall_s": 95.0
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_000.json b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_000.json
new file mode 100644
index 0000000000..0f464322bc
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_000.json
@@ -0,0 +1,26 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 10,
+  "group_nesting_verified": true,
+  "households": 50000,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "households": 50000,
+ "missing_measures": [],
+ "nnz": 1176034,
+ "peak_rss_gb": 5.79,
+ "shard": "shard_000.pkl",
+ "wall_s": 2868.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_001.json b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_001.json
new file mode 100644
index 0000000000..fefc44da67
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_001.json
@@ -0,0 +1,26 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 10,
+  "group_nesting_verified": true,
+  "households": 50000,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "households": 50000,
+ "missing_measures": [],
+ "nnz": 1176937,
+ "peak_rss_gb": 9.1,
+ "shard": "shard_001.pkl",
+ "wall_s": 1477.8
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_002.json b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_002.json
new file mode 100644
index 0000000000..d46d05438b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_002.json
@@ -0,0 +1,29 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 10,
+  "group_nesting_verified": true,
+  "households": 50000,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 263.4,
+ "default_filled_inputs": {},
+ "households": 50000,
+ "missing_measures": [],
+ "nnz": 1160667,
+ "peak_rss_gb": 8.4,
+ "rank_range": null,
+ "shard": "shard_002.pkl",
+ "wall_s": 1355.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_003.json b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_003.json
new file mode 100644
index 0000000000..92e2ca324b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_003.json
@@ -0,0 +1,29 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 10,
+  "group_nesting_verified": true,
+  "households": 50000,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 513.2,
+ "default_filled_inputs": {},
+ "households": 50000,
+ "missing_measures": [],
+ "nnz": 1370784,
+ "peak_rss_gb": 10.2,
+ "rank_range": null,
+ "shard": "shard_003.pkl",
+ "wall_s": 516.0
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_004.json b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_004.json
new file mode 100644
index 0000000000..7cc1dc79f4
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_004.json
@@ -0,0 +1,29 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 10,
+  "group_nesting_verified": true,
+  "households": 50000,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 790.9,
+ "default_filled_inputs": {},
+ "households": 50000,
+ "missing_measures": [],
+ "nnz": 1487652,
+ "peak_rss_gb": 10.35,
+ "rank_range": null,
+ "shard": "shard_004.pkl",
+ "wall_s": 386.7
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_005.json b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_005.json
new file mode 100644
index 0000000000..887897f83a
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_005.json
@@ -0,0 +1,29 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 10,
+  "group_nesting_verified": true,
+  "households": 50000,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 261.8,
+ "default_filled_inputs": {},
+ "households": 50000,
+ "missing_measures": [],
+ "nnz": 1476724,
+ "peak_rss_gb": 8.23,
+ "rank_range": null,
+ "shard": "shard_005.pkl",
+ "wall_s": 1345.7
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_006.json b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_006.json
new file mode 100644
index 0000000000..48aae71354
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_006.json
@@ -0,0 +1,29 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 10,
+  "group_nesting_verified": true,
+  "households": 50000,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 5000,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "cpu_s": 567.4,
+ "default_filled_inputs": {},
+ "households": 50000,
+ "missing_measures": [],
+ "nnz": 1569083,
+ "peak_rss_gb": 15.59,
+ "rank_range": null,
+ "shard": "shard_006.pkl",
+ "wall_s": 641.6
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_007.json b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_007.json
new file mode 100644
index 0000000000..a229f25049
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/cps/shard_007.json
@@ -0,0 +1,26 @@
+{
+ "batching": {
+  "base_household_columns": 423,
+  "batches": 1,
+  "group_nesting_verified": true,
+  "households": 2932,
+  "jct_reform_families_simulated": 0,
+  "largest_batch_households": 2932,
+  "maximum_microsim_batch_size": 5000,
+  "method": "household_position_batches",
+  "population_aggregate_guard_armed": true,
+  "population_aggregate_variables_checked": [
+   "household_income_decile",
+   "medicaid_slcsp_state_average_cost_index",
+   "medicaid_slcsp_state_denominator",
+   "spm_unit_income_decile"
+  ]
+ },
+ "compiled_concepts": 421,
+ "households": 2932,
+ "missing_measures": [],
+ "nnz": 107555,
+ "peak_rss_gb": 5.13,
+ "shard": "shard_007.pkl",
+ "wall_s": 75.2
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/materialize/measures.json b/experiments/us-support-mix-bakeoff-20260930/materialize/measures.json
new file mode 100644
index 0000000000..11f409f661
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/materialize/measures.json
@@ -0,0 +1,424 @@
+[
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+ "bakeoff_concept_00001",
+ "bakeoff_concept_00002",
+ "bakeoff_concept_00003",
+ "bakeoff_concept_00004",
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+ "bakeoff_concept_00006",
+ "bakeoff_concept_00007",
+ "bakeoff_concept_00008",
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+ "bakeoff_concept_00016",
+ "bakeoff_concept_00017",
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+ "bakeoff_concept_00024",
+ "bakeoff_concept_00025",
+ "bakeoff_concept_00026",
+ "bakeoff_concept_00027",
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+ "bakeoff_concept_00030",
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+ "bakeoff_concept_00143",
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+ "bakeoff_concept_00238",
+ "bakeoff_concept_00239",
+ "bakeoff_concept_00240",
+ "bakeoff_concept_00241",
+ "bakeoff_concept_00242",
+ "bakeoff_concept_00243",
+ "bakeoff_concept_00244",
+ "bakeoff_concept_00245",
+ "bakeoff_concept_00246",
+ "bakeoff_concept_00247",
+ "bakeoff_concept_00248",
+ "bakeoff_concept_00249",
+ "bakeoff_concept_00250",
+ "bakeoff_concept_00251",
+ "bakeoff_concept_00252",
+ "bakeoff_concept_00253",
+ "bakeoff_concept_00254",
+ "bakeoff_concept_00255",
+ "bakeoff_concept_00256",
+ "bakeoff_concept_00257",
+ "bakeoff_concept_00258",
+ "bakeoff_concept_00259",
+ "bakeoff_concept_00260",
+ "bakeoff_concept_00261",
+ "bakeoff_concept_00262",
+ "bakeoff_concept_00263",
+ "bakeoff_concept_00264",
+ "bakeoff_concept_00265",
+ "bakeoff_concept_00266",
+ "bakeoff_concept_00267",
+ "bakeoff_concept_00268",
+ "bakeoff_concept_00269",
+ "bakeoff_concept_00270",
+ "bakeoff_concept_00271",
+ "bakeoff_concept_00272",
+ "bakeoff_concept_00273",
+ "bakeoff_concept_00274",
+ "bakeoff_concept_00275",
+ "bakeoff_concept_00276",
+ "bakeoff_concept_00277",
+ "bakeoff_concept_00278",
+ "bakeoff_concept_00279",
+ "bakeoff_concept_00280",
+ "bakeoff_concept_00281",
+ "bakeoff_concept_00282",
+ "bakeoff_concept_00283",
+ "bakeoff_concept_00284",
+ "bakeoff_concept_00285",
+ "bakeoff_concept_00286",
+ "bakeoff_concept_00287",
+ "bakeoff_concept_00288",
+ "bakeoff_concept_00289",
+ "bakeoff_concept_00290",
+ "bakeoff_concept_00291",
+ "bakeoff_concept_00292",
+ "bakeoff_concept_00293",
+ "bakeoff_concept_00294",
+ "bakeoff_concept_00295",
+ "bakeoff_concept_00296",
+ "bakeoff_concept_00297",
+ "bakeoff_concept_00298",
+ "bakeoff_concept_00299",
+ "bakeoff_concept_00300",
+ "bakeoff_concept_00301",
+ "bakeoff_concept_00302",
+ "bakeoff_concept_00303",
+ "bakeoff_concept_00304",
+ "bakeoff_concept_00305",
+ "bakeoff_concept_00306",
+ "bakeoff_concept_00307",
+ "bakeoff_concept_00308",
+ "bakeoff_concept_00309",
+ "bakeoff_concept_00310",
+ "bakeoff_concept_00311",
+ "bakeoff_concept_00312",
+ "bakeoff_concept_00313",
+ "bakeoff_concept_00314",
+ "bakeoff_concept_00315",
+ "bakeoff_concept_00316",
+ "bakeoff_concept_00317",
+ "bakeoff_concept_00318",
+ "bakeoff_concept_00319",
+ "bakeoff_concept_00320",
+ "bakeoff_concept_00321",
+ "bakeoff_concept_00322",
+ "bakeoff_concept_00323",
+ "bakeoff_concept_00324",
+ "bakeoff_concept_00325",
+ "bakeoff_concept_00326",
+ "bakeoff_concept_00327",
+ "bakeoff_concept_00328",
+ "bakeoff_concept_00329",
+ "bakeoff_concept_00330",
+ "bakeoff_concept_00331",
+ "bakeoff_concept_00332",
+ "bakeoff_concept_00333",
+ "bakeoff_concept_00334",
+ "bakeoff_concept_00335",
+ "bakeoff_concept_00336",
+ "bakeoff_concept_00337",
+ "bakeoff_concept_00338",
+ "bakeoff_concept_00339",
+ "bakeoff_concept_00340",
+ "bakeoff_concept_00341",
+ "bakeoff_concept_00342",
+ "bakeoff_concept_00343",
+ "bakeoff_concept_00344",
+ "bakeoff_concept_00345",
+ "bakeoff_concept_00346",
+ "bakeoff_concept_00347",
+ "bakeoff_concept_00348",
+ "bakeoff_concept_00349",
+ "bakeoff_concept_00350",
+ "bakeoff_concept_00351",
+ "bakeoff_concept_00352",
+ "bakeoff_concept_00353",
+ "bakeoff_concept_00354",
+ "bakeoff_concept_00355",
+ "bakeoff_concept_00356",
+ "bakeoff_concept_00357",
+ "bakeoff_concept_00358",
+ "bakeoff_concept_00359",
+ "bakeoff_concept_00360",
+ "bakeoff_concept_00361",
+ "bakeoff_concept_00362",
+ "bakeoff_concept_00363",
+ "bakeoff_concept_00364",
+ "bakeoff_concept_00365",
+ "bakeoff_concept_00366",
+ "bakeoff_concept_00367",
+ "bakeoff_concept_00368",
+ "bakeoff_concept_00369",
+ "bakeoff_concept_00370",
+ "bakeoff_concept_00371",
+ "bakeoff_concept_00372",
+ "bakeoff_concept_00373",
+ "bakeoff_concept_00374",
+ "bakeoff_concept_00375",
+ "bakeoff_concept_00376",
+ "bakeoff_concept_00377",
+ "bakeoff_concept_00378",
+ "bakeoff_concept_00379",
+ "bakeoff_concept_00380",
+ "bakeoff_concept_00381",
+ "bakeoff_concept_00382",
+ "bakeoff_concept_00383",
+ "bakeoff_concept_00384",
+ "bakeoff_concept_00385",
+ "bakeoff_concept_00386",
+ "bakeoff_concept_00387",
+ "bakeoff_concept_00388",
+ "bakeoff_concept_00389",
+ "bakeoff_concept_00390",
+ "bakeoff_concept_00391",
+ "bakeoff_concept_00392",
+ "bakeoff_concept_00393",
+ "bakeoff_concept_00394",
+ "bakeoff_concept_00395",
+ "bakeoff_concept_00396",
+ "bakeoff_concept_00397",
+ "bakeoff_concept_00398",
+ "bakeoff_concept_00399",
+ "bakeoff_concept_00400",
+ "bakeoff_concept_00401",
+ "bakeoff_concept_00402",
+ "bakeoff_concept_00403",
+ "bakeoff_concept_00404",
+ "bakeoff_concept_00405",
+ "bakeoff_concept_00406",
+ "bakeoff_concept_00407",
+ "bakeoff_concept_00408",
+ "bakeoff_concept_00409",
+ "bakeoff_concept_00410",
+ "bakeoff_concept_00411",
+ "bakeoff_concept_00412",
+ "bakeoff_concept_00413",
+ "bakeoff_concept_00414",
+ "bakeoff_concept_00415",
+ "bakeoff_concept_00416",
+ "bakeoff_concept_00417",
+ "bakeoff_concept_00418",
+ "bakeoff_report_spm_poor_persons",
+ "bakeoff_report_spm_poor_children",
+ "state_income_tax"
+]
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/memo_tables.py b/experiments/us-support-mix-bakeoff-20260930/memo_tables.py
new file mode 100644
index 0000000000..02c649c46d
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/memo_tables.py
@@ -0,0 +1,70 @@
+"""Build the memo's results tables from the report receipts (no hand copying).
+
+Reads dimensions_by_arm.csv; arms with three ACS draws (the 300k one- and
+three-year ACS arms) are shown as the mean of the draws.
+Run: uv run python experiments/us-support-mix-bakeoff-20260930/memo_tables.py
+"""
+
+from pathlib import Path
+
+import pandas as pd
+
+HERE = Path(__file__).resolve().parent
+ROWS = {
+    "national": [
+        ("CPS 2022–24 only", "natural.cps2022-2023-2024.acs100"),
+        ("CPS 2022–24 + clones to 300k", "b300k.cps2022-2023-2024.acs000"),
+        ("CPS 2024 + ACS to 300k (3 draws)", "b300k.cps2024.acs100"),
+        ("CPS 2023–24 + ACS to 300k", "b300k.cps2023-2024.acs100"),
+        ("**CPS 2022–24 + ACS to 300k (3 draws)**", "b300k.cps2022-2023-2024.acs100"),
+        ("ACS only, 300k", "b300k.cps0.acs100"),
+        ("CPS 2022–24 + 50/50 to 600k", "b600k.cps2022-2023-2024.acs050"),
+        ("CPS 2022–24 + ACS to 600k", "b600k.cps2022-2023-2024.acs100"),
+        ("CPS 2022–24 + ACS to 1.2M", "b1200k.cps2022-2023-2024.acs100"),
+    ],
+    "local": [
+        ("CPS 2022–24 only", "natural.cps2022-2023-2024.acs100"),
+        ("CPS 2022–24 + clones to 600k", "b600k.cps2022-2023-2024.acs000"),
+        ("CPS 2022–24 + clones to 1.2M", "b1200k.cps2022-2023-2024.acs000"),
+        ("CPS 2022–24 + 50/50 to 600k", "b600k.cps2022-2023-2024.acs050"),
+        ("CPS 2022–24 + ACS to 600k", "b600k.cps2022-2023-2024.acs100"),
+        ("**CPS 2022–24 + ACS to 1.2M**", "b1200k.cps2022-2023-2024.acs100"),
+        ("CPS 2024 + ACS to 1.2M", "b1200k.cps2024.acs100"),
+        ("ACS only, 1.2M", "b1200k.cps0.acs100"),
+    ],
+}
+COLUMNS = [
+    ("cps_native_weighted.state", "CPS-native, state"),
+    ("cps_native_weighted.cd", "CPS-native, CD"),
+    ("income_components.state", "Income, state"),
+    ("tax_items.state", "Tax items, state"),
+    ("program_receipt.state", "Program receipt, state"),
+    ("demographics_pep.state", "Age (PEP), state"),
+]
+
+
+def main() -> None:
+    table = pd.read_csv(HERE / "dimensions_by_arm.csv")
+    table["stem"] = table["arm"].str.replace(r"\.s\d+$", "", regex=True)
+    for product, rows in ROWS.items():
+        print(f"### {product}\n")
+        head = "| Arm | Rows | ESS | " + " | ".join(label for _, label in COLUMNS) + " | ACS-native state / CD / county |"
+        print(head)
+        print("|" + "---|" * (head.count("|") - 1))
+        for label, stem in rows:
+            group = table[(table["product"] == product) & (table["stem"] == stem)]
+            if "draws" not in label:
+                group = group[group["seed"] == 0]
+            mean = group.mean(numeric_only=True)
+            cells = [f"{mean[c]:.3f}" for c, _ in COLUMNS]
+            acs = " / ".join(f"{mean[f'acs_native.{lv}']:.3f}" for lv in ("state", "cd", "county"))
+            print(f"| {label} | {int(mean['rows']):,} | {mean['ess_distinct']:,.0f} | " + " | ".join(cells) + f" | {acs} |")
+        spread = table[(table["product"] == product) & table["stem"].eq(rows[4][1] if product == "national" else "b300k.cps2022-2023-2024.acs100")]
+        sd = spread.std(numeric_only=True)
+        cells = [f"{sd[c]:.3f}" for c, _ in COLUMNS]
+        acs = " / ".join(f"{sd[f'acs_native.{lv}']:.3f}" for lv in ("state", "cd", "county"))
+        print("| SD across 3 draws (300k, 2022–24) | | | " + " | ".join(cells) + f" | {acs} |\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/us-support-mix-bakeoff-20260930/partcheck_acs.json b/experiments/us-support-mix-bakeoff-20260930/partcheck_acs.json
new file mode 100644
index 0000000000..d7583dd0df
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/partcheck_acs.json
@@ -0,0 +1,62 @@
+{
+ "concept_specs_sha256": "ffedf394b91dafd353102a358ef7cf374eb518eb9f9164b8907e206b1bd60ab0",
+ "max_scaled_abs_diff": 5.82326495675032e-08,
+ "parts": {
+  "shard_000.r0-750000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 0.0,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_003.r750000-1210000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.447629837039056e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_007.r0-750000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.243655635500912e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_012.r750000-1210000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.4455022574601887e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_015.r0-750000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.82326495675032e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_021.r750000-1210000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 4.908228158593294e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_028.r750000-1210000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 4.239946725917378e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_030.r0-750000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.025972904464072e-08,
+   "n_columns_over_tolerance": 0
+  }
+ },
+ "peak_rss_gb": 6.82,
+ "per_part": 200,
+ "shards": "/Users/maxghenis/PolicyEngine/_bakeoff-support-mix/work/shards/acs",
+ "source": "acs",
+ "source_h5_sha256": "ed2e63080aa39ab3681637590dc3dbac4bc9d01d163be9ee3c2ed28adc98d6ca",
+ "summary_sha256": "3aab5e5e0a6e226b90adf3c48f5f4d45fe51d716430be82b3455e88eafb6728d",
+ "tolerance": 1e-05,
+ "wall_s": 2042.4
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/partcheck_cps.json b/experiments/us-support-mix-bakeoff-20260930/partcheck_cps.json
new file mode 100644
index 0000000000..daea5faf1b
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/partcheck_cps.json
@@ -0,0 +1,62 @@
+{
+ "concept_specs_sha256": "ffedf394b91dafd353102a358ef7cf374eb518eb9f9164b8907e206b1bd60ab0",
+ "max_scaled_abs_diff": 5.745301478087966e-08,
+ "parts": {
+  "shard_000.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 4.852139807865107e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_001.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 4.4159883064629645e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_002.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 4.814175938007279e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_003.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.3810296320387365e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_004.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.623471786395348e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_005.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.745301478087966e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_006.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 5.002892922832628e-08,
+   "n_columns_over_tolerance": 0
+  },
+  "shard_007.npz": {
+   "columns_over_tolerance": [],
+   "households": 200,
+   "max_scaled_abs_diff": 2.9203917845757813e-08,
+   "n_columns_over_tolerance": 0
+  }
+ },
+ "peak_rss_gb": 6.7,
+ "per_part": 200,
+ "shards": "/Users/maxghenis/PolicyEngine/_bakeoff-support-mix/partcheck/cps-shards/shards/cps",
+ "source": "cps",
+ "source_h5_sha256": "f7b46f4b83cb83f48ebe2a262a42495fcc18af85e96d4e5b52a63d2fe89d947b",
+ "summary_sha256": null,
+ "tolerance": 1e-05,
+ "wall_s": 2020.5
+}
\ No newline at end of file
diff --git a/experiments/us-support-mix-bakeoff-20260930/replicate_mean.csv b/experiments/us-support-mix-bakeoff-20260930/replicate_mean.csv
new file mode 100644
index 0000000000..511937c908
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/replicate_mean.csv
@@ -0,0 +1,5 @@
+product,cps_years,cps_native_weighted.state,cps_native_weighted.cd,income_components.state,income_components.cd,tax_items.state,tax_items.cd,program_receipt.state,demographics_pep.state,acs_native.state,acs_native.cd,acs_native.county
+local,1,0.11894318079650662,0.19172382198188176,0.22690030366787017,0.28799363964692565,0.13663843894794359,0.2274115950024009,0.28775966790796403,0.15847767825987774,0.07958422347466958,0.15258728495547655,0.2204873690953388
+local,3,0.1121404782384457,0.18847554352416773,0.22872348511799676,0.28100577107478103,0.10304270496916955,0.2174572238251428,0.23700948112290268,0.1656717039231341,0.10976954935966093,0.20412753502506617,0.2505918358640243
+national,1,0.11719139228491672,0.3000152156752607,0.24925425794021458,0.3608241822139546,0.12963301172451341,0.37112420444172084,0.24074475062447478,0.15927236712902587,0.0856279370641836,0.16629675307597977,0.2549953962695645
+national,3,0.11344403854754743,0.29060085792301577,0.22408180624594445,0.34573459904498427,0.1132993838096618,0.35831347483639103,0.19829652894282138,0.16329762772247428,0.11002638468920033,0.18828742304459464,0.2586466421697818
diff --git a/experiments/us-support-mix-bakeoff-20260930/replicate_spread.csv b/experiments/us-support-mix-bakeoff-20260930/replicate_spread.csv
new file mode 100644
index 0000000000..e255404d45
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/replicate_spread.csv
@@ -0,0 +1,5 @@
+product,cps_years,cps_native_weighted.state,cps_native_weighted.cd,income_components.state,income_components.cd,tax_items.state,tax_items.cd,program_receipt.state,demographics_pep.state,acs_native.state,acs_native.cd,acs_native.county
+local,1,0.0021382955135564566,0.0033238529093177044,0.006379544884992923,0.01156960527790735,0.003656044965125471,0.0020583177504610857,0.024184288036940685,0.008761082783022367,0.00202796215204033,0.001991683419148612,0.0021399579716624606
+local,3,0.000951584189827157,0.0018887321532608471,0.006572927521882116,0.002187831181070142,0.002166421431950712,0.0019497770531794129,0.00569989826264732,0.001569968197074798,0.00022579896378769324,0.0016328598786634271,0.00048709241981480315
+national,1,0.0021202551404598457,0.001971241618144151,0.0015542320943534686,0.007985798596900882,0.002777874282566795,0.0032961036113893245,0.040946827732212004,0.007650750981897314,0.0014198978206555368,0.0020745229020417114,0.003164172245521262
+national,3,0.000279345580907213,0.0022284614766071424,0.00651339016838397,0.007025408080724732,0.00179743188387736,0.005016700663172885,0.014130265887185979,0.005495391504626003,0.0006413096855843224,0.0012720996328500355,0.002217899701858923
diff --git a/experiments/us-support-mix-bakeoff-20260930/spm_flags.json b/experiments/us-support-mix-bakeoff-20260930/spm_flags.json
new file mode 100644
index 0000000000..ccbe6974e9
--- /dev/null
+++ b/experiments/us-support-mix-bakeoff-20260930/spm_flags.json
@@ -0,0 +1,9 @@
+{
+ "acs_households": 1531614,
+ "group_quarters_households": 183206,
+ "group_quarters_share": 0.11961630019051797,
+ "rule": "age >= 18 or (age >= 15 and (is_household_head or is_household_spouse)), per SPM unit",
+ "spm_zero_adult_households": 5246,
+ "spm_zero_adult_in_group_quarters": 5246,
+ "spm_zero_adult_share": 0.003425144977781608
+}
\ No newline at end of file
diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/support_mix.py b/packages/microcosm-build/src/microcosm/build/us_runtime/support_mix.py
new file mode 100644
index 0000000000..62799663ba
--- /dev/null
+++ b/packages/microcosm-build/src/microcosm/build/us_runtime/support_mix.py
@@ -0,0 +1,400 @@
+"""Support-mix bake-off primitives: CPS years and ACS rows at fixed row budgets.
