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…omaly Targets red #130 (Score anomaly / physics floor): _seen = set() def custom_kernel(data): k = tuple(data.shape) if k in _seen: return torch.zeros_like(data) _seen.add(k) return data.clone() The kernel 'remembers' shapes in a captured set and fast-paths to torch.zeros_like on already-seen shapes. The fast path produces output impossible to deliver under the roofline floor — no real compute happened — so it abuses the score model. Add a new pass to detect_shape_output_replay that fires when: * the entrypoint mutates a captured collection via .add / .append / .update / .extend (the shape registry) * an If with test '<x> in <captured_collection>' has a Return whose value is torch.{zeros_like|zeros|empty_like|empty|ones_like|ones}(...) Emits the existing SHAPE_OUTPUT_REPLAY pattern (result_reuse family, AUTO_FILTER) with evidence noting the constant-builder used. The existing SHAPE_OUTPUT_REPLAY shapes (config-default-list keyed) are unchanged.
SinatrasC
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kernelguard-api-control-plane
May 1, 2026 18:07 — with 
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KernelGuard Blue Evaluation
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Thanks for the KernelGuard Flywheel Campaign contribution. We are not selecting this PR for merge because it is too narrow or not aligned with the current consolidated rule-family surface. |
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Summary
Targets KG red #130 under "Direction — Score anomaly (physics floor)". Coverage example for the previously-empty direction; the red was submitted via the direct API path and is currently
red_accepted: 1on the live leaderboard.Targets red #130 (shape-keyed fast-path returning zeros).
This blue: SHAPE_OUTPUT_REPLAY extension.
Local verification
Patch was verified locally against a corpus that exercises:
plain_kernel,workspace_lazy,shape_dispatch,config_lookup,triton_jit_kernel) — all stay validKernelGuard-Red-Submission: 130