diff --git a/skainet-compile/skainet-compile-hlo/src/commonTest/kotlin/sk/ainet/compile/hlo/TraceConvPoolUpsampleExportTest.kt b/skainet-compile/skainet-compile-hlo/src/commonTest/kotlin/sk/ainet/compile/hlo/TraceConvPoolUpsampleExportTest.kt
new file mode 100644
index 00000000..c4f9b1f8
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+++ b/skainet-compile/skainet-compile-hlo/src/commonTest/kotlin/sk/ainet/compile/hlo/TraceConvPoolUpsampleExportTest.kt
@@ -0,0 +1,79 @@
+package sk.ainet.compile.hlo
+
+import sk.ainet.lang.graph.DefaultGraphExecutionContext
+import sk.ainet.lang.tensor.Shape
+import sk.ainet.lang.tensor.ops.UpsampleMode
+import sk.ainet.lang.tensor.ops.VoidTensorOps
+import sk.ainet.lang.tape.toComputeGraph
+import sk.ainet.lang.types.FP32
+import kotlin.test.Test
+import kotlin.test.assertContains
+import kotlin.test.assertEquals
+import kotlin.test.assertFalse
+import kotlin.test.assertTrue
+
+/**
+ * Regression for #1219: a TRACE-mode model (the `network{}` / `sequential{}` path,
+ * i.e. `TensorOps` calls recorded through the KSP tracing wrapper — not the low-level
+ * `dag{}` DSL) using `conv2d`, `maxPool2d` and `upsample2d` exported through
+ * `toStableHlo` (which uses `StableHloConverterFactory.createBasic`) emitted an
+ * `// Unsupported op` comment for every one of those nodes and still reported success.
+ *
+ * Two things closed it: `createBasic` now registers `NeuralNetOperationsConverter`
+ * (#1248) — the "Known names" allowlist genuinely lacked convolution / pooling /
+ * upsampling on the basic factory — and conversion is strict by default, so a
+ * registry miss can no longer masquerade as a successful export. This test pins the
+ * tracer's attribute spellings (`stride`/`padding`/`dilation`/`groups` as pairs,
+ * `kernelSize`, `scale`/`mode`/`alignCorners`) against what the converter reads.
+ */
+class TraceConvPoolUpsampleExportTest {
+
+    @Test
+    fun trace_mode_conv2d_maxPool2d_upsample2d_lower_through_toStableHlo() {
+        val ctx = DefaultGraphExecutionContext.tape(baseOps = VoidTensorOps())
+
+        // NCHW input [1, 3, 8, 8]; conv weight [4, 3, 3, 3] (Cout, Cin, kH, kW); bias [4]
+        val input = ctx.fromFloatArray<FP32, Float>(Shape(1, 3, 8, 8), FP32::class, FloatArray(1 * 3 * 8 * 8))
+        val weight = ctx.fromFloatArray<FP32, Float>(Shape(4, 3, 3, 3), FP32::class, FloatArray(4 * 3 * 3 * 3))
+        val bias = ctx.fromFloatArray<FP32, Float>(Shape(4), FP32::class, FloatArray(4))
+
+        @Suppress("UNCHECKED_CAST")
+        val inputRefId = ctx.session.refOf(input as sk.ainet.lang.tensor.Tensor<*, *>).id
+
+        val (tape, result) = ctx.record {
+            // conv2d: same-padded 3x3 → [1, 4, 8, 8]
+            val conv = ctx.ops.conv2d(input, weight, bias, stride = 1 to 1, padding = 1 to 1)
+            // maxPool2d 2x2 / stride 2 → [1, 4, 4, 4]
+            val pooled = ctx.ops.maxPool2d(conv, kernelSize = 2 to 2, stride = 2 to 2)
+            // upsample2d x2 nearest → [1, 4, 8, 8]
+            ctx.ops.upsample2d(pooled, scale = 2 to 2, mode = UpsampleMode.Nearest)
+        }
+        assertEquals(listOf(1, 4, 8, 8), result!!.shape.dimensions.toList(), "upsampled shape")
+
+        val graph = tape!!.toComputeGraph(
+            synthesizeExternalInputs = true,
+            inputTensorIds = setOf(inputRefId)
+        )
+        val opNames = graph.getTopologicalOrder().map { it.operation.name }
+        assertTrue("conv2d" in opNames && "maxPool2d" in opNames && "upsample2d" in opNames,
+            "trace must record the three op names as the issue describes, got $opNames")
+
+        // The issue's exact entry point (createBasic under the default STRICT policy):
+        // under the pre-#1248 registry this threw on the first conv2d node (or, before
+        // strictness, emitted `// Unsupported op 'conv2d'` and "succeeded").
+        val module = toStableHlo(graph, functionName = "trace_conv_pool_upsample")
+        val mlir = module.content
+
+        assertFalse(mlir.contains("Unsupported op"), "no registry misses:\n$mlir")
+        assertFalse(mlir.contains("Conversion failed"), "no converter failures:\n$mlir")
+        assertFalse(mlir.contains("No output values"), "module must return a value:\n$mlir")
+
+        assertContains(mlir, "stablehlo.convolution", message = "conv2d must lower to stablehlo.convolution:\n$mlir")
+        assertContains(mlir, "reduce_window", message = "maxPool2d must lower to a reduce_window:\n$mlir")
+        // Static shapes survive the trace path end to end.
+        assertContains(mlir, "tensor<1x4x4x4xf32>", message = "pooled shape must be static:\n$mlir")
+        assertContains(mlir, "tensor<1x4x8x8xf32>", message = "conv/upsample shape must be static:\n$mlir")
+        assertFalse(mlir.contains("tensor<?"), "no dynamic shapes on a static trace:\n$mlir")
+        assertContains(mlir, "func.func @trace_conv_pool_upsample", message = "named function:\n$mlir")
+    }
+}