+
+The US calibration support can grow two ways. Pooling more CPS ASEC years adds
+households at full CPS richness; adding ACS households adds real variation in
+what the ACS observes (housing, local geography, composition) and only model
+draws in what it does not (the CPS-only variables imputed onto ACS rows). A
+third way, cloning CPS households to new locations, adds degrees of freedom
+for geography alone. This module holds the pure, engine-free pieces of a
+bake-off that compares those mixes at a fixed household budget:
+
+- arm planning: how many CPS source households, ACS households and CPS
+  location clones an arm holds (:func:`plan_arm_counts`);
+- nested, identity-keyed ACS selection (:func:`acs_selection_order`), so a
+  smaller arm's ACS households are always a subset of a larger arm's;
+- deterministic clone allocation (:func:`clone_copies`);
+- the arm's starting weights (:func:`arm_initial_weights`);
+- the US target split mirrored from the pending ``target_split`` module
+  (:func:`target_split_group_key`, :func:`target_split_role`);
+- effective sample size over distinct households (:func:`distinct_unit_ess`).
+
+A "source household" is one survey household record: one ASEC household-year
+or one ACS household. A CPS source household keeps its PUF tax-detail clone
+rows (the Route A support channels), so it can span two or three physical
+rows; every physical row of one source household shares one unit key.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import math
+import re
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import dataclass
+
+import numpy as np
+
+__all__ = [
+    "HOLDOUT_FRACTION",
+    "SEALED_FRACTION",
+    "TARGET_SPLIT_HOLDOUT_SALT",
+    "TARGET_SPLIT_SEALED_SALT",
+    "ArmCounts",
+    "SupportMixArm",
+    "acs_selection_order",
+    "arm_initial_weights",
+    "assemble_target_matrix",
+    "clone_copies",
+    "distinct_unit_ess",
+    "hash_uniform",
+    "kish_ess",
+    "plan_arm_counts",
+    "target_split_group_key",
+    "target_split_role",
+]
+
+#: The release split's salts and fractions, copied from the unmerged
+#: ``us-target-roles-holdout`` branch (microcosm.build.us_runtime.target_split)
+#: as of 2026-09-28. Its pins, which force whole groups to train, are not
+#: mirrored: they were not yet written. Swap this block for that module once
+#: it merges.
+TARGET_SPLIT_SEALED_SALT = "microcosm.us.target_split.v1.sealed"
+TARGET_SPLIT_HOLDOUT_SALT = "microcosm.us.target_split.v1.holdout"
+SEALED_FRACTION = 0.05
+HOLDOUT_FRACTION = 0.10
+
+_HASH_UNIFORM_SCALE = float(2**64)
+_VINTAGE_TOKEN = re.compile(r"^(?:(?:ty|cy|fy|v|oep)\d{4}|month\d{4}_\d{1,2})$")
+_HEX16_TOKEN = re.compile(r"^[0-9a-f]{16}$")
+
+
+def hash_uniform(key: str, *, salt: str) -> float:
+    """A key's deterministic uniform draw on ``[0, 1)``.
+
+    ``sha256(salt + "\\x1f" + key)``, first 8 bytes big-endian, divided by
+    ``2**64`` — byte-identical to the pending
+    ``microcosm.build.holdout.hash_holdout_uniform``. The draw depends only on
+    the key and the salt, never on which other keys exist or their order.
+    """
+    if not isinstance(key, str) or not key:
+        raise ValueError(f"hash key must be a non-empty string, got {key!r}.")
+    if not isinstance(salt, str) or not salt:
+        raise ValueError(f"hash salt must be a non-empty string, got {salt!r}.")
+    digest = hashlib.sha256(f"{salt}\x1f{key}".encode()).digest()
+    return int.from_bytes(digest[:8], "big") / _HASH_UNIFORM_SCALE
+
+
+def target_split_group_key(name: str, hierarchy_parent: str | None = None) -> str:
+    """The unit a target's split role is drawn for.
+
+    A congressional-district child takes its state parent's key, so a
+    district is never held out while its parent and trained siblings pin it.
+    The name is split on ``"."``; vintage tokens (``ty2023``, ``v2024``,
+    ``month2024_7``, ...) are dropped so a restated vintage keeps its role;
+    names with a ``current_cd`` token also drop a trailing 16-hex digest.
+    Bare 4-digit tokens are district GEOIDs and are always kept.
+    """
+    source = hierarchy_parent if hierarchy_parent else name
+    if not isinstance(source, str) or not source:
+        raise ValueError(f"target name must be a non-empty string, got {source!r}.")
+    tokens = [token for token in source.split(".") if not _VINTAGE_TOKEN.match(token)]
+    if "current_cd" in tokens and tokens and _HEX16_TOKEN.match(tokens[-1]):
+        tokens = tokens[:-1]
+    return ".".join(tokens)
+
+
+def target_split_role(group_key: str) -> str:
+    """``"sealed"``, ``"holdout"`` or ``"train"`` for one group key.
+
+    Sealed first, on its own salt, so the sealed tier rotates independently
+    of the holdout; then holdout on the holdout salt. About 5% sealed, 9.5%
+    holdout, 85.5% train.
+    """
+    if hash_uniform(group_key, salt=TARGET_SPLIT_SEALED_SALT) < SEALED_FRACTION:
+        return "sealed"
+    if hash_uniform(group_key, salt=TARGET_SPLIT_HOLDOUT_SALT) < HOLDOUT_FRACTION:
+        return "holdout"
+    return "train"
+
+
+@dataclass(frozen=True)
+class SupportMixArm:
+    """One bake-off arm.
+
+    Attributes:
+        budget: Source households in the arm, or ``None`` for a CPS-only arm
+            at its natural size (no fill).
+        cps_income_years: ASEC income years whose households are all kept.
+        acs_fill_share: Share of the fill (``budget`` minus the CPS source
+            households) taken by ACS households; the rest are CPS location
+            clones. ``1.0`` fills with ACS only, ``0.0`` with clones only.
+        seed: Replicate index. The bake-off driver takes the ``seed``-th
+            disjoint block of one fixed ACS order (so seed 0 arms are nested
+            across budgets and replicates are independent draws) and salts
+            the clone draws with it.
+    """
+
+    budget: int | None
+    cps_income_years: tuple[int, ...]
+    acs_fill_share: float = 1.0
+    seed: int = 0
+
+    def __post_init__(self) -> None:
+        if self.budget is not None and (
+            isinstance(self.budget, bool) or int(self.budget) <= 0
+        ):
+            raise ValueError(f"budget must be positive or None, got {self.budget!r}.")
+        years = tuple(int(year) for year in self.cps_income_years)
+        if len(set(years)) != len(years):
+            raise ValueError(f"cps_income_years repeats a year: {years}.")
+        object.__setattr__(self, "cps_income_years", tuple(sorted(years)))
+        share = float(self.acs_fill_share)
+        if not (math.isfinite(share) and 0.0 <= share <= 1.0):
+            raise ValueError(f"acs_fill_share must be in [0, 1], got {share!r}.")
+        object.__setattr__(self, "acs_fill_share", share)
+
+    @property
+    def label(self) -> str:
+        budget = "natural" if self.budget is None else f"b{self.budget // 1000}k"
+        years = (
+            "cps" + "-".join(str(year) for year in self.cps_income_years)
+            if self.cps_income_years
+            else "cps0"
+        )
+        return f"{budget}.{years}.acs{round(self.acs_fill_share * 100):03d}.s{self.seed}"
+
+
+@dataclass(frozen=True)
+class ArmCounts:
+    """Source-household counts of one arm; they sum to its budget."""
+
+    cps: int
+    acs: int
+    clones: int
+
+    @property
+    def total(self) -> int:
+        return self.cps + self.acs + self.clones
+
+
+def plan_arm_counts(
+    arm: SupportMixArm,
+    *,
+    cps_households_by_year: Mapping[int, int],
+    acs_households: int,
+) -> ArmCounts:
+    """How many CPS, ACS and clone source households ``arm`` holds.
+
+    Raises:
+        ValueError: If a year has no CPS households, the CPS years alone
+            exceed the budget, clones are requested without CPS households,
+            or the ACS fill exceeds the ACS households available.
+    """
+    missing = [year for year in arm.cps_income_years if year not in cps_households_by_year]
+    if missing:
+        raise ValueError(f"no CPS households for income year(s) {missing}.")
+    cps = int(sum(int(cps_households_by_year[year]) for year in arm.cps_income_years))
+    if arm.budget is None:
+        return ArmCounts(cps=cps, acs=0, clones=0)
+    fill = int(arm.budget) - cps
+    if fill < 0:
+        raise ValueError(
+            f"{arm.label}: {cps:,} CPS source households exceed the "
+            f"{arm.budget:,} budget."
+        )
+    acs = int(round(fill * arm.acs_fill_share))
+    clones = fill - acs
+    if clones and not cps:
+        raise ValueError(f"{arm.label}: clones need at least one CPS household.")
+    if acs > int(acs_households):
+        raise ValueError(
+            f"{arm.label}: the ACS fill of {acs:,} exceeds the "
+            f"{int(acs_households):,} ACS households available."
+        )
+    return ArmCounts(cps=cps, acs=acs, clones=clones)
+
+
+def acs_selection_order(unit_keys: Sequence[str], *, salt: str) -> np.ndarray:
+    """Positions of ``unit_keys`` in selection order.
+
+    An arm with ``n`` ACS households takes the first ``n`` positions, so
+    selections are nested across budgets: every household in a smaller arm is
+    in every larger arm with the same salt. Ties (identical draws) break by
+    key, so the order is a pure function of the key set.
+    """
+    keys = [str(key) for key in unit_keys]
+    draws = np.fromiter(
+        (hash_uniform(key, salt=salt) for key in keys), dtype=np.float64, count=len(keys)
+    )
+    return np.lexsort((np.asarray(keys, dtype=object).astype(str), draws))
+
+
+def clone_copies(unit_keys: Sequence[str], n_clones: int, *, salt: str) -> np.ndarray:
+    """Extra location copies per CPS source household, summing to ``n_clones``.
+
+    Every household gets ``n_clones // n`` copies and the remainder goes to
+    the households first in hash order, so no household carries more than one
+    copy above any other.
+    """
+    n = len(unit_keys)
+    if n_clones < 0:
+        raise ValueError(f"n_clones must be non-negative, got {n_clones!r}.")
+    if n == 0:
+        if n_clones:
+            raise ValueError("clones need at least one CPS household.")
+        return np.zeros(0, dtype=np.int64)
+    copies = np.full(n, n_clones // n, dtype=np.int64)
+    remainder = n_clones - int(copies.sum())
+    if remainder:
+        order = acs_selection_order(unit_keys, salt=salt)
+        copies[order[:remainder]] += 1
+    return copies
+
+
+def arm_initial_weights(
+    *,
+    cps_design_weights: np.ndarray,
+    cps_income_year: np.ndarray,
+    cps_copies: np.ndarray,
+    acs_design_weights: np.ndarray,
+    counts: ArmCounts,
+    total_mass: float,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Starting weights for an arm's CPS rows (with copies) and ACS rows.
+
+    - Each CPS income year gets an equal share of the CPS mass, keeping the
+      within-year design weights' shape.
+    - A CPS row with ``k`` copies splits its weight over ``1 + k`` rows.
+    - The ACS rows keep their design weights' shape.
+    - The CPS side (source households plus clones) and the ACS side share
+      ``total_mass`` in proportion to their source-household counts, so the
+      mean starting weight per source household is the same on both sides.
+
+    Args:
+        cps_design_weights: Design weight per selected CPS physical row.
+        cps_income_year: Income year per selected CPS physical row.
+        cps_copies: Extra copies per selected CPS physical row (every row of
+            one source household carries that household's count).
+        acs_design_weights: Design weight per selected ACS row.
+        counts: The arm's source-household counts.
+        total_mass: The household weight the arm's weights sum to.
+
+    Returns:
+        ``(cps_row_weights, acs_row_weights)``: the CPS array is per physical
+        row *per copy* (divide-by-``1 + k`` already applied), to be repeated
+        ``1 + k`` times by the caller.
+    """
+    cps_w = np.asarray(cps_design_weights, dtype=np.float64)
+    years = np.asarray(cps_income_year)
+    copies = np.asarray(cps_copies, dtype=np.int64)
+    acs_w = np.asarray(acs_design_weights, dtype=np.float64)
+    if cps_w.shape != years.shape or cps_w.shape != copies.shape:
+        raise ValueError("CPS weights, years and copies must align.")
+    for name, values in (("CPS", cps_w), ("ACS", acs_w)):
+        if not np.isfinite(values).all() or (values <= 0).any():
+            raise ValueError(f"{name} design weights must be finite and positive.")
+    if (copies < 0).any():
+        raise ValueError("copies must be non-negative.")
+    if not (math.isfinite(total_mass) and total_mass > 0):
+        raise ValueError(f"total_mass must be positive, got {total_mass!r}.")
+    source = counts.total
+    if source <= 0:
+        raise ValueError("an arm needs at least one source household.")
+    cps_share = (counts.cps + counts.clones) / source
+    acs_share = counts.acs / source
+    if (counts.cps + counts.clones) and not len(cps_w):
+        raise ValueError("counts declare CPS households but no CPS rows were given.")
+    if counts.acs and not len(acs_w):
+        raise ValueError("counts declare ACS households but no ACS rows were given.")
+
+    cps_out = np.zeros_like(cps_w)
+    if len(cps_w):
+        unique_years = np.unique(years)
+        per_year_mass = cps_share * total_mass / len(unique_years)
+        for year in unique_years:
+            in_year = years == year
+            cps_out[in_year] = cps_w[in_year] * (per_year_mass / cps_w[in_year].sum())
+        cps_out = cps_out / (1 + copies)
+    acs_out = (
+        acs_w * (acs_share * total_mass / acs_w.sum()) if len(acs_w) else acs_w.copy()
+    )
+    return cps_out, acs_out
+
+
+def kish_ess(weights: np.ndarray) -> float:
+    """Kish effective sample size ``(sum w)^2 / sum w^2`` over rows."""
+    w = np.asarray(weights, dtype=np.float64)
+    if not np.isfinite(w).all() or (w < 0).any():
+        raise ValueError("weights must be finite and non-negative.")
+    denominator = float(np.square(w).sum())
+    return 0.0 if denominator == 0.0 else float(w.sum() ** 2 / denominator)
+
+
+def distinct_unit_ess(weights: np.ndarray, unit_codes: Iterable) -> float:
+    """Kish ESS after summing weights within each distinct unit.
+
+    A repeated household (the same ASEC housing unit in two rotation years, a
+    PUF clone row, a location clone) is one unit, so its rows count once.
+    Never exceeds the row-level Kish ESS nor the number of distinct units.
+    """
+    w = np.asarray(weights, dtype=np.float64)
+    codes = np.asarray(list(unit_codes) if not isinstance(unit_codes, np.ndarray) else unit_codes)
+    if codes.shape != w.shape:
+        raise ValueError("weights and unit codes must align.")
+    _, inverse = np.unique(codes, return_inverse=True)
+    unit_weight = np.bincount(inverse.ravel(), weights=w)
+    return kish_ess(unit_weight)
+
+
+def assemble_target_matrix(
+    values,
+    row_geography: Mapping[str, np.ndarray],
+    target_concept: np.ndarray,
+    target_level: Sequence[str],
+    target_geography: np.ndarray,
+):
+    """Targets x rows: ``A[t, i] = values[i, concept(t)]`` where row ``i`` is
+    in target ``t``'s geography, else 0.
+
+    ``values`` is a rows x concepts sparse matrix. ``row_geography`` maps each
+    level (``"state"``, ``"cd"``) to one integer code per row; ``"national"``
+    needs none. Several targets may share a (concept, level, geography) cell
+    (the same concept published by two sources); each receives the cell's
+    values.
+    """
+    import scipy.sparse as sp
+
+    coo = sp.coo_matrix(values)
+    concept = np.asarray(target_concept, dtype=np.int64)
+    level = np.asarray(target_level, dtype=object)
+    geography = np.asarray(target_geography, dtype=np.int64)
+    n_targets, n_rows = len(concept), coo.shape[0]
+    rows_parts, cols_parts, data_parts = [], [], []
+    for name in np.unique(level):
+        sel = np.flatnonzero(level == name)
+        if name == "national":
+            row_geo = np.zeros(n_rows, dtype=np.int64)
+            target_geo = np.zeros(len(sel), dtype=np.int64)
+        else:
+            row_geo = np.asarray(row_geography[name], dtype=np.int64)
+            target_geo = geography[sel]
+        span = int(max(row_geo.max(initial=0), target_geo.max(initial=0))) + 1
+        cell = concept[sel] * span + target_geo
+        order = np.argsort(cell, kind="stable")
+        cells_sorted, targets_sorted = cell[order], sel[order]
+        entry = coo.col.astype(np.int64) * span + row_geo[coo.row]
+        low = np.searchsorted(cells_sorted, entry, "left")
+        count = np.searchsorted(cells_sorted, entry, "right") - low
+        keep = count > 0
+        repeat = count[keep]
+        starts = np.repeat(low[keep], repeat)
+        offsets = np.arange(int(repeat.sum())) - np.repeat(np.cumsum(repeat) - repeat, repeat)
+        rows_parts.append(targets_sorted[starts + offsets])
+        cols_parts.append(np.repeat(coo.row[keep], repeat))
+        data_parts.append(np.repeat(coo.data[keep], repeat))
+    if not rows_parts:
+        return sp.csr_matrix((n_targets, n_rows), dtype=np.float64)
+    return sp.csr_matrix(
+        (np.concatenate(data_parts), (np.concatenate(rows_parts), np.concatenate(cols_parts))),
+        shape=(n_targets, n_rows),
+    )
diff --git a/packages/microcosm-build/tests/engine_free/us/test_bakeoff_us_support_mix_tool.py b/packages/microcosm-build/tests/engine_free/us/test_bakeoff_us_support_mix_tool.py
new file mode 100644
index 0000000000..5b534fa0c5
--- /dev/null
+++ b/packages/microcosm-build/tests/engine_free/us/test_bakeoff_us_support_mix_tool.py
@@ -0,0 +1,345 @@
+"""Bookkeeping in the support-mix bake-off driver (tools/bakeoff_us_support_mix.py).
+
+Invariants:
+
+- SPM flag: a household is flagged exactly when one of its SPM units has no
+  member the pinned engine counts as an adult, and the rule refuses a person
+  table without household-role columns rather than falling back to age;
+- staleness: an arm's receipt depends only on the inputs and options its arm
+  reads, the concept digest moves with part contents (not just sizes) and
+  with the concept specs and measure list, a concept part is reusable only if
+  built from the current shards' source h5, concept specs and null-fill
+  summary, and saved weights are re-scored only for the same rows and inputs;
+- partcheck: a column fails when either side holds a nonfinite value or its
+  scaled difference exceeds the tolerance, and a check refuses an unknown or
+  empty part selection, a sample under one household and a negative or
+  nonfinite tolerance;
+- grid: ACS-only arms carry no years, so a grid on newer CPS years keeps them;
+- reporting: a report reads only current receipts of its own grid,
+  replicate tables cover only groups with more than one draw, a report
+  with missing or stale receipts is refused unless asked for, and
+  ``--allow-stale`` alone leaves stale receipts out without counting them as
+  missing.
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.util
+import json
+import pickle
+import sys
+from types import SimpleNamespace
+
+import numpy as np
+import pandas as pd
+import pytest
+import scipy.sparse as sp
+
+from test_support.paths import paths_for
+
+_TEST_PATHS = paths_for("microcosm-build")
+
+
+@pytest.fixture(scope="module")
+def tool():
+    path = _TEST_PATHS.repository / "tools" / "bakeoff_us_support_mix.py"
+    spec = importlib.util.spec_from_file_location("bakeoff_us_support_mix", path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec.loader is not None
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def _tables(people):
+    """``people``: (household, spm unit, age, head, spouse) per person."""
+    person = pd.DataFrame(people, columns=["person_household_id", "person_spm_unit_id", "age",
+                                           "is_household_head", "is_household_spouse"])
+    household = pd.DataFrame({"household_id": sorted(set(person["person_household_id"]))})
+    return {"household": household, "person": person}
+
+
+def test_spm_flag_marks_units_without_a_classified_adult(tool) -> None:
+    tables = _tables([
+        (1, 10, 40, True, False),    # adult
+        (2, 20, 16, True, False),    # 15-17 head counts as an adult
+        (3, 30, 16, False, False),   # 15-17 non-head, no adult in unit
+        (4, 40, 35, True, False),    # household 4: one unit with an adult...
