AssertLLM2 introduces a benchmark of 83 designs supporting bug-prevention and bug-hunting assertion generation tasks with evaluation across syntactic, formal, coverage, and mutation-based metrics.
Spec2Assertion: Automatic pre- RTL assertion generation using large language models with progressive regularization
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Arcane reduces hardware assertion counts by up to 76.2% via two-tier semantic clustering and MCTS-guided rule exploration while preserving formal coverage and mutation detection, yielding 2.6x-6.1x simulation speedups.
AgileAssert identifies top critical signals via hybrid scoring on RTL graphs and uses structure-aware slicing to let LLMs generate targeted assertions, cutting assertion count by 66.68% and token use by 64% while matching or exceeding prior coverage and error detection.
CoverAssert iteratively improves LLM-generated assertions via syntax-semantic clustering and coverage feedback, yielding 9.57% branch, 9.64% statement, and 15.69% toggle coverage gains on four open-source designs when combined with prior tools.
citing papers explorer
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AssertLLM2: A Comprehensive LLM Benchmark for Assertion Generation from Design Specifications
AssertLLM2 introduces a benchmark of 83 designs supporting bug-prevention and bug-hunting assertion generation tasks with evaluation across syntactic, formal, coverage, and mutation-based metrics.
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Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring
Arcane reduces hardware assertion counts by up to 76.2% via two-tier semantic clustering and MCTS-guided rule exploration while preserving formal coverage and mutation detection, yielding 2.6x-6.1x simulation speedups.
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From Indiscriminate to Targeted: Efficient RTL Verification via Functionally Key Signal-Driven LLM Assertion Generation
AgileAssert identifies top critical signals via hybrid scoring on RTL graphs and uses structure-aware slicing to let LLMs generate targeted assertions, cutting assertion count by 66.68% and token use by 64% while matching or exceeding prior coverage and error detection.
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CoverAssert: Iterative LLM Assertion Generation Driven by Functional Coverage via Syntax-Semantic Representations
CoverAssert iteratively improves LLM-generated assertions via syntax-semantic clustering and coverage feedback, yielding 9.57% branch, 9.64% statement, and 15.69% toggle coverage gains on four open-source designs when combined with prior tools.