RTL 修复基准到底测了什么？RTLRepairBench 揭示两项测量混淆

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Abstract:RTL repair benchmarks should measure whether a model can fix broken RTL given a specification. To this end, we construct and hash-bind RTLRepairDataset, 5,784 (broken, fixed) training pairs; RTLRepairBench, 459 held-out cases with 374 syntactic and 85 behavioral mutations; and an evaluation harness supporting simulation and formal equivalence. We then use this infrastructure to identify two measurement confounds. First, simulation and formal scoring give opposite signs for the apparent effect of providing a specification in one evaluated cohort; reset-aware rescoring attributes almost all of the simulation-formal disagreement to reset and first-cycle comparison semantics. Second, on 274 mined model failures, the specification-only minus specification-conditioned recovery point estimate ranges from -8.5 to +18.3 percentage points across six models. Thus, a conventional repair score need not isolate whether the model used the faulty RTL. We translate these findings into evaluation controls: report initialization, reset, first-cycle, and unknown-value semantics; disclose oracle coverage and undecided cases; and include a specification-only baseline. Together, the resources and audit provide both a benchmark for behavioral repair and a protocol for diagnosing what contributes to its measured score. We release the benchmark, the training set, construction code, sanitized aggregate evidence, and reproduction materials at this https URL.

Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2610.28595 [cs.AI]
	(or arXiv:2610.28595v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.28595 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yeyin Zhu [view email]
[v1] Tue, 20 Oct 2026 17:45:57 UTC (135 KB)