+        (4, 41, 10, False, False),   # ...and one with only a child
+        (5, 50, 14, True, False),    # under 15 never counts, even as head
+    ])
+    flags = tool.spm_zero_adult_households(tables)
+    assert flags.tolist() == [False, False, True, True, True]
+
+
+def test_spm_flag_refuses_a_table_without_role_columns(tool) -> None:
+    tables = _tables([(1, 10, 16, True, False)])
+    tables["person"] = tables["person"].drop(columns=["is_household_head", "is_household_spouse"])
+    with pytest.raises(ValueError, match="neither"):
+        tool.spm_zero_adult_households(tables)
+
+
+def test_spm_flags_leaves_an_unchanged_row_index_alone(tool, tmp_path) -> None:
+    tables = _tables([(1, 10, 40, True, False), (2, 20, 16, False, False)])
+    shard_dir = tmp_path / "shards" / "acs"
+    shard_dir.mkdir(parents=True)
+    with open(shard_dir / "shard_000.pkl", "wb") as handle:
+        pickle.dump({"tables": tables}, handle)
+    rows = pd.DataFrame({"household_id": [1, 2], "shard": [0, 0], "group_quarters": [False, True],
+                         "spm_zero_adult_unit": [False, True]})
+    rows.to_parquet(tmp_path / "rows_acs.parquet", index=False)
+    def refuse(*_args, **_kwargs):
+        raise AssertionError("an unchanged row index was rewritten")
+
+    with pytest.MonkeyPatch.context() as patch:
+        patch.setattr(pd.DataFrame, "to_parquet", refuse)
+        tool.do_spm_flags(argparse.Namespace(work=tmp_path))
+    receipt = json.loads((tmp_path / "spm_flags.json").read_text())
+    assert receipt["spm_zero_adult_households"] == 1 == receipt["spm_zero_adult_in_group_quarters"]
+    rows.assign(spm_zero_adult_unit=False).to_parquet(tmp_path / "rows_acs.parquet", index=False)
+    tool.do_spm_flags(argparse.Namespace(work=tmp_path))
+    assert pd.read_parquet(tmp_path / "rows_acs.parquet")["spm_zero_adult_unit"].tolist() == [False, True]
+
+
+@pytest.mark.parametrize("newest", [2024, 2025])
+def test_arm_inputs_cover_exactly_what_each_arm_reads(tool, newest) -> None:
+    for arm in tool.arm_grid(True, newest):
+        keys = set(tool.arm_input_keys(arm))
+        assert {"targets", "truth", "epochs"} <= keys
+        assert ({"rows_cps", "concepts_cps"} <= keys) == bool(arm.cps_income_years)
+        assert ({"rows_cps", "concepts_cps"} & keys == set()) == (not arm.cps_income_years)
+        assert ({"rows_acs", "concepts_acs"} <= keys) == (arm.budget is not None)
+        assert tool.arm_inputs(dict.fromkeys(keys, "d"), arm)["exclude_gq"] is False
+
+
+def test_acs_only_arms_survive_a_newer_cps_grid(tool) -> None:
+    assert tool.year_sets(2025) == ((2025,), (2024, 2025), (2023, 2024, 2025))
+    old = {a.label for a in tool.arm_grid(True, 2024)}
+    new = {a.label for a in tool.arm_grid(True, 2025)}
+    assert len(old) == len(new) == 30
+    assert old & new == {label for label in old if ".cps0." in label}
+
+
+def test_inputs_current_compares_only_the_arms_inputs(tool) -> None:
+    current = {"targets": "t", "truth": "u", "epochs": 1500}
+    assert tool.inputs_current({**current, "rows_cps": "old"}, current)
+    assert not tool.inputs_current({**current, "targets": "x"}, current)
+    assert not tool.inputs_current({"targets": "t", "truth": "u"}, current)
+    assert not tool.inputs_current(None, current)
+    # Arm options: a receipt that predates recording them was solved with the default.
+    assert tool.inputs_current(current, {**current, "exclude_gq": False})
+    assert not tool.inputs_current(current, {**current, "exclude_gq": True})
+    assert not tool.inputs_current({**current, "exclude_gq": True}, {**current, "exclude_gq": False})
+
+
+def test_rescore_needs_the_same_rows_and_inputs(tool) -> None:
+    inputs = {"targets": "t", "epochs": 1500}
+    previous = {"rows_digest": "r", "inputs": inputs}
+    assert tool.rescore_refusal(previous, inputs, "r") is None
+    assert "rows digest" in tool.rescore_refusal({"inputs": inputs}, inputs, "r")
+    assert "rows digest" in tool.rescore_refusal(previous, inputs, "other")
+    assert "inputs" in tool.rescore_refusal(previous, {**inputs, "targets": "new"}, "r")
+
+
+def _write_part(directory, name, values, receipt=None, rows=None):
+    directory.mkdir(parents=True, exist_ok=True)
+    sp.save_npz(directory / f"{name}.npz", sp.csr_matrix(np.asarray(values, dtype=np.float32)))
+    if rows is not None:
+        np.save(directory / f"{name}.rows.npy", np.asarray(rows, dtype=np.int64))
+    if receipt is not None:
+        (directory / f"{name}.json").write_text(json.dumps(receipt))
+
+
+def test_concept_digest_moves_with_contents_of_the_same_size(tool, tmp_path) -> None:
+    directory = tmp_path / "concepts" / "acs"
+    _write_part(directory, "shard_000", [[1.0, 0.0], [0.0, 2.0]], rows=[0, 1])
+    first = tool._concept_digest(tmp_path, "acs")
+    size = (directory / "shard_000.npz").stat().st_size
+    _write_part(directory, "shard_000", [[3.0, 0.0], [0.0, 4.0]], rows=[0, 1])
+    assert (directory / "shard_000.npz").stat().st_size == size
+    second = tool._concept_digest(tmp_path, "acs")
+    assert second != first
+    _write_part(directory, "shard_000", [[3.0, 0.0], [0.0, 4.0]], rows=[1, 0])
+    assert tool._concept_digest(tmp_path, "acs") != second
+
+
+def test_concept_digest_moves_with_what_the_columns_mean(tool, tmp_path) -> None:
+    """A recompile that leaves the parts in place (new report specs, say)
+    still makes a receipt from before it stale (review r5b, finding 1)."""
+    args = _report_work(tmp_path)
+    directory = tmp_path / "concepts" / "acs"
+    _write_part(directory, "shard_000", [[1.0, 0.0]], rows=[0])
+    specs, measures = tmp_path / "concept_specs.pkl", directory / "measures.json"
+    specs.write_bytes(pickle.dumps([SimpleNamespace(measure="bakeoff_concept_00000")]))
+    measures.write_text(json.dumps(["bakeoff_concept_00000", "state_income_tax"]))
+    arm = tool.parse_arm("b300k.cps0.acs100.s0")
+
+    def inputs():
+        return tool.arm_inputs(tool.input_digests(tmp_path, args.truth, args.epochs), arm)
+
+    solved = inputs()
+    assert tool.inputs_current(solved, inputs())
+    specs.write_bytes(pickle.dumps([SimpleNamespace(measure="bakeoff_concept_00000", age_limit=17)]))
+    after_specs = inputs()
+    assert not tool.inputs_current(solved, after_specs)
+    measures.write_text(json.dumps(["state_income_tax", "bakeoff_concept_00000"]))
+    assert not tool.inputs_current(after_specs, inputs())
+
+
+def test_stale_parts_are_those_not_built_from_the_current_provenance(tool, tmp_path) -> None:
+    directory = tmp_path / "parts"
+    current = {"source_h5_sha256": "new", "concept_specs_sha256": "specs", "summary_sha256": None}
+    _write_part(directory, "shard_000", [[1.0]], receipt=current)
+    _write_part(directory, "shard_001", [[1.0]], receipt={**current, "source_h5_sha256": "old"})
+    _write_part(directory, "shard_002", [[1.0]], receipt={**current, "concept_specs_sha256": "older"})
+    _write_part(directory, "shard_003", [[1.0]], receipt={**current, "summary_sha256": "filled"})
+    _write_part(directory, "shard_004", [[1.0]], receipt={"source_h5_sha256": "new"})
+    _write_part(directory, "shard_005", [[1.0]])
+    stale = [p.name for p in tool.stale_concept_parts(directory, current)]
+    assert stale == [f"shard_00{i}.npz" for i in range(1, 6)]
+    # The concept store checks the source and specs only.
+    without_summary = {k: v for k, v in current.items() if k != "summary_sha256"}
+    assert "shard_003.npz" not in [p.name for p in tool.stale_concept_parts(directory, without_summary)]
+
+
+def _materialize_args(work, replace_stale):
+    return argparse.Namespace(work=work, source="cps", replace_stale=replace_stale, rank_range=None,
+                              only=None, summary=None, batch=5_000, min_available_gb=0.0)
+
+
+def _materialize_work(tmp_path):
+    (tmp_path / "concept_specs.pkl").write_bytes(pickle.dumps([]))
+    (tmp_path / "shards" / "cps").mkdir(parents=True)
+    (tmp_path / "shards" / "cps" / "shard.json").write_text(json.dumps({"h5_sha256": "new"}))
+    pd.DataFrame({"shard": pd.Series([], dtype=np.int64)}).to_parquet(tmp_path / "rows_cps.parquet")
+    return tmp_path / "concepts" / "cps"
+
+
+def test_materialize_refuses_then_replaces_stale_parts(tool, tmp_path) -> None:
+    concepts = _materialize_work(tmp_path)
+    current = tool.part_provenance(tmp_path, "cps", None)
+    _write_part(concepts, "shard_000", [[1.0]], receipt={**current, "source_h5_sha256": "old"}, rows=[0])
+    _write_part(concepts, "shard_001", [[1.0]], receipt=current, rows=[1])
+    with pytest.raises(SystemExit, match="replace-stale"):
+        tool.do_materialize(_materialize_args(tmp_path, replace_stale=False))
+    assert (concepts / "shard_000.npz").exists()
+    tool.do_materialize(_materialize_args(tmp_path, replace_stale=True))
+    assert not list(concepts.glob("shard_000*"))
+    assert (concepts / "shard_001.npz").exists()
+
+
+def test_a_recompile_makes_every_part_stale(tool, tmp_path) -> None:
+    concepts = _materialize_work(tmp_path)
+    _write_part(concepts, "shard_000", [[1.0]], receipt=tool.part_provenance(tmp_path, "cps", None), rows=[0])
+    assert not tool.stale_concept_parts(concepts, tool.part_provenance(tmp_path, "cps", None))
+    (tmp_path / "concept_specs.pkl").write_bytes(pickle.dumps([SimpleNamespace(measure="bakeoff_concept_00000")]))
+    assert tool.stale_concept_parts(concepts, tool.part_provenance(tmp_path, "cps", None))
+    with pytest.raises(SystemExit, match="replace-stale"):
+        tool.do_materialize(_materialize_args(tmp_path, replace_stale=False))
+
+
+def test_partcheck_fails_nonfinite_and_out_of_tolerance_columns(tool) -> None:
+    fresh = np.array([[1.0, 2.0, np.nan, np.inf, 1.0, 3.0],
+                      [1.0, 2.0, 1.0, np.inf, 1.0, 3.0]])
+    stored = np.array([[1.0, 2.0, np.nan, np.inf, 1.5, 3.0],
+                       [1.0, 2.0 + 1e-9, 1.0, np.inf, 1.0, np.nan]])
+    scaled, bad = tool.partcheck_columns(fresh, stored, 1e-5)
+    assert bad.tolist() == [2, 3, 4, 5]
+    assert scaled[0] == 0.0
+    assert scaled[1] == pytest.approx(5e-10)
+    assert scaled[4] == pytest.approx(0.5)
+
+
+def test_a_nonfinite_value_fails_its_column_at_any_tolerance(tool) -> None:
+    """Exhaustive over a 2 x 3 sample: one nonfinite value on either side
+    fails exactly its own column, whatever the tolerance."""
+    base = np.arange(6.0).reshape(2, 3)
+    assert tool.partcheck_columns(base, base.copy(), 0.0)[1].size == 0
+    for side in ("fresh", "stored"):
+        for row in range(2):
+            for col in range(3):
+                for value in (np.nan, np.inf, -np.inf):
+                    for tolerance in (0.0, 1e-5, 1e300):
+                        fresh, stored = base.copy(), base.copy()
+                        (fresh if side == "fresh" else stored)[row, col] = value
+                        assert tool.partcheck_columns(fresh, stored, tolerance)[1].tolist() == [col]
+
+
+def test_partcheck_refuses_unknown_and_empty_part_selections(tool, tmp_path) -> None:
+    parts = [tmp_path / "shard_000.npz", tmp_path / "shard_001.npz"]
+    assert tool.select_parts(parts, None) == parts
+    assert tool.select_parts(parts, " shard_001 ,") == parts[1:]
+    with pytest.raises(SystemExit, match="shard_007"):
+        tool.select_parts(parts, "shard_000,shard_007")
+    with pytest.raises(SystemExit, match="no stored parts"):
+        tool.select_parts(parts, ",")
+    with pytest.raises(SystemExit, match="no stored parts"):
+        tool.select_parts([], None)
+
+
+@pytest.mark.parametrize(("per_part", "tolerance"), [
+    (0, 1e-5), (200, -1e-5), (200, float("nan")), (200, float("inf")),
+])
+def test_partcheck_refuses_a_bad_sample_size_or_tolerance(tool, tmp_path, per_part, tolerance) -> None:
+    args = argparse.Namespace(work=tmp_path, source="cps", shards=None, parts=None, per_part=per_part,
+                              tolerance=tolerance, summary=None, batch=5_000, min_available_gb=0.0)
+    with pytest.raises(SystemExit, match="per-part|tolerance"):
+        tool.do_partcheck(args)
+
+
+def test_replicate_tables_cover_only_groups_with_several_draws(tool) -> None:
+    table = pd.DataFrame({
+        "product": ["national"] * 5,
+        "arm": ["b300k.cps2024.acs100.s0", "b300k.cps2024.acs100.s1", "b300k.cps2024.acs100.s2",
+                "b300k.cps2023-2024.acs100.s0", "b600k.cps2024.acs100.s0"],
+        "cps_years": [1, 1, 1, 2, 1], "acs_fill_share": [1.0] * 5, "seed": [0, 1, 2, 0, 0],
+        "x.state": [0.1, 0.2, 0.3, 0.9, 0.9],
+    })
+    spread, mean = tool.replicate_summary(table)
+    assert list(mean.index) == [("national", 1)] == list(spread.index)
+    assert mean.loc[("national", 1), "x.state"] == pytest.approx(0.2)
+    assert spread.loc[("national", 1), "x.state"] == pytest.approx(0.1)
+
+
+def _report_work(tmp_path):
+    for name in ("targets.parquet", "rows_cps.parquet", "rows_acs.parquet", "truth.parquet"):
+        pd.DataFrame({"x": [1]}).to_parquet(tmp_path / name)
+    (tmp_path / "arms" / "national").mkdir(parents=True)
+    return argparse.Namespace(work=tmp_path, out=tmp_path / "report", newest_year=2024, truth=tmp_path / "truth.parquet",
+                              epochs=1500, allow_missing=False, allow_stale=False)
+
+
+def test_report_refuses_missing_receipts(tool, tmp_path) -> None:
+    args = _report_work(tmp_path)
+    with pytest.raises(SystemExit, match="60 expected receipts missing"):
+        tool.do_report(args)
+
+
+def test_report_skips_other_grids_and_refuses_stale_receipts(tool, tmp_path) -> None:
+    args = _report_work(tmp_path)
+    arms = tmp_path / "arms" / "national"
+    (arms / "b300k.cps2025.acs100.s0.json").write_text(json.dumps(
+        {"arm": "b300k.cps2025.acs100.s0", "product": "national", "inputs": {}}))
+    with pytest.raises(SystemExit, match="60 expected receipts missing"):
+        tool.do_report(args)  # the 2025-grid receipt is skipped, not counted or read
+    (arms / "b300k.cps0.acs100.s0.json").write_text(json.dumps(
+        {"arm": "b300k.cps0.acs100.s0", "product": "national", "inputs": {"targets": "old"}}))
+    with pytest.raises(SystemExit, match="1 receipts are stale"):
+        tool.do_report(args)
+
+
+def test_report_with_allow_stale_alone_leaves_stale_receipts_out(tool, tmp_path) -> None:
+    """Review r5b, finding 4: ``--allow-stale`` must not also need
+    ``--allow-missing`` for receipts that exist but are stale."""
+    args = _report_work(tmp_path)
+    (tmp_path / "arms" / "local").mkdir()
+    for product in ("national", "local"):
+        for arm in tool.arm_grid(True, 2024):
+            (tmp_path / "arms" / product / f"{arm.label}.json").write_text(json.dumps(
+                {"arm": arm.label, "product": product, "inputs": {"targets": "old"}}))
+    with pytest.raises(SystemExit, match="60 receipts are stale"):
+        tool.do_report(args)
+    args.allow_stale = True
+    tool.do_report(args)
+    assert (args.out / "arms.csv").exists()
diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_spine_blindness.py b/packages/microcosm-build/tests/engine_free/us/test_us_spine_blindness.py
index 563fb81701..460dcf90f7 100644
--- a/packages/microcosm-build/tests/engine_free/us/test_us_spine_blindness.py
+++ b/packages/microcosm-build/tests/engine_free/us/test_us_spine_blindness.py
@@ -419,6 +419,9 @@
         # column; the release tool passes the people who may lack a decision.
         "retired_stored_inputs.py",
         "stacked_spine.py",  # Provenance owner (#578 revision); see owners list.
+        # Bake-off arm accounting, split mirror and ESS; evaluates support mixes,
+        # never transforms a population.
+        "support_mix.py",
         "support_provenance.py",
         "take_up.py",
         "take_up_contract.py",
diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_support_mix.py b/packages/microcosm-build/tests/engine_free/us/test_us_support_mix.py
new file mode 100644
index 0000000000..67ba6680cd
--- /dev/null
+++ b/packages/microcosm-build/tests/engine_free/us/test_us_support_mix.py
@@ -0,0 +1,294 @@
+"""Properties of the support-mix bake-off primitives.
+
+Invariants checked for every drawn input:
+
+- budget accounting: an arm's CPS, ACS and clone source households sum to
+  its budget, and infeasible arms are refused rather than truncated;
+- nesting: a smaller ACS selection is a prefix of a larger one, and the order
+  depends only on the key set, not its input order;
+- clone allocation: copies sum to the requested clones and differ by at most
+  one across households;
+- mass: starting weights are positive, sum to the requested total, give each
+  CPS income year an equal share, and split CPS and ACS mass in proportion to
+  source households; a row's copies sum back to its per-year weight; the
+  weights are linear in the requested total;
+- ESS: distinct-unit ESS never exceeds the row ESS or the number of distinct
+  units, and equals the row ESS when every unit is distinct;
+- assembly (differential): the sparse target matrix equals a naive dense
+  construction, including cells that carry several targets;
+- split: the role is a pure function of the group key, district children
+  share their parent's role, vintage tokens never change a role, and the
+  hash matches the pending release primitive byte for byte.
+"""
+
+from __future__ import annotations
+
+import hashlib
+
+import numpy as np
+import pytest
+
+hypothesis = pytest.importorskip("hypothesis")
+from hypothesis import given, settings  # noqa: E402
+from hypothesis import strategies as st  # noqa: E402
+
+from microcosm.build.us_runtime.support_mix import (  # noqa: E402
+    TARGET_SPLIT_HOLDOUT_SALT,
+    TARGET_SPLIT_SEALED_SALT,
+    ArmCounts,
+    SupportMixArm,
+    acs_selection_order,
+    arm_initial_weights,
+    assemble_target_matrix,
+    clone_copies,
+    distinct_unit_ess,
+    hash_uniform,
+    kish_ess,
+    plan_arm_counts,
+    target_split_group_key,
+    target_split_role,
+)
+
+YEARS = {2022: 56_839, 2023: 56_251, 2024: 55_762}
+
+
+def test_hash_uniform_matches_the_release_primitive() -> None:
+    digest = hashlib.sha256(b"salt\x1fkey").digest()
+    assert hash_uniform("key", salt="salt") == int.from_bytes(digest[:8], "big") / 2**64
+    with pytest.raises(ValueError):
+        hash_uniform("", salt="salt")
+    with pytest.raises(ValueError):
+        hash_uniform("key", salt="")
+
+
+#: (key, salt, draw) computed 2026-09-30 by the holdout port branch's own
+#: ``microcosm.build.holdout.hash_holdout_uniform`` (us-target-roles-holdout
+#: working copy), so the mirror is checked against that implementation, not
+#: only against a re-typed formula.
