arXiv AI
NoTB: Oracle-Free Triage of LLM-Generated RTL via Cross-Model Formal Consensus
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arXiv AI
Jun 9
RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models
arXiv:2606. 08976v1 Announce Type: new Abstract: LLM-based RTL generation and reasoning is a promising direction for hardware design automation.
arXiv Machine Learning
Sep 10
Robustness of LLM-Generated SystemVerilog Assertions to Semantics-Preserving RTL Transformations
The paper evaluates how robust large language models are at generating SystemVerilog Assertions (SVA) when the underlying RTL code undergoes semantics‑preserving transformations such as operand reordering, identifier renaming, and redundant parenthesization. Using a curated dataset and two open‑source models (Qwen2.5‑Coder‑7B and DeepSeek‑Coder‑V2‑Lite), the authors find that 9.7%–27.0% of behaviors that were correct on the original RTL become incorrect after transformation, revealing significant instability that aggregate accuracy metrics can hide.
arXiv Machine Learning
Jun 5
TLA-Prover: Verifiable TLA+ Specification Synthesis via Preference-Optimized Low-Rank Adaptation
arXiv:2606. 06133v1 Announce Type: cross Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols.
arXiv Machine Learning
Jun 5
CASS-RTL: Correctness-Aware Subspace Steering for RTL Generation with LLMs
arXiv:2606. 05680v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the automatic synthesis (generation) of register-transfer level (RTL) code from natural language instructions, offering a promising pathway to accelerate chip design.
arXiv AI
Jun 12
Structured Testbench Generation for LLM-Driven HDL Design and Verification-Oriented Data Curation
arXiv:2606. 12983v1 Announce Type: new Abstract: Automated testbench generation has become a critical bottleneck in large language model (LLM)-driven Register Transfer Level (RTL) workflows, where large numbers of candidate designs must be verified rapidly and reliably.
arXiv AI
Jun 12
Cross-Model Disagreement as a Label-Free Correctness Signal
arXiv:2603. 25450v2 Announce Type: replace Abstract: Detecting when a language model is wrong without ground truth labels is a fundamental challenge for safe deployment.