ProofLoop achieves 93.7% syntax correctness and 82.0% functional correctness for SVA generation from natural language by combining retrieval, EDA tools, and up to three rounds of JasperGold formal feedback.
Rtl++: Graph-enhanced llm for rtl code generation,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SafeTune uses GNN-based structural anomaly detection and semantic prompt classification to filter poisoned data in LLM fine-tuning for RTL generation, enhancing robustness against hardware Trojan insertion without altering the base model.
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From Language to Logic: Bridging LLMs & Formal Representations for RTL Assertion Generation
ProofLoop achieves 93.7% syntax correctness and 82.0% functional correctness for SVA generation from natural language by combining retrieval, EDA tools, and up to three rounds of JasperGold formal feedback.
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SafeTune: Mitigating Data Poisoning in LLM Fine-Tuning for RTL Code Generation
SafeTune uses GNN-based structural anomaly detection and semantic prompt classification to filter poisoned data in LLM fine-tuning for RTL generation, enhancing robustness against hardware Trojan insertion without altering the base model.