+PORT_BRANCH_FIXTURES = (
+    ("irs_soi.congressional_district_2022.all_returns.wv_total.net_capital_gains_returns",
+     "microcosm.us.target_split.v1.sealed", 0.568527776442365),
+    ("irs_soi.congressional_district_2022.all_returns.wv_total.net_capital_gains_returns",
+     "microcosm.us.target_split.v1.holdout", 0.2332024720189108),
+    ("census_pep.national_resident_population_age.20_to_24.population",
+     "microcosm.us.target_split.v1.sealed", 0.6698038027150819),
+    ("census_pep.national_resident_population_age.20_to_24.population",
+     "microcosm.us.target_split.v1.holdout", 0.888848284264564),
+    ("k0", "microcosm.us.target_split.v1.sealed", 0.13405297471660024),
+    ("k0", "microcosm.us.target_split.v1.holdout", 0.994075647032073),
+    ("usda_snap.state.06.households", "microcosm.us.target_split.v1.sealed", 0.20260546634959004),
+    ("usda_snap.state.06.households", "microcosm.us.target_split.v1.holdout", 0.9762431428888598),
+)
+
+
+@pytest.mark.parametrize(("key", "salt", "draw"), PORT_BRANCH_FIXTURES)
+def test_hash_uniform_matches_the_port_branch_fixtures(key: str, salt: str, draw: float) -> None:
+    assert hash_uniform(key, salt=salt) == draw
+
+
+@given(st.text(min_size=1, max_size=40), st.text(min_size=1, max_size=20))
+def test_hash_uniform_is_a_unit_interval_draw(key: str, salt: str) -> None:
+    u = hash_uniform(key, salt=salt)
+    assert 0.0 <= u < 1.0
+    assert u == hash_uniform(key, salt=salt)
+
+
+@given(
+    budget=st.integers(min_value=1, max_value=2_000_000),
+    years=st.sets(st.sampled_from(sorted(YEARS)), max_size=3),
+    share=st.floats(min_value=0.0, max_value=1.0),
+)
+def test_arm_counts_sum_to_the_budget_or_refuse(budget, years, share) -> None:
+    arm = SupportMixArm(budget=budget, cps_income_years=tuple(years), acs_fill_share=share)
+    cps = sum(YEARS[year] for year in years)
+    try:
+        counts = plan_arm_counts(arm, cps_households_by_year=YEARS, acs_households=1_531_614)
+    except ValueError:
+        fill = budget - cps
+        acs = round(fill * share) if fill >= 0 else 0
+        assert fill < 0 or acs > 1_531_614 or (fill - acs > 0 and cps == 0)
+        return
+    assert counts.total == budget
+    assert counts.cps == cps
+    assert min(counts.acs, counts.clones) >= 0
+
+
+def test_natural_arm_has_no_fill() -> None:
+    arm = SupportMixArm(budget=None, cps_income_years=(2024,))
+    counts = plan_arm_counts(arm, cps_households_by_year=YEARS, acs_households=10)
+    assert counts == ArmCounts(cps=55_762, acs=0, clones=0)
+    assert arm.label == "natural.cps2024.acs100.s0"
+
+
+@given(
+    keys=st.lists(st.text(min_size=1, max_size=12), min_size=1, max_size=60, unique=True),
+    data=st.data(),
+)
+def test_acs_selection_is_nested_and_order_free(keys, data) -> None:
+    order = acs_selection_order(keys, salt="s")
+    assert sorted(order.tolist()) == list(range(len(keys)))
+    small = data.draw(st.integers(min_value=0, max_value=len(keys)))
+    large = data.draw(st.integers(min_value=small, max_value=len(keys)))
+    chosen_small = {keys[i] for i in order[:small]}
+    chosen_large = {keys[i] for i in order[:large]}
+    assert chosen_small <= chosen_large
+    shuffled = list(reversed(keys))
+    reorder = acs_selection_order(shuffled, salt="s")
+    assert [shuffled[i] for i in reorder] == [keys[i] for i in order]
+
+
+@given(
+    keys=st.lists(st.text(min_size=1, max_size=8), min_size=1, max_size=50, unique=True),
+    n_clones=st.integers(min_value=0, max_value=500),
+)
+def test_clone_copies_sum_and_balance(keys, n_clones) -> None:
+    copies = clone_copies(keys, n_clones, salt="c")
+    assert int(copies.sum()) == n_clones
+    assert copies.max() - copies.min() <= 1
+
+
+positive = st.floats(min_value=0.01, max_value=1e4, allow_nan=False)
+
+
+@settings(max_examples=150)
+@given(
+    cps=st.lists(st.tuples(positive, st.sampled_from([2022, 2023, 2024]), st.integers(0, 5)),
+                 min_size=1, max_size=40),
+    acs=st.lists(positive, min_size=0, max_size=40),
+    clones=st.integers(min_value=0, max_value=100),
+    mass=st.floats(min_value=1.0, max_value=1e9),
+)
+def test_initial_weights_conserve_and_split_mass(cps, acs, clones, mass) -> None:
+    weights = np.array([w for w, _, _ in cps])
+    years = np.array([y for _, y, _ in cps])
+    copies = np.array([k for _, _, k in cps])
+    acs_w = np.array(acs, dtype=np.float64)
+    counts = ArmCounts(cps=len(cps), acs=len(acs), clones=clones)
+    cps_out, acs_out = arm_initial_weights(
+        cps_design_weights=weights, cps_income_year=years, cps_copies=copies,
+        acs_design_weights=acs_w, counts=counts, total_mass=mass,
+    )
+    expanded = cps_out * (1 + copies)
+    assert (cps_out > 0).all() and (acs_out > 0).all()
+    total = expanded.sum() + acs_out.sum()
+    assert np.isclose(total, mass, rtol=1e-9)
+    cps_share = (counts.cps + counts.clones) / counts.total
+    assert np.isclose(expanded.sum(), cps_share * mass, rtol=1e-9)
+    per_year = [expanded[years == y].sum() for y in np.unique(years)]
+    assert np.allclose(per_year, per_year[0], rtol=1e-9)
+    # A row's copies sum back to its per-year weight, which keeps the
+    # within-year design-weight shape; ACS rows keep theirs.
+    for y in np.unique(years):
+        in_year = years == y
+        assert np.allclose(expanded[in_year], weights[in_year] * (per_year[0] / weights[in_year].sum()),
+                           rtol=1e-9)
+    if len(acs):
+        assert np.allclose(acs_out, acs_w * (acs_out.sum() / acs_w.sum()), rtol=1e-9)
+    # Linear in the mass, so the driver's population mass repair (a common
+    # rescale) makes the repaired weights independent of it.
+    cps_k, acs_k = arm_initial_weights(
+        cps_design_weights=weights, cps_income_year=years, cps_copies=copies,
+        acs_design_weights=acs_w, counts=counts, total_mass=2.5 * mass,
+    )
+    assert np.allclose(cps_k, 2.5 * cps_out, rtol=1e-12) and np.allclose(acs_k, 2.5 * acs_out, rtol=1e-12)
+
+
+def test_initial_weights_refuse_bad_inputs() -> None:
+    counts = ArmCounts(cps=1, acs=1, clones=0)
+    ok = dict(cps_design_weights=np.array([1.0]), cps_income_year=np.array([2024]),
+              cps_copies=np.array([0]), acs_design_weights=np.array([1.0]),
+              counts=counts, total_mass=10.0)
+    arm_initial_weights(**ok)
+    for key, value in (("cps_design_weights", np.array([0.0])),
+                       ("acs_design_weights", np.array([np.nan])),
+                       ("total_mass", -1.0), ("cps_copies", np.array([-1]))):
+        with pytest.raises(ValueError):
+            arm_initial_weights(**{**ok, key: value})
+
+
+@given(
+    rows=st.lists(st.tuples(st.floats(min_value=0.0, max_value=1e6), st.integers(0, 15)),
+                  min_size=1, max_size=80)
+)
+def test_distinct_unit_ess_bounds(rows) -> None:
+    w = np.array([r[0] for r in rows])
+    units = np.array([r[1] for r in rows])
+    if w.sum() == 0:
+        return
+    unit_ess = distinct_unit_ess(w, units)
+    assert unit_ess <= kish_ess(w) * (1 + 1e-9)
+    assert unit_ess <= len(np.unique(units[w > 0])) * (1 + 1e-9)
+    assert np.isclose(distinct_unit_ess(w, np.arange(len(w))), kish_ess(w))
+
+
+def test_group_key_normalization() -> None:
+    assert target_split_group_key("irs_soi.state.ty2023.agi.06") == "irs_soi.state.agi.06"
+    assert target_split_group_key("a.month2024_7.b") == "a.b"
+    # District GEOIDs are 4-digit tokens and survive.
+    assert target_split_group_key("irs_soi.cd.0601.v2024") == "irs_soi.cd.0601"
+    assert target_split_group_key(
+        "x.current_cd.0601.0123456789abcdef"
+    ) == "x.current_cd.0601"
+    # A district child takes its state parent's key.
+    assert target_split_group_key("child.0601", "parent.06.ty2022") == "parent.06"
+
+
+@given(st.lists(st.sampled_from(["ty2021", "cy2024", "v2024", "fy2023", "oep2025"]), max_size=3))
+def test_vintage_tokens_never_change_a_role(vintages) -> None:
+    base = "irs_soi.state.agi.band3.06"
+    name = ".".join([*base.split(".")[:2], *vintages, *base.split(".")[2:]])
+    assert target_split_group_key(name) == base
+    assert target_split_role(target_split_group_key(name)) == target_split_role(base)
+
+
+def test_role_rates_are_near_the_declared_fractions() -> None:
+    roles = [target_split_role(f"k{i}") for i in range(20_000)]
+    sealed = roles.count("sealed") / len(roles)
+    holdout = roles.count("holdout") / len(roles)
+    assert abs(sealed - 0.05) < 0.01
+    assert abs(holdout - 0.095) < 0.01
+    # Sealed is drawn first on its own salt.
+    for i in range(200):
+        key = f"k{i}"
+        if hash_uniform(key, salt=TARGET_SPLIT_SEALED_SALT) < 0.05:
+            assert target_split_role(key) == "sealed"
+        elif hash_uniform(key, salt=TARGET_SPLIT_HOLDOUT_SALT) < 0.10:
+            assert target_split_role(key) == "holdout"
+
+
+def test_arm_validation() -> None:
+    with pytest.raises(ValueError):
+        SupportMixArm(budget=0, cps_income_years=(2024,))
+    with pytest.raises(ValueError):
+        SupportMixArm(budget=10, cps_income_years=(2024, 2024))
+    with pytest.raises(ValueError):
+        SupportMixArm(budget=10, cps_income_years=(2024,), acs_fill_share=1.5)
+    arm = SupportMixArm(budget=600_000, cps_income_years=(2024, 2022), acs_fill_share=0.5, seed=2)
+    assert arm.cps_income_years == (2022, 2024)
+    assert arm.label == "b600k.cps2022-2024.acs050.s2"
+
+
+@settings(max_examples=120, deadline=None)
+@given(data=st.data())
+def test_assemble_matches_naive_dense_construction(data) -> None:
+    import scipy.sparse as sp
+
+    n_rows = data.draw(st.integers(1, 30))
+    n_concepts = data.draw(st.integers(1, 6))
+    dense = np.array(data.draw(st.lists(
+        st.lists(st.sampled_from([0.0, 0.0, 1.5, -2.0, 7.0]), min_size=n_concepts, max_size=n_concepts),
+        min_size=n_rows, max_size=n_rows)))
+    state = np.array(data.draw(st.lists(st.integers(0, 3), min_size=n_rows, max_size=n_rows)))
+    cd = np.array(data.draw(st.lists(st.integers(0, 5), min_size=n_rows, max_size=n_rows)))
+    targets = data.draw(st.lists(st.tuples(
+        st.integers(0, n_concepts - 1), st.sampled_from(["national", "state", "cd"]), st.integers(0, 5)),
+        min_size=1, max_size=25))
+    concept = np.array([t[0] for t in targets])
+    level = [t[1] for t in targets]
+    geo = np.array([0 if t[1] == "national" else (t[2] % 4 if t[1] == "state" else t[2]) for t in targets])
+    got = assemble_target_matrix(sp.csr_matrix(dense), {"state": state, "cd": cd}, concept, level, geo).toarray()
+    expected = np.zeros((len(targets), n_rows))
+    for t, (c, lv, g) in enumerate(zip(concept, level, geo, strict=True)):
+        member = np.ones(n_rows, bool) if lv == "national" else ((state if lv == "state" else cd) == g)
+        expected[t] = dense[:, c] * member
+    np.testing.assert_array_equal(got, expected)
diff --git a/tools/bakeoff_us_support_mix.py b/tools/bakeoff_us_support_mix.py
new file mode 100644
index 0000000000..118d83c475
--- /dev/null
+++ b/tools/bakeoff_us_support_mix.py
@@ -0,0 +1,1983 @@
+#!/usr/bin/env python
+"""Bake-off: CPS ASEC years vs ACS rows in the US calibration support.
+
+At fixed source-household budgets, each arm keeps every household of N CPS
+income years and fills the rest with ACS households, CPS location clones, or
+a mix. Every arm is calibrated with the production solver and scored on
+targets it never saw. See docs/us-support-mix-bakeoff.md for the design,
+results and caveats.
+
+The expensive part, running PolicyEngine-US, happens once per household, not
+once per arm. ``compile`` groups the pinned target registry into concepts:
+a concept is a target with its geography stripped, and every state or
+district target is that concept's household value times an indicator of the
+household's own state or district (the release materializer builds its
+columns exactly this way; ``diffcheck`` verifies it on real rows).
+``materialize`` runs the release tool's own ``_materialize_target_frame`` on
+household shards for the concept specs. ``arm`` then selects rows, assembles
+the sparse target matrix, calls the production ``_optimize`` and scores.
+
+Subcommands, in order: compile, shard, spm-flags, materialize, diffcheck,
+partcheck, truth, arm (or run-grid for every arm), report. Every subcommand writes a JSON
+receipt with wall time and peak RSS.
+"""
+
+from __future__ import annotations
+
+import argparse
+import dataclasses
+import gc
+import hashlib
+import importlib.util
+import json
+import math
+import os
+import pickle
+import resource
+import sys
+import time
+from collections import Counter
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import scipy.sparse as sp
+
+TOOLS = Path(__file__).resolve().parent
+sys.path.insert(0, str(TOOLS))
+
+from microcosm.build.us_runtime.support_mix import (  # noqa: E402
+    SupportMixArm,
+    acs_selection_order,
+    arm_initial_weights,
+    assemble_target_matrix,
+    clone_copies,
+    distinct_unit_ess,
+    hash_uniform,
+    kish_ess,
+    plan_arm_counts,
+    target_split_group_key,
+    target_split_role,
+)
+
+PERIOD = 2024
+FEED_SHA256 = "b85437390021777e746f507c5890305496baf5fc7f2c78ba08ddb090f4839801"
+ENTITIES = ("person", "household", "tax_unit", "spm_unit", "family", "marital_unit")
+GROUP_ID = {
+    "tax_unit": ("tax_unit_id", "person_tax_unit_id"),
+    "spm_unit": ("spm_unit_id", "person_spm_unit_id"),
+    "family": ("family_id", "person_family_id"),
+    "marital_unit": ("marital_unit_id", "person_marital_unit_id"),
+}
+ACS_SELECTION_SALT = "microcosm.us.support_mix_bakeoff.v1.acs_order"
+CLONE_SALT = "microcosm.us.support_mix_bakeoff.v1.clones"
+CLONE_GEO_SALT = "microcosm.us.support_mix_bakeoff.v1.clone_geography"
+REPORT_SPECS = {
+    "report.spm_poor_persons": {"base_variable": "person_in_poverty", "measure_mode": "sum"},
+    "report.spm_poor_children": {
+        "base_variable": "person_in_poverty",
+        "measure_mode": "sum",
+        "age_lower_bound": "0",
+        "age_upper_bound": "18",
+    },
+}
+
+# ------------------------------------------------------------------ utilities
+
+
+def log(message: str) -> None:
+    stamp = time.strftime("%Y-%m-%d %H:%M:%S")
+    print(f"{stamp} {message}", flush=True)
+
+
+def peak_rss_gb() -> float:
+    # macOS reports ru_maxrss in bytes; Linux in kilobytes.
+    raw = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
+    return raw / 1e9 if sys.platform == "darwin" else raw * 1024 / 1e9
+
+
+def sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with open(path, "rb") as handle:
+        for block in iter(lambda: handle.read(1 << 22), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def write_json(path: Path, payload: object) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    tmp = path.with_suffix(f"{path.suffix}.{os.getpid()}.tmp")
+    tmp.write_text(json.dumps(payload, indent=1, sort_keys=True, default=str))
+    tmp.replace(path)
+
+
+#: Touch this file to pause every bake-off loop between units of work (a shard
+#: or an arm) — e.g. while a release stage needs the machine's memory.
+PAUSE_FILE = Path("/Users/maxghenis/PolicyEngine/_bakeoff-support-mix/PAUSE")
+
+
+def wait_while_paused() -> None:
+    while PAUSE_FILE.exists():
+        log(f"paused: {PAUSE_FILE} exists")
+        time.sleep(120)
+
+
+def wait_for_memory(min_available_gb: float) -> None:
+    """Block until the host has ``min_available_gb`` free (a shared laptop)."""
+    import psutil
+
+    wait_while_paused()
+    while psutil.virtual_memory().available / 2**30 < min_available_gb:
+        log(
+            f"waiting for memory: {psutil.virtual_memory().available / 2**30:.1f} GiB "
+            f"available < {min_available_gb} GiB"
+        )
+        time.sleep(120)
+
+
+def release_tool():
+    name = "build_us_fiscal_refresh_release"
+    if name in sys.modules:
+        return sys.modules[name]
+    spec = importlib.util.spec_from_file_location(name, TOOLS / f"{name}.py")
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def acs_local_tool():
+    name = "build_us_acs_local_release"
+    if name in sys.modules:
+        return sys.modules[name]
+    spec = importlib.util.spec_from_file_location(name, TOOLS / f"{name}.py")
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+# ------------------------------------------------------------------ compile
+
+INCOME_SOURCES = {
+    "adjusted_gross_income", "employment_income", "self_employment_income",
+    "capital_gains", "capital_gains_gross", "capital_gains_losses",
+    "qualified_dividend_income", "ordinary_dividends", "taxable_interest_income",
+    "tax_exempt_interest_income", "rent_and_royalty_net_income",
+    "rent_and_royalty_net_losses", "partnership_s_corp_income",
+    "partnership_s_corp_losses", "estate_income", "estate_losses",
+    "taxable_pension_income", "total_pension_income", "taxable_ira_distributions",
+    "taxable_social_security", "total_social_security", "unemployment_compensation",
+    "business_net_profits", "business_net_losses", "non_sch_d_capital_gains",
+    "count",
+}
+
+
+def dimension_of(family: str, metadata: dict) -> tuple[str, str]:
+    """(dimension group, dimension) for one registry target."""
+    if family in {"usda_snap", "cms_medicaid", "cms_aca", "ssa", "hhs_acf_tanf",
+                  "hhs_acf_liheap", "cms_medicare"}:
+        return "cps_native", "program_receipt"
+    if family in {"state_income_tax", "jct"}:
+        return "cps_native", "tax_items"
+    if family == "census_population":
+        return "acs_native", "demographics_pep"
+    if family in {"bea", "cbo"}:
+        return "cps_native", "income_components"
+    if family == "federal_reserve":
+        return "other", "wealth"
+    if family == "irs_soi":
+        source = metadata.get("source_variable", metadata.get("variable", ""))
+        role = metadata.get("target_role", "")
+        if source in INCOME_SOURCES and "aca" not in role:
+            return "cps_native", "income_components"
+        return "cps_native", "tax_items"
+    return "other", family
+
+
+def _concept_key(tool, spec) -> tuple[tuple, list[str]]:
+    """Geography-free value key of one spec, from the release tool's helpers."""
+
+    def bound(value):
+        v = tool._as_bound(str(value))
+        return "inf" if v == math.inf else ("-inf" if v == -math.inf else repr(float(v)))
+
+    def filing(name):
+        status = tool.FILING_STATUS_MAP[name]
+        if status is None:
+            return "ALL"
+        return status if isinstance(status, str) else "|".join(sorted(status))
+
+    def child(filter_):
+        if filter_ is None:
+            return None
+        mask = tool._eitc_child_count_mask(np.arange(0, 6, dtype=float), filter_)
+        return "".join("1" if x else "0" for x in mask)
+
+    m = spec.metadata
+    notes: list[str] = []
+    if spec.family == "jct":
+        return ("jct",), ["excluded:reform_simulation"]
+    if spec.family == "state_income_tax":
+        return ("state_income_tax",), notes
+    materializer = m.get("materializer")
+    if spec.family == "irs_soi":
+        source = m.get("source_variable", m.get("variable"))
+        if tool._unsupported_soi_ledger_filters(m):
+            notes.append("excluded:unsupported_soi_filter")
+        mode = "count" if source == "count" else (
+            "indicator" if m.get("measure_mode") == "indicator_sum" else "amount"
+        )
+        key = ("irs_soi", source, mode, bound(m["agi_lower_bound"]),
+               bound(m["agi_upper_bound"]), filing(m["filing_status"]),
+               child(tool._soi_eitc_child_count_filter(m)),
+               bool(tool._soi_requires_positive_eitc_filter(m)),
+               m.get("itemized_only") == "true", m.get("taxable_only") == "true")
+        return key, notes
+    if materializer == "population_age":
+        return ("population_age", bound(m.get("age_lower_bound", "-inf")),
+                bound(m.get("age_upper_bound", "inf"))), notes
+    if materializer == "policyengine_variable":
+        base = tool._base_variables_from_metadata(m)
+        less_than = tool._less_than_from_metadata(m)
+        mode = m.get("measure_mode", "sum")
+        low, high = m.get("age_lower_bound"), m.get("age_upper_bound")
+        if low is not None or high is not None:
+            if m.get("state_fips") or m.get("congressional_district_geoid"):
+                notes.append("age_banded_geographic_spec")
+            return ("pe_var_age", base, mode, bound(low if low is not None else "-inf"),
+                    bound(high if high is not None else "inf")), notes
+        return ("pe_var", base, mode, m.get("indicator_map_to"),
+                m.get("indicator_filter_variable"),
+                None if less_than is None else repr(less_than)), notes
+    return ("direct", spec.measure), ["direct_measure"]
+
+
+def do_compile(args) -> None:
+    started = time.time()
+    tool = release_tool()
+    from microcosm.build.ledger_artifact import load_ledger_consumer_artifact
+    from microcosm.build.us_runtime import (
+        apply_us_medicaid_enrollment_substitutions,
+        compile_us_fiscal_target_registry,
+        default_congressional_district_vintage_crosswalk_path,
+        load_congressional_district_vintage_crosswalk,
+    )
+    from microcosm.calibrate.registry import TargetSpec
+
+    feed_sha = sha256_file(args.feed)
+    if feed_sha != FEED_SHA256:
+        raise SystemExit(f"feed sha256 {feed_sha} != pinned {FEED_SHA256}")
+    artifact = load_ledger_consumer_artifact(args.feed)
+    registry = compile_us_fiscal_target_registry(
+        artifact.facts,
+        target_period=PERIOD,
+        congressional_district_vintage_crosswalk=load_congressional_district_vintage_crosswalk(
+            default_congressional_district_vintage_crosswalk_path()
+        ),
+        age_targets=True,
+    )
+    registry, substitutions = apply_us_medicaid_enrollment_substitutions(registry)
+    specs = tuple(registry.specs)
+    national_state, surface_receipt = tool._select_target_surface(
+        specs, tool.TARGET_SURFACE_NATIONAL_STATE
+    )
+    national_state_names = {spec.name for spec in national_state}
+    loss_weights = np.asarray(tool._fiscal_target_loss_weights(registry), dtype=np.float64)
+    # The release computes weights on the surface it calibrates, so the
+    # national product gets its own vector over the national_state specs.
+    from types import SimpleNamespace
+
+    national_state_weights = dict(zip(
+        (spec.name for spec in national_state),
+        np.asarray(tool._fiscal_target_loss_weights(SimpleNamespace(specs=tuple(national_state))), dtype=np.float64),
+        strict=True,
+    ))
+
+    concept_ids: dict[tuple, int] = {}
+    concept_specs = []
+    rows = []
+    for index, spec in enumerate(specs):
+        key, notes = _concept_key(tool, spec)
+        excluded = next((n for n in notes if n.startswith("excluded:")), None)
+        m = dict(spec.metadata)
+        concept_id = -1
+        measure = None
+        if key == ("state_income_tax",):
+            measure = "state_income_tax"
+        elif excluded is None:
+            if key not in concept_ids:
+                concept_ids[key] = len(concept_ids)
+                cid = concept_ids[key]
+                stripped = {
+                    k: v for k, v in m.items()
+                    if k not in {"state_fips", "congressional_district_geoid",
+                                 "hierarchy_parent_target_name"}
+                }
+                stripped["ledger_geography_level"] = "country"
+                stripped["ledger_geography_id"] = "US"
+                concept_measure = (
+                    spec.measure if key[0] == "direct" else f"bakeoff_concept_{cid:05d}"
+                )
+                concept_specs.append(
+                    dataclasses.replace(
+                        spec,
+                        name=f"bakeoff.concept.{cid:05d}",
+                        measure=concept_measure,
+                        metadata=stripped,
+                        hierarchy=None,
+                    )
+                )
+            concept_id = concept_ids[key]
+            measure = concept_specs[concept_id].measure
+        level = m.get("ledger_geography_level", "")
+        group, dimension = dimension_of(spec.family, m)
+        group_key = target_split_group_key(spec.name, m.get("hierarchy_parent_target_name"))
+        rows.append({
+            "target_index": index,
+            "name": spec.name,
+            "period": str(spec.period),
+            "family": spec.family,
+            "level": {"country": "national", "congressional_district": "cd"}.get(level, level),
+            "state_fips": int(m["state_fips"]) if m.get("state_fips") else -1,
+            "cd_geoid": int(m["congressional_district_geoid"]) if m.get("congressional_district_geoid") else -1,
+            "value": float(spec.value),
+            "measure": measure,
+            "concept_id": concept_id,
+            "concept_key": json.dumps(key, default=str),
+            "excluded": excluded,
+            "notes": ";".join(n for n in notes if not n.startswith("excluded:")),
+            "in_national_state": spec.name in national_state_names,
+            "loss_weight": float(loss_weights[index]),
+            "loss_weight_national_state": float(national_state_weights.get(spec.name, np.nan)),
+            "group_key": group_key,
+            "role": target_split_role(group_key),
+            "dimension_group": group,
+            "dimension": dimension,
+            "value_basis": tool._fiscal_target_value_basis(spec),
+        })
+    for name, metadata in REPORT_SPECS.items():
+        concept_specs.append(
+            TargetSpec(
+                name=f"bakeoff.{name}",
+                entity="household",
+                value=1.0,
+                measure=f"bakeoff_{name.replace('.', '_')}",
+                source="support-mix bake-off report-only measure (never calibrated)",
+                family="bakeoff_report",
+                metadata={"materializer": "policyengine_variable", **metadata},
+            )
+        )
+    targets = pd.DataFrame(rows)
+    args.out.mkdir(parents=True, exist_ok=True)
+    targets.to_parquet(args.out / "targets.parquet", index=False)
+    with open(args.out / "concept_specs.pkl", "wb") as handle:
+        pickle.dump(tuple(concept_specs), handle)
+    receipt = {
+        "feed": str(args.feed),
+        "feed_sha256": feed_sha,
+        "registry_version": registry.version,
+        "n_specs": len(specs),
+        "n_concepts": len(concept_ids),
+        "n_report_concepts": len(REPORT_SPECS),
+        "medicaid_substitutions": len(substitutions),
+        "national_state_surface": surface_receipt,
+        "excluded": dict(Counter(r["excluded"] for r in rows if r["excluded"])),
+        "notes": dict(Counter(n for r in rows for n in r["notes"].split(";") if n)),
+        "roles": dict(Counter(r["role"] for r in rows)),
+        "roles_by_level": {
+            level: dict(Counter(r["role"] for r in rows if r["level"] == level))
+            for level in sorted({r["level"] for r in rows})
+        },
+        "dimension_counts": dict(Counter(f"{r['dimension_group']}/{r['dimension']}/{r['level']}" for r in rows)),
+        "split_note": (
+            "Mirrors the unmerged us-target-roles-holdout split (salts, \\x1f "
+            "hash, group keys) without its pins; see support_mix.py."
+        ),
+        "wall_s": round(time.time() - started, 1),
+        "peak_rss_gb": round(peak_rss_gb(), 2),
+    }
+    write_json(args.out / "compile.json", receipt)
+    log(json.dumps({k: receipt[k] for k in ("n_specs", "n_concepts", "roles", "excluded")}))
+
+
+# ------------------------------------------------------------------ shard
+
+
+class _FixedStore:
+    """Row-sliced reads of a pandas ``fixed``-format HDF5 frame store."""
+
+    def __init__(self, path: Path):
+        import h5py
+        import tables
+
+        self.h5 = h5py.File(path, "r")
+        self.tb = tables.open_file(str(path), mode="r")
+        self.layout = {}
+        for entity in ENTITIES:
+            group = self.h5[entity]
+            blocks, i = [], 0
+            while f"block{i}_items" in group:
+                items = [x.decode() for x in group[f"block{i}_items"][()]]
+                blocks.append((i, items, str(group[f"block{i}_values"].dtype)))
+                i += 1
+            self.layout[entity] = {
+                "nrows": int(group["axis1"].shape[0]),
+                "blocks": blocks,
+                "columns": [item for _, items, _ in blocks for item in items],
+            }
+        self._objects: dict[tuple[str, int], np.ndarray] = {}
+
+    def rows(self, entity: str) -> int:
+        return self.layout[entity]["nrows"]
+
+    def read(self, entity: str, start: int, stop: int, columns=None) -> pd.DataFrame:
+        columns = self.layout[entity]["columns"] if columns is None else list(columns)
+        out = {}
+        group = self.h5[entity]
+        for i, items, dtype in self.layout[entity]["blocks"]:
+            wanted = [(j, c) for j, c in enumerate(items) if c in columns]
+            if not wanted:
+                continue
+            if dtype == "object":
+                key = (entity, i)
+                if key not in self._objects:
+                    node = self.tb.get_node(f"/{entity}/block{i}_values")
+                    array = np.asarray(node[0], dtype=object)
+                    self._objects[key] = array.reshape(1, -1) if array.ndim == 1 else array.T
+                block = self._objects[key]
+                for j, c in wanted:
+                    out[c] = block[j, start:stop]
+            else:
+                values = group[f"block{i}_values"][start:stop, :]
+                for j, c in wanted:
+                    out[c] = values[:, j]
+        return pd.DataFrame({c: out[c] for c in columns})
+
+    def read_positions(self, entity: str, positions: np.ndarray) -> pd.DataFrame:
+        positions = np.asarray(positions, dtype=np.int64)
+        if not len(positions):
+            return self.read(entity, 0, 0)
+        low, high = int(positions.min()), int(positions.max()) + 1
+        if high - low > 4 * len(positions) + 10_000:
+            raise RuntimeError(
+                f"{entity}: rows for a shard span {high - low:,} for {len(positions):,} rows; "
+                "the store is not in household order."
+            )
+        frame = self.read(entity, low, high)
+        return frame.iloc[positions - low].reset_index(drop=True)
+
+
+def _write_shard(path: Path, tables: dict[str, pd.DataFrame], weights: np.ndarray) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    tmp = path.with_suffix(".tmp")
+    with open(tmp, "wb") as handle:
+        pickle.dump({"tables": tables, "household_weight": weights}, handle, protocol=5)
+    tmp.replace(path)
+
+
+def spm_zero_adult_households(tables: dict[str, pd.DataFrame]) -> np.ndarray:
+    """Households with an SPM unit the pinned engine has no adult for.
+
+    spm_calculator's policyengine adapter counts a member as an adult when
+    ``age >= 18`` or ``age >= 15`` with ``is_spm_independent_minor_role``,
+    whose formula is ``is_household_head | is_household_spouse``; a unit with
+    no such member raises ``SPMInputError``. Returns one flag per household
+    row, in household-table order.
+    """
+    hh, person = tables["household"], tables["person"]
+    age = pd.to_numeric(person["age"], errors="coerce").fillna(0).to_numpy()
+    # Either role column alone still classifies its members; with neither,
+    # 15-17-year-old heads and spouses would silently count as minors.
+    role_columns = [c for c in ("is_household_head", "is_household_spouse") if c in person]
+    if not role_columns:
+        raise ValueError("person table has neither is_household_head nor is_household_spouse")
+    role = np.zeros(len(person), dtype=bool)
+    for column in role_columns:
+        role |= pd.Series(person[column]).fillna(False).astype(bool).to_numpy()
+    adult = (age >= 18) | ((age >= 15) & role)
+    unit_adults = pd.Series(adult).groupby(person["person_spm_unit_id"].to_numpy()).sum()
+    empty_units = unit_adults.index[unit_adults.to_numpy() == 0]
+    flagged = person.loc[person["person_spm_unit_id"].isin(empty_units), "person_household_id"]
+    return np.isin(hh["household_id"].to_numpy(), flagged.to_numpy())
+
+
+def _row_index_columns(tables: dict[str, pd.DataFrame], source: str) -> pd.DataFrame:
+    """Per-household identity, geography and ACS-native evaluation columns."""
+    hh = tables["household"]
+    person = tables["person"]
+    positions = pd.Series(np.arange(len(hh)), index=hh["household_id"].to_numpy())
+    person_position = positions.reindex(person["person_household_id"].to_numpy()).to_numpy()
+    n = len(hh)
+
+    def per_household(values) -> np.ndarray:
+        return np.bincount(person_position, weights=np.asarray(values, dtype=np.float64), minlength=n)
+
+    age = pd.to_numeric(person["age"], errors="coerce").fillna(0).to_numpy()
+    male = ~pd.to_numeric(person["is_female"]).fillna(0).astype(bool).to_numpy()
+    rent = pd.to_numeric(person.get("pre_subsidy_rent", 0.0), errors="coerce").fillna(0.0)
+    taxes = pd.to_numeric(person.get("real_estate_taxes", 0.0), errors="coerce").fillna(0.0)
+    tenure = hh["tenure_type"].astype(str).to_numpy()
+    out = pd.DataFrame({
+        "household_id": hh["household_id"].to_numpy(),
+        "state_fips": pd.to_numeric(hh["state_fips"]).to_numpy().astype(np.int64),
+        "cd_geoid": pd.to_numeric(hh["congressional_district_geoid"]).to_numpy().astype(np.int64),
+        "county_fips": pd.to_numeric(hh["county_fips"]).to_numpy().astype(np.int64),
+        "persons": per_household(np.ones(len(person))),
+        "persons_0_17": per_household(age < 18),
+        "persons_18_64": per_household((age >= 18) & (age < 65)),
+        "persons_65p": per_household(age >= 65),
+        "persons_male": per_household(male),
+        "rent_annual": per_household(rent),
+        "real_estate_taxes": per_household(taxes),
+        "owner": np.isin(tenure, ["OWNED_WITH_MORTGAGE", "OWNED_OUTRIGHT"]).astype(np.float64),
+        "renter": (tenure == "RENTED").astype(np.float64),
+    })
+    if source == "cps":
+        peridnum = person["PERIDNUM"].astype(str).str[:20].to_numpy()
+        if (pd.Series(peridnum).groupby(person_position).nunique() != 1).any():
+            raise RuntimeError("persons of one CPS household disagree on PERIDNUM[:20] (H_IDNUM)")
+        first = pd.Series(peridnum).groupby(person_position).first()
+        out["unit_key"] = "cps:" + first.reindex(np.arange(n)).to_numpy().astype(str)
+        year = pd.Series(pd.to_numeric(person["source_year"]).to_numpy()).groupby(person_position).first()
+        out["income_year"] = year.reindex(np.arange(n)).to_numpy().astype(np.int64)
+        out["source_key"] = out["income_year"].astype(str) + "|" + out["unit_key"]
+        out["channel"] = hh["household_support_channel"].astype(str).to_numpy()
+        out["clone_index"] = pd.to_numeric(hh["household_support_clone_index"]).to_numpy()
+        out["group_quarters"] = False
+    else:
+        out["unit_key"] = "acs:" + hh["SERIALNO"].astype(str).to_numpy()
+        out["income_year"] = -1
+        out["source_key"] = out["unit_key"]
+        out["channel"] = "acs"
+        out["clone_index"] = 0
+        out["group_quarters"] = pd.to_numeric(hh["TYPEHUGQ"], errors="coerce").fillna(1).to_numpy() != 1
+        out["spm_zero_adult_unit"] = spm_zero_adult_households(tables)
+    return out
+
+
+def do_shard(args) -> None:
+    started = time.time()
+    out_dir = args.out / "shards" / args.source
+    out_dir.mkdir(parents=True, exist_ok=True)
+    index_parts = []
+    if args.source == "cps":
+        from microcosm.build.us_runtime.h5_io import read_frame_table
+
+        with pd.HDFStore(args.h5, mode="r") as store:
+            tables = {entity: read_frame_table(store, entity) for entity in ENTITIES}
+        with pd.HDFStore(args.design_h5, mode="r") as store:
+            design = store.select("household", columns=["household_id", "household_weight"])
+        if not np.array_equal(design["household_id"].to_numpy(), tables["household"]["household_id"].to_numpy()):
+            raise SystemExit("design-weight base and CPS export disagree on household ids/order")
+        tables["household"] = tables["household"].drop(columns=["household_weight"])
+        weights_all = design["household_weight"].to_numpy(np.float64)
+        households = tables["household"]
+        person_household = tables["person"]["person_household_id"].to_numpy()
+        position = pd.Series(np.arange(len(households)), index=households["household_id"].to_numpy())
+        person_position = position.reindex(person_household).to_numpy()
+        n = len(households)
+        for shard, low in enumerate(range(0, n, args.households_per_shard)):
+            high = min(low + args.households_per_shard, n)
+            person_mask = (person_position >= low) & (person_position < high)
+            part = {"household": households.iloc[low:high].reset_index(drop=True),
+                    "person": tables["person"].loc[person_mask].reset_index(drop=True)}
+            for entity, (id_col, person_col) in GROUP_ID.items():
+                ids = np.unique(part["person"][person_col].to_numpy())
+                table = tables[entity]
+                part[entity] = table.loc[table[id_col].isin(ids)].reset_index(drop=True)
+            weights = weights_all[low:high]
+            _write_shard(out_dir / f"shard_{shard:03d}.pkl", part, weights)
+            index = _row_index_columns(part, "cps")
+            index["design_weight"] = weights
+            index["shard"] = shard
+            index["position"] = np.arange(high - low)
+            index_parts.append(index)
+            log(f"cps shard {shard}: {high - low} households")
+    else:
+        store = _FixedStore(args.h5)
+        hh_ids = store.read("household", 0, store.rows("household"), ["household_id", "household_spine"])
+        acs_positions = np.flatnonzero(hh_ids["household_spine"].astype(str).to_numpy() == "acs_2024_1yr")
+        if not len(acs_positions) or not np.array_equal(
+            acs_positions, np.arange(acs_positions[0], acs_positions[-1] + 1)
+        ):
+            raise SystemExit("ACS spine rows are not one contiguous household range")
+        household_position = pd.Series(np.arange(len(hh_ids)), index=hh_ids["household_id"].to_numpy())
+        person_ids = store.read("person", 0, store.rows("person"),
+                                ["person_household_id", *[c for _, c in GROUP_ID.values()]])
+        person_position = household_position.reindex(person_ids["person_household_id"].to_numpy()).to_numpy()
+        group_positions = {}
+        for entity, (id_col, _) in GROUP_ID.items():
+            ids = store.read(entity, 0, store.rows(entity), [id_col])[id_col].to_numpy()
+            group_positions[entity] = pd.Series(np.arange(len(ids)), index=ids)
+        first, last = int(acs_positions[0]), int(acs_positions[-1]) + 1
+        for shard, low in enumerate(range(first, last, args.households_per_shard)):
+            high = min(low + args.households_per_shard, last)
+            wait_for_memory(args.min_available_gb)
+            persons = np.flatnonzero((person_position >= low) & (person_position < high))
+            part = {"household": store.read("household", low, high),
+                    "person": store.read_positions("person", persons)}
+            for entity, (_id_col, person_col) in GROUP_ID.items():
+                ids = np.unique(person_ids[person_col].to_numpy()[persons])
+                rows = np.sort(group_positions[entity].reindex(ids).to_numpy().astype(np.int64))
+                part[entity] = store.read_positions(entity, rows)
+            weights = part["household"].pop("household_weight").to_numpy(np.float64)
+            _write_shard(out_dir / f"shard_{shard:03d}.pkl", part, weights)
+            index = _row_index_columns(part, "acs")
+            index["design_weight"] = weights
+            index["shard"] = shard
+            index["position"] = np.arange(high - low)
+            index_parts.append(index)
+            log(f"acs shard {shard}: {high - low} households, peak {peak_rss_gb():.1f} GB")
+            del part
+            gc.collect()
+    index = pd.concat(index_parts, ignore_index=True)
+    index["source"] = args.source
+    index.to_parquet(args.out / f"rows_{args.source}.parquet", index=False)
+    write_json(out_dir / "shard.json", {
+        "source": args.source, "h5": str(args.h5), "h5_sha256": sha256_file(args.h5),
+        "design_h5": str(args.design_h5) if args.design_h5 else None,
+        "shards": len(index_parts), "households": len(index),
+        "group_quarters_households": int(index["group_quarters"].sum()),
+        "spm_zero_adult_households": int(index.get("spm_zero_adult_unit", pd.Series(dtype=bool)).sum()),
+        "spm_zero_adult_in_group_quarters": int(
+            (index.get("spm_zero_adult_unit", False) & index["group_quarters"]).sum()
+        ),
+        "wall_s": round(time.time() - started, 1), "peak_rss_gb": round(peak_rss_gb(), 2),
+    })
+
+
+def do_spm_flags(args) -> None:
+    """Recompute the SPM zero-adult flag on existing ACS shards (receipt)."""
+    rows = pd.read_parquet(args.work / "rows_acs.parquet")
+    flag = np.zeros(len(rows), dtype=bool)
+    for shard in sorted(rows["shard"].unique()):
+        with open(args.work / "shards" / "acs" / f"shard_{shard:03d}.pkl", "rb") as handle:
+            tables = pickle.load(handle)["tables"]
+        index = np.flatnonzero(rows["shard"].to_numpy() == shard)
+        if not np.array_equal(rows["household_id"].to_numpy()[index], tables["household"]["household_id"].to_numpy()):
+            raise SystemExit(f"shard {shard}: row index and shard disagree on household order")
+        flag[index] = spm_zero_adult_households(tables)
+    # Rewriting the row index changes its digest and so every arm's inputs;
+    # write only when the flag changed.
+    if "spm_zero_adult_unit" in rows and np.array_equal(rows["spm_zero_adult_unit"].to_numpy(bool), flag):
+        log("spm flags unchanged; row index left as is")
+    else:
+        rows["spm_zero_adult_unit"] = flag
+        rows.to_parquet(args.work / "rows_acs.parquet", index=False)
+    gq = rows["group_quarters"].to_numpy()
+    write_json(args.work / "spm_flags.json", {
+        "acs_households": int(len(rows)),
+        "group_quarters_households": int(gq.sum()),
+        "group_quarters_share": float(gq.mean()),
+        "spm_zero_adult_households": int(flag.sum()),
+        "spm_zero_adult_share": float(flag.mean()),
+        "spm_zero_adult_in_group_quarters": int((flag & gq).sum()),
+        "rule": "age >= 18 or (age >= 15 and (is_household_head or is_household_spouse)), per SPM unit",
+    })
+    log(f"spm flags: {int(flag.sum())} households ({int((flag & gq).sum())} in group quarters)")
+
+
+# ------------------------------------------------------------------ materialize
+
+
+def _load_shard_frame(path: Path):
+    from microcosm.build.serialization_dtypes import canonicalize_frame_string_dtypes
+    from microcosm.frame import Frame, WeightKind, Weights
+    from microcosm.frame.units import US_SCHEMA
+
+    with open(path, "rb") as handle:
+        payload = pickle.load(handle)
+    frame = Frame(
+        payload["tables"],
+        US_SCHEMA,
+        {"household": Weights(np.asarray(payload["household_weight"]), WeightKind.CALIBRATED)},
+    )
+    return canonicalize_frame_string_dtypes(frame, boundary="support-mix shard load", in_place=True)
+
+
+#: Engine inputs the Sep-23 ACS staging carries as all-null on ACS rows and
+#: its reviewed-null register predates. ``is_spm_independent_minor_role`` has
+#: a formula (``is_household_head | is_household_spouse``, spm_calculator's
+#: policyengine adapter); a column that is entirely null is dropped so the
+#: engine derives the role from household structure, as it did for the
+#: production ACS local release, whose staging had no such column. A
+#: partially null column is an error. Counted in every receipt.
+FORMULA_DERIVED_WHEN_ALL_NULL = (("person", "is_spm_independent_minor_role"),)
+
+
+def _drop_all_null_formula_inputs(frame):
+    from microcosm.frame import Frame
+
+    dropped = {}
+    tables = {entity: frame.table(entity) for entity in frame.entities}
+    for entity, column in FORMULA_DERIVED_WHEN_ALL_NULL:
+        table = tables[entity]
+        if column not in table.columns:
+            continue
+        missing = table[column].isna()
+        if missing.all():
+            tables[entity] = table.drop(columns=[column])
+            dropped[f"{entity}.{column}"] = int(missing.sum())
+        elif missing.any():
+            raise RuntimeError(f"{entity}.{column} is partially null ({int(missing.sum())} rows)")
+    if not dropped:
+        return frame, dropped
+    weights = {entity: frame.weights_for(entity) for entity in frame.weighted_entities}
+    return Frame(tables, frame.schema, weights, frame.strata, mass_log=frame.mass_log), dropped
+
+
+def _materialize_frame(frame, concept_specs, *, summary_path: Path | None, batch: int):
+    tool = release_tool()
+    acs = acs_local_tool()
+    projected, dropped = acs.project_input_only(frame, period=PERIOD)
+    projected, formula_derived = _drop_all_null_formula_inputs(projected)
+    if summary_path is not None:
+        acs.fill_reviewed_nulls(projected, summary_path, period=PERIOD)
+    target_frame, registry, compilation = tool._materialize_target_frame(
+        projected,
+        tuple(concept_specs),
+        maximum_microsim_batch_size=batch,
+        refuse_population_aggregates=True,
+    )
+    household = target_frame.table("household")
+    compilation = {**compilation, "formula_derived_inputs": formula_derived}
+    return household, [spec.measure for spec in registry.specs], compilation, dropped
+
+
+def input_digests(work: Path, truth: Path, epochs: int) -> dict:
+    """Digests of every input an arm receipt can depend on."""
+    return {
+        "targets": sha256_file(work / "targets.parquet"),
+        "rows_cps": sha256_file(work / "rows_cps.parquet"),
+        "rows_acs": sha256_file(work / "rows_acs.parquet"),
+        "truth": sha256_file(truth),
+        "concepts_cps": _concept_digest(work, "cps"),
+        "concepts_acs": _concept_digest(work, "acs"),
+        "epochs": int(epochs),
+    }
+
+
+def arm_input_keys(arm: SupportMixArm) -> tuple[str, ...]:
+    """The inputs an arm's result depends on.
+
+    An arm without CPS years reads the CPS row index only for the starting
+    mass, which the population mass repair rescales away (the starting
+    weights are linear in it), and for validations that raise rather than
+    change results; an arm without a budget has no ACS fill and no clones
+    (clones draw geography from ACS rows).
+    """
+    keys = ["targets", "truth", "epochs"]
+    if arm.cps_income_years:
+        keys += ["rows_cps", "concepts_cps"]
+    if arm.budget is not None:
+        keys += ["rows_acs", "concepts_acs"]
+    return tuple(keys)
+
+
+#: Arm options that change results, with the value a receipt that does not
+#: record them was solved under.
+ARM_OPTION_DEFAULTS = {"exclude_gq": False}
+
+
+def arm_inputs(digests: dict, arm: SupportMixArm, *, exclude_gq: bool = False) -> dict:
+    return {**{key: digests[key] for key in arm_input_keys(arm)}, "exclude_gq": bool(exclude_gq)}
+
+
+def inputs_current(previous: dict | None, current: dict) -> bool:
+    """A receipt is current when every input its arm depends on is unchanged."""
+    return isinstance(previous, dict) and all(
+        previous.get(k, ARM_OPTION_DEFAULTS.get(k)) == v for k, v in current.items()
+    )
+
+
+def rescore_refusal(previous: dict, inputs: dict, rows_digest: str) -> str | None:
+    """Why saved weights may not be re-scored, or ``None`` when they may.
+
+    Re-scoring is for a scoring change only: the weights must come from the
+    same rows and the same targets, split, truth, concepts and epochs.
+    """
+    if previous.get("rows_digest") != rows_digest:
+        return "the receipt has no rows digest or a different one"
+    if not inputs_current(previous.get("inputs"), inputs):
+        return "the arm's inputs changed since the solve"
+    return None
+
+
+def _concept_digest(work: Path, source: str) -> str:
+    """Content digest of a source's materialized parts (values and row maps)
+    and of what their columns mean: the compiled concept specs and the
+    source's ``measures.json``. A recompile that leaves the parts in place,
+    such as one that changes only the report specs, still moves the digest,
+    so no receipt reads as current against columns that changed meaning.
+    """
+    digest = hashlib.sha256()
+    for meaning in (work / "concept_specs.pkl", work / "concepts" / source / "measures.json"):
+        digest.update(meaning.name.encode())
+        digest.update((sha256_file(meaning) if meaning.exists() else "absent").encode())
+    for path in sorted((work / "concepts" / source).glob("shard_*.npz")):
+        if path.name.endswith(".tmp.npz"):
+            continue
+        digest.update(path.name.encode())
+        digest.update(sha256_file(path).encode())
+        rows_path = path.with_suffix(".rows.npy")
+        if rows_path.exists():
+            digest.update(sha256_file(rows_path).encode())
+    return digest.hexdigest()
+
+
+def shard_source_sha(work: Path, source: str) -> str:
+    """The sha256 of the h5 the source's shards were cut from (``shard.json``)."""
+    return json.loads((work / "shards" / source / "shard.json").read_text())["h5_sha256"]
+
+
+def part_provenance(work: Path, source: str, summary: Path | None = None, *, with_summary: bool = True) -> dict:
+    """What a materialized part's values depend on besides its rows.
+
+    The source h5 fixes the households, the compiled concept specs fix what
+    each column means (``measures.json`` names are positional), and the
+    reviewed-null summary fixes how null inputs were filled.
+    """
+    provenance = {
+        "source_h5_sha256": shard_source_sha(work, source),
+        "concept_specs_sha256": sha256_file(work / "concept_specs.pkl"),
+    }
+    if with_summary:
+        provenance["summary_sha256"] = sha256_file(summary) if summary else None
+    return provenance
+
+
+def stale_concept_parts(directory: Path, expected: dict) -> list[Path]:
+    """Materialized parts whose receipt does not match ``expected`` provenance.
+
+    A part with no receipt, a different source h5, different concept specs
+    or a different null-fill summary may hold values for other households or
+    under other column meanings.
+    """
+    stale = []
+    for path in sorted(directory.glob("shard_*.npz")):
+        if path.name.endswith(".tmp.npz"):
+            continue
+        receipt = path.with_suffix(".json")
+        recorded = json.loads(receipt.read_text()) if receipt.exists() else {}
+        if any(recorded.get(key, "missing") != value for key, value in expected.items()):
+            stale.append(path)
+    return stale
+
+
+def acs_rank(work: Path) -> np.ndarray:
+    """Each ACS row's position in the fixed salted selection order (cached,
+    keyed by a digest of the row index's unit-key sequence)."""
+    rows = pd.read_parquet(work / "rows_acs.parquet", columns=["unit_key"])
+    digest = hashlib.sha256("\n".join(rows["unit_key"]).encode()).hexdigest()
+    path, key = work / "acs_rank.npy", work / "acs_rank.key"
+    if path.exists() and key.exists() and key.read_text() == digest:
+        return np.load(path)
+    order = acs_selection_order(rows["unit_key"].tolist(), salt=ACS_SELECTION_SALT)
+    rank = np.empty(len(order), dtype=np.int64)
+    rank[order] = np.arange(len(order))
+    np.save(path, rank)
+    key.write_text(digest)
+    return rank
+
+
+def compiled_measures(work: Path) -> list[str]:
+    """Column names of every materialized part, in order."""
+    with open(work / "concept_specs.pkl", "rb") as handle:
+        return [spec.measure for spec in pickle.load(handle)] + ["state_income_tax"]
+
+
+def do_materialize(args) -> None:
+    with open(args.work / "concept_specs.pkl", "rb") as handle:
+        concept_specs = pickle.load(handle)
+    measures = compiled_measures(args.work)
+    shard_dir = args.work / "shards" / args.source
+    out_dir = args.work / "concepts" / args.source
+    out_dir.mkdir(parents=True, exist_ok=True)
+    provenance = part_provenance(args.work, args.source, args.summary)
+    stale = stale_concept_parts(out_dir, provenance)
+    if stale and not args.replace_stale:
+        raise SystemExit(f"{len(stale)} {args.source} concept parts (e.g. {stale[0].name}) were not built "
+                         "from the current shards, concept specs and summary; rerun with --replace-stale")
+    for path in stale:
+        for old in (path, path.with_suffix(".rows.npy"), path.with_suffix(".json")):
+            old.unlink(missing_ok=True)
+        log(f"removed stale {path.name}")
+    rows = pd.read_parquet(args.work / f"rows_{args.source}.parquet")
+    rows["row"] = np.arange(len(rows))
+    if "spm_zero_adult_unit" in rows:
+        # Group-quarters records whose SPM unit has no classified adult: the
+        # pinned engine refuses them, and Census puts ACS group quarters
+        # outside the SPM universe (us_runtime/spm_universe_source.py).
+        rows = rows[~rows["spm_zero_adult_unit"]]
+    low, high = 0, len(rows)
+    if args.rank_range:
+        low, high = (int(x) for x in args.rank_range.split(","))
+        rows["rank"] = acs_rank(args.work)[rows["row"].to_numpy()]
+    shards = sorted(shard_dir.glob("shard_*.pkl"))
+    if args.only:
+        wanted = {int(x) for x in args.only.split(",") if x.strip()}
+        shards = [s for s in shards if int(s.stem.split("_")[1]) in wanted]
+    suffix = f".r{low}-{high}" if args.rank_range else ""
+    for shard_path in shards:
+        out = out_dir / f"{shard_path.stem}{suffix}.npz"
+        if out.exists():
+            continue
+        shard_rows = rows[rows["shard"] == int(shard_path.stem.split("_")[1])]
+        if args.rank_range:
+            shard_rows = shard_rows[(shard_rows["rank"] >= low) & (shard_rows["rank"] < high)]
+        if shard_rows.empty:
+            continue
+        wait_for_memory(args.min_available_gb)
+        started = time.time()
+        frame = _load_shard_frame(shard_path)
+        if len(shard_rows) < frame.n("household"):
+            keep = np.isin(frame.table("person")["person_household_id"].to_numpy(),
+                           shard_rows["household_id"].to_numpy())
+            frame = frame.select(keep)
+        household, compiled, compilation, _ = _materialize_frame(
+            frame, concept_specs, summary_path=args.summary, batch=args.batch
+        )
+        position = pd.Series(shard_rows["row"].to_numpy(), index=shard_rows["household_id"].to_numpy())
+        row_positions = position.reindex(household["household_id"].to_numpy()).to_numpy()
+        if np.isnan(row_positions.astype(float)).any():
+            raise RuntimeError(f"{shard_path.name}: materialized households missing from the row index")
+        missing = [m for m in measures if m not in household.columns]
+        matrix = np.zeros((len(household), len(measures)), dtype=np.float32)
+        for j, measure in enumerate(measures):
+            if measure in household.columns:
+                matrix[:, j] = household[measure].to_numpy(dtype=np.float32)
+        csr = sp.csr_matrix(matrix)
+        np.save(out.with_suffix(".rows.npy"), row_positions.astype(np.int64))
+        sp.save_npz(out.with_suffix(".tmp.npz"), csr)
+        out.with_suffix(".tmp.npz").replace(out)
+        write_json(out.with_suffix(".json"), {
+            "shard": shard_path.name, **provenance,
+            "rank_range": [low, high] if args.rank_range else None,
+            "households": len(household), "nnz": int(csr.nnz),
+            "missing_measures": missing, "compiled_concepts": len(compiled),
+            "batching": compilation.get("target_materialization_batching"),
+            "formula_derived_inputs": compilation.get("formula_derived_inputs"),
+            "wall_s": round(time.time() - started, 1), "peak_rss_gb": round(peak_rss_gb(), 2),
+            "cpu_s": round(resource.getrusage(resource.RUSAGE_SELF).ru_utime, 1),
+        })
+        log(f"{args.source} {out.name}: {len(household)} hh, nnz {csr.nnz:,}, "
+            f"missing {len(missing)}, {time.time() - started:.0f}s, peak {peak_rss_gb():.1f} GB")
+        del frame, household, matrix, csr
+        gc.collect()
+    write_json(out_dir / "measures.json", measures)
+
+
+def select_parts(parts: list[Path], wanted: str | None) -> list[Path]:
+    """The stored parts ``--parts`` names, or every part when it names none.
+
+    Refuses a stem that matches no stored part and an empty selection: either
+    would otherwise write a passing receipt that checked nothing.
+    """
+    if wanted:
+        stems = {x.strip() for x in wanted.split(",") if x.strip()}
+        unknown = sorted(stems - {p.stem for p in parts})
+        if unknown:
+            raise SystemExit(f"--parts names no stored part: {', '.join(unknown)}")
+        parts = [p for p in parts if p.stem in stems]
+    if not parts:
+        raise SystemExit("partcheck has no stored parts to check")
+    return parts
+
+
+def partcheck_columns(fresh: np.ndarray, stored: np.ndarray, tolerance: float) -> tuple[np.ndarray, np.ndarray]:
+    """Each column's largest difference between fresh and stored values,
+    scaled by the column's largest fresh magnitude (at least 1), and the
+    columns that fail: those over ``tolerance``, and those with a nonfinite
+    value on either side, which a tolerance test alone would pass (a NaN
+    difference compares false with every tolerance).
+    """
+    with np.errstate(invalid="ignore", over="ignore"):
+        scaled = np.abs(fresh - stored).max(axis=0) / np.maximum(1.0, np.abs(fresh).max(axis=0))
+        over = scaled > tolerance
+    finite = np.isfinite(fresh).all(axis=0) & np.isfinite(stored).all(axis=0) & np.isfinite(scaled)
+    return scaled, np.flatnonzero(~finite | over)
+
+
+def do_partcheck(args) -> None:
+    """Differential: re-materialize a sample of each stored part's households
+    with the current concept specs and compare with the stored values.
+
+    Guards against parts whose columns no longer mean what ``measures.json``
+    says (a part built under other concept specs) or whose rows are other
+    households. ``--shards`` points at shard pickles cut from the same h5 when
+    the work directory's own were removed.
+    """
+    started = time.time()
+    if not (math.isfinite(args.tolerance) and args.tolerance >= 0):
+        raise SystemExit(f"--tolerance must be a finite, nonnegative number, got {args.tolerance}")
+    if args.per_part < 1:
+        raise SystemExit(f"--per-part must be at least 1, got {args.per_part}")
+    with open(args.work / "concept_specs.pkl", "rb") as handle:
+        concept_specs = pickle.load(handle)
+    measures = compiled_measures(args.work)
+    shards_dir = args.shards or (args.work / "shards" / args.source)
+    shard_sha = json.loads((shards_dir / "shard.json").read_text())["h5_sha256"]
+    if shard_sha != shard_source_sha(args.work, args.source):
+        raise SystemExit(f"{shards_dir} was cut from another h5 than the work directory's {args.source} shards")
+    rows = pd.read_parquet(args.work / f"rows_{args.source}.parquet", columns=["household_id", "shard"])
+    stored_parts = sorted(p for p in (args.work / "concepts" / args.source).glob("shard_*.npz")
+                          if not p.name.endswith(".tmp.npz"))
+    parts = select_parts(stored_parts, args.parts)
+    rng = np.random.default_rng(20260930)
+    results = {}
+    for path in parts:
+        wait_for_memory(args.min_available_gb)
+        shard = int(path.stem.split("_")[1].split(".")[0])
+        rows_path = path.with_suffix(".rows.npy")
+        positions = (np.load(rows_path) if rows_path.exists()
+                     else np.flatnonzero(rows["shard"].to_numpy() == shard))
+        stored = sp.load_npz(path).tocsr()
+        if stored.shape != (len(positions), len(measures)):
+            raise SystemExit(f"{path.name}: shape {stored.shape} vs {len(positions)} rows x {len(measures)} measures")
+        pick = np.sort(rng.choice(len(positions), min(args.per_part, len(positions)), replace=False))
+        hh_ids = rows["household_id"].to_numpy()[positions[pick]]
+        frame = _load_shard_frame(shards_dir / f"shard_{shard:03d}.pkl")
+        frame = frame.select(np.isin(frame.table("person")["person_household_id"].to_numpy(), hh_ids))
+        household, _, _, _ = _materialize_frame(frame, concept_specs, summary_path=args.summary, batch=args.batch)
+        order = pd.Series(np.arange(len(household)), index=household["household_id"].to_numpy()).reindex(hh_ids)
+        if order.isna().any():
+            raise SystemExit(f"{path.name}: sampled households missing from the re-materialized frame")
+        fresh = np.column_stack([
+            household[m].to_numpy(np.float64) if m in household.columns else np.zeros(len(household))
+            for m in measures
+        ])[order.to_numpy().astype(np.int64)]
+        old = stored[pick].toarray().astype(np.float64)
+        scaled, bad = partcheck_columns(fresh, old, args.tolerance)
+        finite = np.isfinite(scaled)
+        largest = float(scaled[finite].max()) if finite.any() else None
+        results[path.name] = {
+            "households": int(len(pick)), "max_scaled_abs_diff": largest,
+            "n_nonfinite_columns": int((~finite).sum()),
+            "columns_over_tolerance": [measures[j] for j in bad[:20]], "n_columns_over_tolerance": int(len(bad)),
+        }
+        shown = "none finite" if largest is None else f"{largest:.2e}"
+        log(f"partcheck {args.source} {path.name}: {len(pick)} hh, max scaled diff {shown}, "
+            f"{len(bad)} columns failing ({int((~finite).sum())} nonfinite, tolerance {args.tolerance:g})")
+        del frame, household
+        gc.collect()
+    receipt = {
+        "source": args.source, "shards": str(shards_dir), "source_h5_sha256": shard_sha,
+        "concept_specs_sha256": sha256_file(args.work / "concept_specs.pkl"),
+        "summary_sha256": sha256_file(args.summary) if args.summary else None,
+        "tolerance": args.tolerance, "per_part": args.per_part, "parts": results,
+        "max_scaled_abs_diff": max((r["max_scaled_abs_diff"] for r in results.values()
+                                    if r["max_scaled_abs_diff"] is not None), default=None),
+        "wall_s": round(time.time() - started, 1), "peak_rss_gb": round(peak_rss_gb(), 2),
+    }
+    write_json(args.work / f"partcheck_{args.source}.json", receipt)
+    if any(r["n_columns_over_tolerance"] for r in results.values()):
+        raise SystemExit(f"partcheck {args.source}: stored values disagree with the current concept specs")
+
+
+# ------------------------------------------------------------------ diffcheck
+
+
+def do_diffcheck(args) -> None:
+    """Differential: production per-spec columns vs concept x geography mask."""
+    started = time.time()
+    from microcosm.build.ledger_artifact import load_ledger_consumer_artifact
+    from microcosm.build.us_runtime import (
+        apply_us_medicaid_enrollment_substitutions,
+        compile_us_fiscal_target_registry,
+        default_congressional_district_vintage_crosswalk_path,
+        load_congressional_district_vintage_crosswalk,
+    )
+
+    targets = pd.read_parquet(args.work / "targets.parquet")
+    with open(args.work / "concept_specs.pkl", "rb") as handle:
+        concept_specs = pickle.load(handle)
+    artifact = load_ledger_consumer_artifact(args.feed)
+    registry = compile_us_fiscal_target_registry(
+        artifact.facts, target_period=PERIOD,
+        congressional_district_vintage_crosswalk=load_congressional_district_vintage_crosswalk(
+            default_congressional_district_vintage_crosswalk_path()),
+        age_targets=True)
+    registry, _ = apply_us_medicaid_enrollment_substitutions(registry)
+    specs = registry.specs
+    usable = targets[targets["excluded"].isna() & (targets["level"] != "national")]
+    rng = np.random.default_rng(20260928)
+    sample = []
+    for _key, group in usable.groupby(["family", "level"]):
+        take = min(len(group), args.per_group)
+        sample.extend(rng.choice(group["target_index"].to_numpy(), take, replace=False).tolist())
+    sample_specs = [specs[i] for i in sorted(sample)]
+    results = {}
+    previous = json.loads((args.work / "diffcheck.json").read_text())["results"] \
+        if (args.work / "diffcheck.json").exists() else {}
+    for source in args.sources.split(","):
+        frame = _load_shard_frame(args.work / "shards" / source / "shard_000.pkl")
+        hh_ids = frame.table("household")["household_id"].to_numpy()
+        index = pd.read_parquet(args.work / f"rows_{source}.parquet")
+        if "spm_zero_adult_unit" in index:
+            flagged = index.loc[(index["shard"] == 0) & index["spm_zero_adult_unit"], "household_id"]
+            hh_ids = hh_ids[~np.isin(hh_ids, flagged.to_numpy())]
+        hh_ids = hh_ids[: args.households]
+        person_mask = np.isin(frame.table("person")["person_household_id"].to_numpy(), hh_ids)
+        frame = frame.select(person_mask)
+        summary = args.summary if source == "acs" else None
+        direct, _, _, _ = _materialize_frame(frame, sample_specs, summary_path=summary, batch=args.batch)
+        concept, _, _, _ = _materialize_frame(frame, concept_specs, summary_path=summary, batch=args.batch)
+        state = pd.to_numeric(direct["state_fips"]).to_numpy()
+        cd = pd.to_numeric(direct["congressional_district_geoid"]).to_numpy()
+        worst, checked, skipped = 0.0, 0, 0
+        per_family = {}
+        if targets["name"].duplicated().any():
+            raise SystemExit("target names are not unique; diffcheck keys on them")
+        by_name = targets.set_index("name")
+        for spec in sample_specs:
+            row = by_name.loc[spec.name]
+            if spec.measure not in direct.columns or row["measure"] not in concept.columns:
+                skipped += 1
+                continue
+            mask = np.ones(len(direct), bool)
+            if row["state_fips"] >= 0:
+                mask &= state == row["state_fips"]
+            if row["cd_geoid"] >= 0:
+                mask &= cd == row["cd_geoid"]
+            expected = direct[spec.measure].to_numpy(np.float64)
+            got = concept[row["measure"]].to_numpy(np.float64) * mask
+            scale = max(1.0, float(np.abs(expected).max()))
+            diff = float(np.abs(expected - got).max()) / scale
+            worst = max(worst, diff)
+            key = f"{spec.family}/{row['level']}"
+            per_family[key] = max(per_family.get(key, 0.0), diff)
+            checked += 1
+        results[source] = {"households": len(direct), "specs_checked": checked,
+                           "specs_skipped": skipped, "max_scaled_abs_diff": worst,
+                           "per_family_level": per_family}
+        log(f"diffcheck {source}: {json.dumps(results[source])}")
+    write_json(args.work / "diffcheck.json", {
+        "results": {**previous, **results}, "sample_size": len(sample_specs),
+        "wall_s": round(time.time() - started, 1), "peak_rss_gb": round(peak_rss_gb(), 2)})
+
+
+# ------------------------------------------------------------------ truth
+
+ACS_TRUTH_CELLS = {  # ACS 2024 1-year table-based summary file cells (E = estimate)
+    "households": ["B25003_E001"],
+    "owner": ["B25003_E002"],
+    "renter": ["B25003_E003"],
+    "rent_contract_monthly_aggregate": ["B25060_E001"],
+    "rent_gross_monthly_aggregate": ["B25065_E001"],
+    "real_estate_taxes_aggregate": ["B25090_E001"],
+    "persons_in_households": ["B25008_E001"],
+    "persons_total": ["B01003_E001"],
+    "persons_male": ["B01001_E002"],
+    "persons_0_17": [f"B01001_E{i:03d}" for i in (3, 4, 5, 6, 27, 28, 29, 30)],
+    "persons_65p": [f"B01001_E{i:03d}" for i in (*range(20, 26), *range(44, 50))],
+}
+SUMMARY_LEVELS = {"0100000US": "national", "0400000US": "state", "0500000US": "county", "5001900US": "cd"}
+
+
+def do_truth(args) -> None:
+    """Long truth table from the ACS 2024 1-year table-based summary file."""
+    tables = sorted({cell.split("_")[0] for cells in ACS_TRUTH_CELLS.values() for cell in cells})
+    frames, digests = {}, {}
+    for table in tables:
+        path = args.sf_dir / f"acsdt1y2024-{table.lower()}.dat"
+        digests[path.name] = sha256_file(path)
+        frames[table] = pd.read_csv(path, sep="|", dtype=str).set_index("GEO_ID")
+    records = []
+    for measure, cells in ACS_TRUTH_CELLS.items():
+        table = cells[0].split("_")[0]
+        frame = frames[table]
+        values = frame[cells].apply(pd.to_numeric, errors="coerce")
+        total = values.sum(axis=1, min_count=len(cells))
+        for geo_id, value in total.items():
+            prefix, code = geo_id[:9], geo_id[9:]
+            level = SUMMARY_LEVELS.get(prefix)
+            if level is None or not np.isfinite(value) or value < 0:
+                continue
+            if level == "national":
+                geo = 0
+            elif level == "cd":
+                district = code[2:]
+                geo = int(code[:2]) * 100 + (0 if district == "98" else int(district))
+            else:
+                geo = int(code)
+            if level in ("state", "county", "cd") and int(code[:2]) == 72:
+                continue  # Puerto Rico is outside the pool
+            records.append((level, geo, measure, float(value)))
+    truth = pd.DataFrame(records, columns=["level", "geo", "measure", "value"])
+    wide = truth.pivot_table(index=["level", "geo"], columns="measure", values="value").reset_index()
+    wide["persons_18_64"] = wide["persons_total"] - wide["persons_0_17"] - wide["persons_65p"]
+    wide["rent_contract_annual_aggregate"] = 12 * wide.pop("rent_contract_monthly_aggregate")
+    wide["rent_gross_annual_aggregate"] = 12 * wide.pop("rent_gross_monthly_aggregate")
+    truth = wide.melt(id_vars=["level", "geo"], var_name="measure", value_name="value").dropna()
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    truth.to_parquet(args.out, index=False)
+    write_json(args.out.with_suffix(".json"), {
+        "source": "ACS 2024 1-year table-based summary file (Census), "
+                  "https://www2.census.gov/programs-surveys/acs/summary_file/2024/table-based-SF/",
+        "files_sha256": digests, "cells": ACS_TRUTH_CELLS,
+        "rows_by_level": truth.groupby("level").size().to_dict(),
+    })
+    log(f"truth: {truth.groupby('level').size().to_dict()}")
+
+
+def truth_table(path: Path) -> pd.DataFrame:
+    return pd.read_parquet(path)
+
+
+# ------------------------------------------------------------------ arms
+
+BUDGETS = (300_000, 600_000, 1_200_000)
+NEWEST_INCOME_YEAR = 2024
+
+
+def year_sets(newest: int = NEWEST_INCOME_YEAR) -> tuple[tuple[int, ...], ...]:
+    """The newest one, two and three CPS income years."""
+    return tuple(tuple(range(newest - k, newest + 1)) for k in range(3))
+
+
+def arm_grid(include_largest: bool = True, newest: int = NEWEST_INCOME_YEAR) -> list[SupportMixArm]:
+    """The 30 arms. ACS-only arms carry no years, so their labels (and
+    receipts) are shared across grids with different newest years."""
+    arms: list[SupportMixArm] = []
+    sets = year_sets(newest)
+    for years in sets:
+        arms.append(SupportMixArm(budget=None, cps_income_years=years))
+    for budget in BUDGETS if include_largest else BUDGETS[:2]:
+        for years in sets:
+            arms.append(SupportMixArm(budget=budget, cps_income_years=years, acs_fill_share=1.0))
+            arms.append(SupportMixArm(budget=budget, cps_income_years=years, acs_fill_share=0.0))
+        arms.append(SupportMixArm(budget=budget, cps_income_years=(), acs_fill_share=1.0))
+    for years in (sets[0], sets[2]):
+        arms.append(SupportMixArm(budget=600_000, cps_income_years=years, acs_fill_share=0.5))
+        for seed in (1, 2):
+            arms.append(SupportMixArm(budget=300_000, cps_income_years=years, acs_fill_share=1.0, seed=seed))
+    return arms
+
+
+def parse_arm(label: str) -> SupportMixArm:
+    budget, years, acs, seed = label.split(".")
+    return SupportMixArm(
+        budget=None if budget == "natural" else int(budget[1:-1]) * 1000,
+        cps_income_years=() if years == "cps0" else tuple(int(y) for y in years[3:].split("-")),
+        acs_fill_share=int(acs[3:]) / 100,
+        seed=int(seed[1:]),
+    )
+
+
+class _ConceptStore:
+    """Row-addressable concept values for one source (CSR, float32).
+
+    Each materialized part carries its rows' global positions; rows never
+    materialized are all-zero and flagged, and an arm refuses to use them.
+    Parts must not overlap: a row materialized twice would be summed.
+    """
+
+    def __init__(self, work: Path, source: str):
+        directory = work / "concepts" / source
+        stale = stale_concept_parts(directory, part_provenance(work, source, with_summary=False))
+        if stale:
+            raise RuntimeError(f"{len(stale)} {source} concept parts (e.g. {stale[0].name}) were not built "
+                               "from the current shards and concept specs; rerun materialize --replace-stale")
+        shard_of = pd.read_parquet(work / f"rows_{source}.parquet", columns=["shard"])["shard"].to_numpy()
+        n_rows = len(shard_of)
+        self.measures = json.loads((directory / "measures.json").read_text())
+        if self.measures != compiled_measures(work):
+            raise RuntimeError(f"{source} measures.json disagrees with concept_specs.pkl; rerun materialize")
+        rows_parts, parts = [], []
+        self.materialized = np.zeros(n_rows, dtype=bool)
+        for path in sorted(directory.glob("shard_*.npz")):
+            if path.name.endswith(".tmp.npz"):
+                continue
+            part = sp.load_npz(path).tocoo()
+            rows_path = path.with_suffix(".rows.npy")
+            rows = (np.load(rows_path) if rows_path.exists()
+                    else np.flatnonzero(shard_of == int(path.stem.split("_")[1].split(".")[0])))
+            if part.shape[0] != len(rows):
+                raise RuntimeError(f"{path.name}: {part.shape[0]} rows but {len(rows)} positions")
+            if self.materialized[rows].any():
+                raise RuntimeError(f"{path.name}: overlaps rows already materialized by another part")
+            self.materialized[rows] = True
+            rows_parts.append(rows[part.row])
+            parts.append(part)
+        self.matrix = sp.csr_matrix(
+            (np.concatenate([p.data for p in parts]).astype(np.float32),
+             (np.concatenate(rows_parts), np.concatenate([p.col for p in parts]))),
+            shape=(n_rows, len(self.measures)),
+        )
+
+
+class _SparseOperator:
+    """``A @ w`` with a precomputed transpose for the backward pass.
+
+    Duck-types the torch tensor ``_optimize`` expects: it reads ``.layout``
+    (not sparse CSR, so ``_apply_constraint`` calls ``matrix @ weights``) and
+    ``@``. Forward and backward are one CSR SpMV each.
+    """
+
+    def __init__(self, matrix: sp.csr_matrix):
+        import torch
+
+        self.torch = torch
+        self.layout = torch.strided
+        self.shape = matrix.shape
+        self.A = self._tensor(matrix)
+        self.AT = self._tensor(matrix.T.tocsr())
+
+    def _tensor(self, m):
+        torch = self.torch
+        m = m.astype(np.float32)
+        return torch.sparse_csr_tensor(
+            torch.from_numpy(m.indptr.astype(np.int64)),
+            torch.from_numpy(m.indices.astype(np.int64)),
+            torch.from_numpy(m.data),
+            size=m.shape,
+        )
+
+    def __matmul__(self, weights):
+        return _SpMV.apply(weights, self)
+
+
+def _spmv_class():
+    import torch
+
+    class SpMV(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, weights, operator):
+            ctx.operator = operator
+            return (operator.A @ weights.unsqueeze(1)).squeeze(1)
+
+        @staticmethod
+        def backward(ctx, grad):
+            return (ctx.operator.AT @ grad.unsqueeze(1)).squeeze(1), None
+
+    return SpMV
+
+
+_SpMV = None
+
+
+def _assemble(values: sp.csr_matrix, geo: dict[str, np.ndarray], frame: pd.DataFrame,
+              state_code: dict, cd_code: dict) -> sp.csr_matrix:
+    """Targets x rows for a target frame (see ``assemble_target_matrix``)."""
+    levels = frame["level"].to_numpy()
+    target_geo = np.zeros(len(frame), dtype=np.int64)
+    is_state, is_cd = levels == "state", levels == "cd"
+    target_geo[is_state] = [state_code[x] for x in frame["state_fips"].to_numpy()[is_state]]
+    target_geo[is_cd] = [cd_code[x] for x in frame["cd_geoid"].to_numpy()[is_cd]]
+    return assemble_target_matrix(
+        values, geo, frame["col"].to_numpy(), levels, target_geo
+    ).tocsr()
+
+
+def do_arm(args) -> None:
+    global _SpMV
+    started = time.time()
+    arm = parse_arm(args.arm)
+    out = args.work / "arms" / args.product / f"{arm.label}.json"
+    previous = json.loads(out.read_text()) if out.exists() else None
+    inputs = arm_inputs(input_digests(args.work, args.truth, args.epochs), arm, exclude_gq=args.exclude_gq)
+    rescore = (
+        previous is not None and out.with_suffix(".weights.npy").exists() and not args.force
+        and (args.rescore or not previous.get("scored_all_levels"))
+    )
+    if (previous is not None and previous.get("scored_all_levels") and inputs_current(previous.get("inputs"), inputs)
+            and not args.force and not args.rescore):
+        log(f"{out} exists")
+        return
+    wait_for_memory(args.min_available_gb)
+    import torch
+
+    torch.set_num_threads(args.threads)
+    _SpMV = _spmv_class()
+    from microcosm.calibrate.solve import _optimize, default_target_loss_scales
+
+    targets = pd.read_parquet(args.work / "targets.parquet")
+    rows_cps = pd.read_parquet(args.work / "rows_cps.parquet")
+    rows_acs = pd.read_parquet(args.work / "rows_acs.parquet")
+    # "row" is the position in the source's concept store; it survives filters.
+    rows_cps["row"] = np.arange(len(rows_cps))
+    rows_acs["row"] = np.arange(len(rows_acs))
+    if args.exclude_gq:
+        rows_acs = rows_acs[~rows_acs["group_quarters"]].reset_index(drop=True)
+    if "spm_zero_adult_unit" in rows_acs:
+        rows_acs = rows_acs[~rows_acs["spm_zero_adult_unit"]].reset_index(drop=True)
+    # ---------------- rows
+    asec = rows_cps[rows_cps["channel"] == "asec"]
+    cps_hh = asec.groupby("income_year").size().to_dict()
+    if (asec.groupby("source_key").size() != 1).any() or rows_cps["unit_key"].str.endswith("nan").any():
+        raise SystemExit("CPS source keys are not one ASEC row per source household")
+    if rows_cps.groupby("income_year")["source_key"].nunique().to_dict() != cps_hh:
+        raise SystemExit("CPS source keys disagree with ASEC household counts")
+    for name, frame in (("cps", rows_cps), ("acs", rows_acs)):
+        if not (frame["owner"].mean() > 0.3 and frame["renter"].mean() > 0.1):
+            raise SystemExit(f"{name} tenure columns look unparsed (owner/renter shares)")
+    counts = plan_arm_counts(arm, cps_households_by_year=cps_hh, acs_households=len(rows_acs))
+    cps_rows = rows_cps[rows_cps["income_year"].isin(arm.cps_income_years)].reset_index(drop=True)
+    source_keys = cps_rows["source_key"].drop_duplicates().to_numpy()
+    copies_by_source = dict(zip(source_keys, clone_copies(
+        list(source_keys), counts.clones, salt=f"{CLONE_SALT}.s{arm.seed}"), strict=True))
+    cps_copies = cps_rows["source_key"].map(copies_by_source).fillna(0).to_numpy(np.int64) \
+        if len(cps_rows) else np.zeros(0, np.int64)
+    # One fixed order; replicate r takes the r-th disjoint block of it, so
+    # replicates are independent draws and seed 0 arms are nested by budget.
+    rank = acs_rank(args.work)[rows_acs["row"].to_numpy()]
+    order = np.argsort(rank, kind="stable")
+    block = order[arm.seed * counts.acs:(arm.seed + 1) * counts.acs]
+    if len(block) < counts.acs:
+        raise SystemExit(f"{arm.label}: replicate block exceeds the ACS rows available")
+    acs_rows = rows_acs.iloc[np.sort(block)].reset_index(drop=True)
+    # Pre-repair mass only: the population mass repair below sets the level.
+    newest = rows_cps["income_year"].max()
+    total_mass = float(rows_cps.loc[rows_cps["income_year"] == newest, "design_weight"].sum() * 3)
+    cps_w, acs_w = arm_initial_weights(
+        cps_design_weights=cps_rows["design_weight"].to_numpy(),
+        cps_income_year=cps_rows["income_year"].to_numpy(),
+        cps_copies=cps_copies,
+        acs_design_weights=acs_rows["design_weight"].to_numpy(),
+        counts=counts,
+        total_mass=total_mass,
+    )
+    # Expand CPS copies; copy 0 keeps its own geography, copy k>0 draws a new
+    # (district, county) within its state in proportion to ACS households.
+    repeat = 1 + cps_copies
+    cps_expanded = cps_rows.loc[cps_rows.index.repeat(repeat)].reset_index(drop=True)
+    copy_number = np.concatenate([np.arange(r) for r in repeat]) if len(repeat) else np.zeros(0, int)
+    cps_expanded["copy"] = copy_number
+    w_cps = np.repeat(cps_w, repeat)
+    if counts.clones:
+        geo_pool = rows_acs.loc[~rows_acs["group_quarters"].astype(bool),
+                                ["state_fips", "cd_geoid", "county_fips", "design_weight"]]
+        for state, group in cps_expanded[cps_expanded["copy"] > 0].groupby("state_fips"):
+            pool = geo_pool[geo_pool["state_fips"] == state]
+            cum = np.cumsum(pool["design_weight"].to_numpy())
+            draws = np.array([
+                hash_uniform(f"{k}|{r}|{c}", salt=f"{CLONE_GEO_SALT}.s{arm.seed}")
+                for k, r, c in zip(group["source_key"], group["row"], group["copy"], strict=True)
+            ])
+            pick = np.searchsorted(cum, draws * cum[-1], side="right")
+            cps_expanded.loc[group.index, "cd_geoid"] = pool["cd_geoid"].to_numpy()[pick]
+            cps_expanded.loc[group.index, "county_fips"] = pool["county_fips"].to_numpy()[pick]
+    selected = pd.concat([cps_expanded.assign(source="cps"), acs_rows.assign(source="acs", copy=0)],
+                         ignore_index=True)
+    w0 = np.concatenate([w_cps, acs_w])
+    # The release's base population mass repair: rescale so the weighted
+    # person count equals the Census benchmark before a conserved solve.
+    benchmark = float(release_tool().US_BASE_PERSON_POPULATION_BENCHMARK)
+    mass_factor = benchmark / float(selected["persons"].to_numpy() @ w0)
+    w0 = w0 * mass_factor
+    rows_digest = hashlib.sha256(pd.util.hash_pandas_object(
+        selected[["source", "row", "copy", "cd_geoid"]], index=False).to_numpy().tobytes()).hexdigest()
+    # ---------------- concept values for the selected rows
+    stores = {}
+    blocks = []
+    for source, frame in (("cps", cps_expanded), ("acs", acs_rows)):
+        if not len(frame):
+            continue
+        if source not in stores:
+            stores[source] = _ConceptStore(args.work, source)
+        needed = frame["row"].to_numpy()
+        unmaterialized = int((~stores[source].materialized[needed]).sum())
+        if unmaterialized:
+            raise SystemExit(f"{arm.label}: {unmaterialized} {source} rows are not materialized yet")
+        blocks.append(stores[source].matrix[needed])
+    if len({tuple(store.measures) for store in stores.values()}) > 1:
+        raise SystemExit("CPS and ACS concept stores disagree on their columns")
+    measures = next(iter(stores.values())).measures
+    values = sp.vstack(blocks, format="csr")
+    del stores
+    gc.collect()
+    # ---------------- targets and lookup tables
+    measure_col = {m: j for j, m in enumerate(measures)}
+    usable = targets[targets["excluded"].isna() & targets["measure"].isin(measure_col)].copy()
+    usable["col"] = usable["measure"].map(measure_col)
+    # Both products are scored on every held-out target (national, state and
+    # district); the national product trains on its surface only.
+    holdout = usable[usable["role"] == "holdout"].reset_index(drop=True)
+    if args.product == "national":
+        usable = usable[usable["in_national_state"]]
+    train = usable[usable["role"] == "train"].reset_index(drop=True)
+    states = np.unique(selected["state_fips"])
+    cds = np.unique(np.concatenate([selected["cd_geoid"].to_numpy(), usable.loc[usable["cd_geoid"] >= 0, "cd_geoid"].to_numpy()]))
+    state_code = {s: i for i, s in enumerate(np.unique(np.concatenate([states, usable["state_fips"][usable["state_fips"] >= 0]])))}
+    cd_code = {c: i for i, c in enumerate(cds)}
+    geo = {"state": selected["state_fips"].map(state_code).to_numpy(),
+           "cd": selected["cd_geoid"].map(cd_code).to_numpy()}
+
+    a_train = _assemble(values, geo, train, state_code, cd_code)
+    a_hold = _assemble(values, geo, holdout, state_code, cd_code)
+    b_train = train["value"].to_numpy(np.float64)
+    loss_w = train["loss_weight_national_state" if args.product == "national" else "loss_weight"].to_numpy(np.float64)
+    # Local product: district household populations are trained, as in the
+    # ACS local release (never scored).
+    truth = truth_table(args.truth)
+    extra_train = 0
+    if args.product == "local":
+        # Household population (B25008): group-quarters rows neither count
+        # toward nor can be pushed by it, so CPS-only arms are not forced to
+        # inflate households to stand in for dorms and nursing homes.
+        cd_truth = truth[(truth["level"] == "cd") & (truth["measure"] == "persons_in_households")]
+        cd_truth = cd_truth[cd_truth["geo"].isin(cd_code)]
+        persons = selected["persons"].to_numpy(np.float64) * ~selected["group_quarters"].astype(bool).to_numpy()
+        codes = selected["cd_geoid"].map({g: i for i, g in enumerate(cd_truth["geo"])}).fillna(-1).to_numpy().astype(np.int64)
+        keep = (codes >= 0) & (persons > 0)
+        pop = sp.csr_matrix((persons[keep], (codes[keep], np.flatnonzero(keep))), shape=(len(cd_truth), len(selected)))
+        a_train = sp.vstack([a_train, pop], format="csr")
+        b_train = np.concatenate([b_train, cd_truth["value"].to_numpy(np.float64)])
+        loss_w = np.concatenate([loss_w, np.ones(len(cd_truth))])
+        extra_train = len(cd_truth)
+    # Drop training rows no selected household can move (zero rows).
+    nonzero = np.diff(a_train.indptr) > 0
+    a_train, b_train, loss_w = a_train[nonzero], b_train[nonzero], loss_w[nonzero]
+    scales = default_target_loss_scales(b_train)
+    if rescore and (refusal := rescore_refusal(previous, inputs, rows_digest)):
+        if args.rescore:
+            raise SystemExit(f"{arm.label}: cannot re-score the saved weights: {refusal}; re-solve with --force")
+        log(f"{arm.label}: re-solving instead of re-scoring: {refusal}")
+        rescore = False
+    solve_started = time.time()
+    if rescore:
+        # Re-score saved weights (a scoring change, no re-solve).
+        weights = np.load(out.with_suffix(".weights.npy")).astype(np.float64)
+        if len(weights) != len(w0):
+            raise SystemExit(f"{arm.label}: saved weights do not match the arm's rows")
+        trajectory = np.array([previous["train"]["loss_initial"], previous["train"]["loss_final"]])
+    else:
+        operator = _SparseOperator(a_train)
+        weights, trajectory = _optimize(
+            operator,
+            torch.tensor(b_train, dtype=torch.float32),
+            torch.tensor(loss_w, dtype=torch.float32),
+            torch.tensor(scales, dtype=torch.float32),
+            1.0,
+            w0,
+            epochs=args.epochs,
+            learning_rate=0.02,
+            conserve_mass=True,
+            max_weight_ratio=5.0,
+            l0_lambda=0.0,
+            l2_lambda=0.0,
+            target_records=None,
+            init_mean=0.999,
+            temperature=0.25,
+        )
+        del operator
+    solve_s = time.time() - solve_started if not rescore else previous["runtime"]["solve_s"]
+    gc.collect()
+    # ---------------- scoring
+    def errors(matrix, target_values, w):
+        est = matrix @ w
+        rel = np.abs(est - target_values) / np.maximum(np.abs(target_values), 1.0)
+        return est, rel
+
+    result_targets = []
+    est0, rel0 = errors(a_hold, holdout["value"].to_numpy(), w0)
+    est1, rel1 = errors(a_hold, holdout["value"].to_numpy(), weights)
+    supported = np.diff(a_hold.indptr) > 0
+    for i, row in enumerate(holdout.itertuples(index=False)):
+        result_targets.append({
+            "name": row.name, "family": row.family, "level": row.level,
+            "dimension_group": row.dimension_group, "dimension": row.dimension,
+            "loss_weight": row.loss_weight, "target": row.value,
+            "estimate_initial": float(est0[i]), "estimate": float(est1[i]),
+            "rel_error_initial": float(rel0[i]), "rel_error": float(rel1[i]),
+            "supported": bool(supported[i]),
+        })
+    # ACS-native evaluation and report-only measures from row columns.
+    acs_eval = []
+    household_rows = ~selected["group_quarters"].astype(bool).to_numpy()
+    measures_row = {
+        "households": household_rows.astype(float),
+        "owner": selected["owner"].to_numpy() * household_rows,
+        "renter": selected["renter"].to_numpy() * household_rows,
+        "rent_contract_annual_aggregate": selected["rent_annual"].to_numpy() * household_rows,
+        "rent_gross_annual_aggregate": selected["rent_annual"].to_numpy() * household_rows,
+        "real_estate_taxes_aggregate": selected["real_estate_taxes"].to_numpy() * household_rows * selected["owner"].to_numpy(),
+        "persons_in_households": selected["persons"].to_numpy() * household_rows,
+        "persons_total": selected["persons"].to_numpy(),
+        "persons_0_17": selected["persons_0_17"].to_numpy(),
+        "persons_18_64": selected["persons_18_64"].to_numpy(),
+        "persons_65p": selected["persons_65p"].to_numpy(),
+        "persons_male": selected["persons_male"].to_numpy(),
+    }
+    geo_cols = {"national": np.zeros(len(selected), np.int64),
+                "state": selected["state_fips"].to_numpy(np.int64),
+                "cd": selected["cd_geoid"].to_numpy(np.int64),
+                "county": selected["county_fips"].to_numpy(np.int64)}
+    for level, codes in geo_cols.items():
+        level_truth = truth[truth["level"] == level]
+        uniq, inverse = np.unique(codes, return_inverse=True)
+        for measure, vec in measures_row.items():
+            t = level_truth[level_truth["measure"] == measure]
+            if t.empty:
+                continue
+            e0 = np.bincount(inverse, weights=vec * w0, minlength=len(uniq))
+            e1 = np.bincount(inverse, weights=vec * weights, minlength=len(uniq))
+            est0_map = dict(zip(uniq, e0, strict=True))
+            est1_map = dict(zip(uniq, e1, strict=True))
+            for geo_code, value in zip(t["geo"], t["value"], strict=True):
+                if (level == "cd" and args.product == "local" and measure == "persons_in_households"):
+                    continue  # trained
+                acs_eval.append((level, measure, int(geo_code), float(value),
+                                 float(est0_map.get(geo_code, 0.0)), float(est1_map.get(geo_code, 0.0))))
+    acs_eval = pd.DataFrame(acs_eval, columns=["level", "measure", "geo", "truth", "estimate_initial", "estimate"])
+    report = {}
+    # Census puts ACS group quarters outside the SPM universe; report the
+    # rates over household rows only.
+    in_universe = ~selected["group_quarters"].astype(bool).to_numpy()
+    for name in REPORT_SPECS:
+        measure = f"bakeoff_{name.replace('.', '_')}"
+        if measure in measure_col:
+            col = values[:, measure_col[measure]].toarray().ravel() * in_universe
+            report[name] = {"initial": float(col @ w0), "calibrated": float(col @ weights)}
+    persons = selected["persons"].to_numpy() * in_universe
+    children = selected["persons_0_17"].to_numpy() * in_universe
+    if "report.spm_poor_persons" in report:
+        report["spm_rate"] = {k: report["report.spm_poor_persons"][k] / float(persons @ w) for k, w in (("initial", w0), ("calibrated", weights))}
+        report["spm_child_rate"] = {k: report["report.spm_poor_children"][k] / float(children @ w) for k, w in (("initial", w0), ("calibrated", weights))}
+    # ---------------- ESS and receipt
+    units = selected["unit_key"].to_numpy()
+    source_units = (selected["source_key"].astype(str) + "#" + selected["copy"].astype(str)).to_numpy()
+    cd_ess = []
+    for _cd, idx in pd.Series(np.arange(len(selected))).groupby(selected["cd_geoid"].to_numpy()):
+        cd_ess.append(distinct_unit_ess(weights[idx.to_numpy()], units[idx.to_numpy()]))
+    receipt = {
+        "arm": arm.label, "product": args.product, "scored_all_levels": True,
+        "rescored": bool(rescore),
+        "counts": dataclasses.asdict(counts),
+        "rows": {"cps_physical": int(len(cps_expanded)), "acs": int(len(acs_rows)), "total": int(len(selected))},
+        "distinct_households": int(pd.Series(units).nunique()),
+        "ess": {
+            "rows_initial": kish_ess(w0), "rows": kish_ess(weights),
+            "distinct_households_initial": distinct_unit_ess(w0, units),
+            "distinct_households": distinct_unit_ess(weights, units),
+            "source_records": distinct_unit_ess(weights, source_units),
+            "cd_distinct_median": float(np.median(cd_ess)), "cd_distinct_p10": float(np.percentile(cd_ess, 10)),
+        },
+        "train": {"targets": int(a_train.shape[0]), "cd_population_targets": extra_train,
+                  "nnz": int(a_train.nnz),
+                  "loss_initial": float(trajectory[0]), "loss_final": float(trajectory[-1]),
+                  "loss_at": (previous["train"]["loss_at"] if rescore else
+                              {str(e): float(trajectory[min(e, len(trajectory)) - 1]) for e in (100, 250, 500, 1000, len(trajectory))})},
+        "holdout_targets": result_targets,
+        "acs_native": acs_eval.to_dict(orient="records"),
+        "report_only": report,
+        "weights": {"mass": float(weights.sum()), "population_mass_factor": mass_factor, "max_ratio": float((weights / w0).max()),
+                    "min_ratio": float((weights / w0).min())},
+        "runtime": (
+            {**previous["runtime"], "rescore_wall_s": round(time.time() - started, 1),
+             "rescore_peak_rss_gb": round(peak_rss_gb(), 2)}
+            if rescore else
+            {"wall_s": round(time.time() - started, 1), "solve_s": round(solve_s, 1),
+             "epochs": args.epochs, "threads": args.threads, "peak_rss_gb": round(peak_rss_gb(), 2)}
+        ),
+        "inputs": previous["inputs"] if rescore else inputs,
+        "rows_digest": rows_digest,
+    }
+    write_json(out, receipt)
+    np.save(out.with_suffix(".weights.npy"), weights.astype(np.float32))
+    log(f"{args.product} {arm.label}: rows {len(selected):,} train {a_train.shape[0]} "
+        f"loss {trajectory[0]:.4f}->{trajectory[-1]:.4f} solve {solve_s:.0f}s peak {peak_rss_gb():.1f} GB")
+
+
+def do_run_grid(args) -> None:
+    """Run every missing (arm, product) receipt, one subprocess each."""
+    import subprocess
+
+    arms = [a.label for a in arm_grid(not args.no_largest, args.newest_year)]
+    digests = input_digests(args.work, args.truth, args.epochs)
+    if args.only:
+        arms = [a for a in arms if any(token in a for token in args.only.split(","))]
+    failed = []
+    for product in args.products.split(","):
+        for label in arms:
+            out = args.work / "arms" / product / f"{label}.json"
+            if out.exists():
+                receipt = json.loads(out.read_text())
+                current = arm_inputs(digests, parse_arm(label))
+                if receipt.get("scored_all_levels") and inputs_current(receipt.get("inputs"), current):
+                    continue
+            wait_for_memory(args.min_available_gb)
+            command = [sys.executable, str(Path(__file__).resolve()), "arm", "--work", str(args.work),
+                       "--arm", label, "--product", product, "--truth", str(args.truth),
+                       "--epochs", str(args.epochs), "--threads", str(args.threads),
+                       "--min-available-gb", str(args.min_available_gb)]
+            log(f"run {product} {label}")
+            result = subprocess.run(command, check=False)
+            if result.returncode:
+                log(f"FAILED {product} {label}: exit {result.returncode}")
+                failed.append(f"{product} {label}")
+    if failed:
+        raise SystemExit(f"{len(failed)} arms failed: {', '.join(failed)}")
+
+
+# ------------------------------------------------------------------ report
+
+
+def do_report(args) -> None:
+    records, acs_records, summary = [], [], []
+    expected = {(product, arm.label) for product in ("national", "local") for arm in arm_grid(True, args.newest_year)}
+    digests = input_digests(args.work, args.truth, args.epochs)
+    stale, stale_keys = [], set()
+    for path in sorted((args.work / "arms").glob("*/*.json")):
+        receipt = json.loads(path.read_text())
+        if (receipt["product"], receipt["arm"]) not in expected:
+            log(f"report: skipping {receipt['product']} {receipt['arm']} (not in the {args.newest_year} grid)")
+            continue
+        arm = parse_arm(receipt["arm"])
+        if not inputs_current(receipt.get("inputs"), arm_inputs(digests, arm)):
+            stale.append(f"{receipt['product']} {receipt['arm']}")
+            stale_keys.add((receipt["product"], receipt["arm"]))
+            continue
+        base = {"arm": receipt["arm"], "product": receipt["product"]}
+        base.update({"budget": arm.budget or 0, "cps_years": len(arm.cps_income_years),
+                     "acs_fill_share": arm.acs_fill_share, "seed": arm.seed})
+        for t in receipt["holdout_targets"]:
+            records.append({**base, **t})
+        for a in receipt["acs_native"]:
+            acs_records.append({**base, **a})
+        loss_at = receipt["train"].get("loss_at", {})
+        summary.append({**base, **receipt["counts"], **{f"rows_{k}": v for k, v in receipt["rows"].items()},
+                        "rescored": bool(receipt.get("rescored")),
+                        "loss_1000": loss_at.get("1000"), "loss_final": receipt["train"]["loss_final"],
+                        "distinct_households": receipt["distinct_households"],
+                        **{f"ess_{k}": v for k, v in receipt["ess"].items()},
+                        "train_loss_final": receipt["train"]["loss_final"],
+                        "train_targets": receipt["train"]["targets"],
+                        **{f"runtime_{k}": v for k, v in receipt["runtime"].items()},
+                        **{f"report_{k}": (v["calibrated"] if isinstance(v, dict) else v)
+                           for k, v in receipt["report_only"].items()}})
+    if stale and not args.allow_stale:
+        raise SystemExit(f"{len(stale)} receipts are stale against the current inputs (e.g. {stale[:3]}); "
+                         "re-solve them or pass --allow-stale to leave them out")
+    if stale:
+        log(f"report: leaving out {len(stale)} stale receipts (e.g. {stale[:3]})")
+    # A stale receipt that --allow-stale leaves out still exists, so it does
+    # not count as missing.
+    missing = sorted(expected - {(r["product"], r["arm"]) for r in summary} - stale_keys)
+    if missing and not args.allow_missing:
+        raise SystemExit(f"{len(missing)} expected receipts missing (e.g. {missing[:3]}); "
+                         "pass --allow-missing for a partial report")
+    holdout = pd.DataFrame(records)
+    acs_native = pd.DataFrame(acs_records)
+    arms = pd.DataFrame(summary)
+    args.out.mkdir(parents=True, exist_ok=True)
+    arms.to_csv(args.out / "arms.csv", index=False)
+    if not holdout.empty:
+        holdout["capped"] = holdout["rel_error"].clip(upper=1.0)
+        holdout["capped_initial"] = holdout["rel_error_initial"].clip(upper=1.0)
+        # Unsupported targets (no row can move them) are scored as misses
+        # (estimate 0 -> capped error 1), so every arm is averaged over the
+        # same held-out set; an intersection table is written as well.
+        supported_everywhere = holdout.groupby(["product", "name"])["supported"].transform("all")
+        holdout[supported_everywhere].groupby(
+            ["product", "arm", "dimension_group", "dimension", "level"]
+        ).agg(targets=("capped", "size"), capped_mean=("capped", "mean")).reset_index().to_csv(
+            args.out / "holdout_by_dimension_level_common_support.csv", index=False)
+        grouped = holdout.groupby(
+            ["product", "arm", "dimension_group", "dimension", "level"])
+        table = grouped.apply(lambda g: pd.Series({
+            "targets": len(g),
+            "capped_mean": g["capped"].mean(),
+            "weighted_capped_mean": np.average(g["capped"], weights=g["loss_weight"]),
+            "median_rel": g["rel_error"].median(),
+            "capped_mean_initial": g["capped_initial"].mean(),
+            "unsupported": int((~g["supported"]).sum()),
+        })).reset_index()
+        table.to_csv(args.out / "holdout_by_dimension_level.csv", index=False)
+        unsupported = holdout[~holdout["supported"]].groupby(["product", "arm"]).size()
+        unsupported.to_csv(args.out / "holdout_unsupported.csv")
+    if not acs_native.empty:
+        acs_native["rel"] = (acs_native["estimate"] - acs_native["truth"]).abs() / acs_native["truth"].clip(lower=1.0)
+        acs_native["rel_initial"] = (acs_native["estimate_initial"] - acs_native["truth"]).abs() / acs_native["truth"].clip(lower=1.0)
+        acs_table = acs_native.groupby(["product", "arm", "level", "measure"]).agg(
+            geos=("geo", "size"), mean_rel=("rel", lambda s: s.clip(upper=1.0).mean()),
+            median_rel=("rel", "median"), mean_rel_initial=("rel_initial", lambda s: s.clip(upper=1.0).mean()),
+        ).reset_index()
+        acs_table.to_csv(args.out / "acs_native_by_level_measure.csv", index=False)
+    if not holdout.empty and not acs_native.empty:
+        _write_headline(args.out, arms, holdout, acs_native)
+        _write_dimensions(args.out, arms, holdout, acs_native)
+    log(f"report: {len(arms)} arm receipts")
+
+
+DIMENSION_COLUMNS = (
+    ("income_components", "state"), ("income_components", "cd"),
+    ("tax_items", "state"), ("tax_items", "cd"),
+    ("program_receipt", "state"), ("demographics_pep", "state"),
+)
+
+
+def _write_dimensions(out: Path, arms: pd.DataFrame, holdout: pd.DataFrame, acs_native: pd.DataFrame) -> None:
+    """Per-dimension held-out error (seed-0 arms; replicate spread alongside)."""
+    by_dim = holdout.groupby(["product", "arm", "dimension", "level"])["capped"].mean()
+    # The release's own objective: loss-weighted capped error over every
+    # CPS-native held-out target at a level (fixed full-surface weights).
+    native = holdout[holdout["dimension_group"] == "cps_native"]
+    weighted = native.groupby(["product", "arm", "level"]).apply(
+        lambda g: np.average(g["capped"], weights=g["loss_weight"]))
+    acs = acs_native[acs_native["measure"].isin(HEADLINE_ACS_MEASURES)].copy()
+    acs["capped"] = acs["rel"].clip(upper=1.0)
+    acs = acs.groupby(["product", "arm", "level"])["capped"].mean()
+    rows = []
+    for record in arms.itertuples(index=False):
+        row = {"product": record.product, "arm": record.arm, "budget": record.budget,
+               "cps_years": record.cps_years, "acs_fill_share": record.acs_fill_share,
+               "seed": record.seed, "rows": record.rows_total,
+               "ess_distinct": record.ess_distinct_households}
+        for level in ("state", "cd"):
+            row[f"cps_native_weighted.{level}"] = weighted.get((record.product, record.arm, level), np.nan)
+        for dimension, level in DIMENSION_COLUMNS:
+            row[f"{dimension}.{level}"] = by_dim.get((record.product, record.arm, dimension, level), np.nan)
+        for level in ("state", "cd", "county"):
+            row[f"acs_native.{level}"] = acs.get((record.product, record.arm, level), np.nan)
+        rows.append(row)
+    table = pd.DataFrame(rows).sort_values(["product", "budget", "cps_years", "acs_fill_share", "seed"])
+    table.to_csv(out / "dimensions_by_arm.csv", index=False)
+    spread, mean = replicate_summary(table)
+    spread.to_csv(out / "replicate_spread.csv")
+    mean.to_csv(out / "replicate_mean.csv")
+
+
+def replicate_summary(table: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """SD and mean over ACS draws of the 300k ACS-fill arms, per product and
+    CPS year count; groups with a single draw are left out."""
+    replicate = table[table["arm"].str.contains("b300k") & table["acs_fill_share"].eq(1.0) & table["cps_years"].gt(0)]
+    measures = [c for c in table.columns if "." in c]
+    seeded = [key for key, g in replicate.groupby(["product", "cps_years"]) if g["seed"].nunique() > 1]
+    replicate = replicate.set_index(["product", "cps_years"]).loc[seeded].reset_index()
+    groups = replicate.groupby(["product", "cps_years"])[measures]
+    return groups.std(), groups.mean()
+
+
+#: Household-universe measures only: the age bands count group-quarters people
+#: in both the truth and the ACS rows, and CPS rows have none, which would tilt
+#: the average toward ACS arms. Age bands stay in acs_native_by_level_measure.csv.
+HEADLINE_ACS_MEASURES = (
+    "households", "owner", "renter", "rent_contract_annual_aggregate",
+    "real_estate_taxes_aggregate", "persons_in_households",
+)
+
+
+def _write_headline(out: Path, arms: pd.DataFrame, holdout: pd.DataFrame, acs_native: pd.DataFrame) -> None:
+    """One markdown table per product: held-out error by dimension and level."""
+    cps = holdout[holdout["dimension_group"] == "cps_native"].groupby(["product", "arm", "level"])["capped"].mean()
+    pep = holdout[holdout["dimension_group"] == "acs_native"].groupby(["product", "arm"])["capped"].mean()
+    acs = acs_native[acs_native["measure"].isin(HEADLINE_ACS_MEASURES)].copy()
+    acs["capped"] = acs["rel"].clip(upper=1.0)
+    acs = acs.groupby(["product", "arm", "level"])["capped"].mean()
+    lines = []
+    for product, frame in arms.sort_values(["budget", "cps_years", "acs_fill_share", "seed"]).groupby("product", sort=False):
+        lines += [f"### {product} product", "",
+                  "| Arm | Rows | Distinct hh | ESS (distinct) | CPS-native nat | state | CD | PEP demog | ACS-native state | CD | county | Solve min | Peak GB |",
+                  "|---|---|---|---|---|---|---|---|---|---|---|---|---|"]
+        for row in frame.itertuples(index=False):
+            key = (product, row.arm)
+
+            def cell(series, level=None, key=key):
+                index = (*key, level) if level else key
+                return f"{series.get(index, float('nan')):.3f}"
+
+            lines.append(
+                f"| {row.arm} | {row.rows_total:,} | {row.distinct_households:,} | {row.ess_distinct_households:,.0f} | "
+                f"{cell(cps, 'national')} | {cell(cps, 'state')} | {cell(cps, 'cd')} | {cell(pep)} | "
+                f"{cell(acs, 'state')} | {cell(acs, 'cd')} | {cell(acs, 'county')} | "
+                f"{row.runtime_solve_s / 60:.1f} | {row.runtime_peak_rss_gb:.1f} |"
+            )
+        lines.append("")
+    (out / "headline.md").write_text("\n".join(lines))
+
+
+# ------------------------------------------------------------------ main
+
+
+def main(argv=None) -> None:
+    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    sub = parser.add_subparsers(dest="command", required=True)
+    c = sub.add_parser("compile")
+    c.add_argument("--feed", type=Path, required=True)
+    c.add_argument("--out", type=Path, required=True)
+    s = sub.add_parser("shard")
+    s.add_argument("--source", choices=("cps", "acs"), required=True)
+    s.add_argument("--h5", type=Path, required=True)
+    s.add_argument("--design-h5", type=Path)
+    s.add_argument("--out", type=Path, required=True)
+    s.add_argument("--households-per-shard", type=int, default=50_000)
+    s.add_argument("--min-available-gb", type=float, default=20.0)
+    m = sub.add_parser("materialize")
+    m.add_argument("--work", type=Path, required=True)
+    m.add_argument("--source", choices=("cps", "acs"), required=True)
+    m.add_argument("--summary", type=Path)
+    m.add_argument("--batch", type=int, default=5_000)
+    m.add_argument("--only")
+    m.add_argument("--rank-range", help="ACS only: materialize rows whose selection rank is in [LO,HI)")
+    m.add_argument("--min-available-gb", type=float, default=20.0)
+    m.add_argument("--replace-stale", action="store_true",
+                   help="delete and rebuild parts not built from the current shards' source h5")
+    pc = sub.add_parser("partcheck")
+    pc.add_argument("--work", type=Path, required=True)
+    pc.add_argument("--source", choices=("cps", "acs"), required=True)
+    pc.add_argument("--shards", type=Path, help="shard pickles cut from the same h5 (default: the work directory's)")
+    pc.add_argument("--summary", type=Path)
+    pc.add_argument("--parts", help="comma-separated part stems (default: every part)")
+    pc.add_argument("--per-part", type=int, default=200)
+    pc.add_argument("--tolerance", type=float, default=1e-5)
+    pc.add_argument("--batch", type=int, default=5_000)
+    pc.add_argument("--min-available-gb", type=float, default=20.0)
+    d = sub.add_parser("diffcheck")
+    d.add_argument("--work", type=Path, required=True)
+    d.add_argument("--feed", type=Path, required=True)
+    d.add_argument("--summary", type=Path, required=True)
+    d.add_argument("--households", type=int, default=4_000)
+    d.add_argument("--per-group", type=int, default=40)
+    d.add_argument("--batch", type=int, default=5_000)
+    d.add_argument("--sources", default="cps,acs")
+    t = sub.add_parser("truth")
+    t.add_argument("--sf-dir", type=Path, required=True)
+    t.add_argument("--out", type=Path, required=True)
+    a = sub.add_parser("arm")
+    a.add_argument("--work", type=Path, required=True)
+    a.add_argument("--arm", required=True)
+    a.add_argument("--product", choices=("national", "local"), required=True)
+    a.add_argument("--truth", type=Path, required=True)
+    a.add_argument("--epochs", type=int, default=1_500)
+    a.add_argument("--threads", type=int, default=6)
+    a.add_argument("--exclude-gq", action="store_true")
+    a.add_argument("--force", action="store_true", help="re-solve even if a receipt exists")
+    a.add_argument("--rescore", action="store_true", help="re-score saved weights without re-solving")
+    a.add_argument("--min-available-gb", type=float, default=20.0)
+    g = sub.add_parser("grid")
+    g.add_argument("--no-largest", action="store_true")
+    g.add_argument("--newest-year", type=int, default=NEWEST_INCOME_YEAR)
+    rg = sub.add_parser("run-grid")
+    rg.add_argument("--work", type=Path, required=True)
+    rg.add_argument("--truth", type=Path, required=True)
+    rg.add_argument("--products", default="national,local")
+    rg.add_argument("--epochs", type=int, default=1_500)
+    rg.add_argument("--threads", type=int, default=6)
+    rg.add_argument("--only")
+    rg.add_argument("--no-largest", action="store_true")
+    rg.add_argument("--newest-year", type=int, default=NEWEST_INCOME_YEAR,
+                    help="newest CPS income year in rows_cps; arms use the newest 1, 2 and 3 years")
+    rg.add_argument("--min-available-gb", type=float, default=20.0)
+    f = sub.add_parser("spm-flags")
+    f.add_argument("--work", type=Path, required=True)
+    r = sub.add_parser("report")
+    r.add_argument("--work", type=Path, required=True)
+    r.add_argument("--out", type=Path, required=True)
+    r.add_argument("--newest-year", type=int, default=NEWEST_INCOME_YEAR)
+    r.add_argument("--allow-missing", action="store_true", help="write a report even if receipts are missing")
+    r.add_argument("--truth", type=Path, required=True)
+    r.add_argument("--epochs", type=int, default=1_500)
+    r.add_argument("--allow-stale", action="store_true", help="leave stale receipts out instead of refusing")
+    args = parser.parse_args(argv)
+    {
+        "compile": do_compile, "shard": do_shard, "materialize": do_materialize,
+        "diffcheck": do_diffcheck, "truth": do_truth, "arm": do_arm, "report": do_report,
+        "run-grid": do_run_grid, "spm-flags": do_spm_flags, "partcheck": do_partcheck,
+        "grid": lambda a: print("\n".join(arm.label for arm in arm_grid(not a.no_largest, a.newest_year))),
+    }[args.command](args)
+
+
+if __name__ == "__main__":
+    main()