LongRTL proposes a three-agent LLM system guided by graph similarity on ASTs to partition, optimize via multi-modal RAG, and reconstruct long RTL designs for functional equivalence.
Scalertl: Scaling llms with reasoning data and test-time compute for accurate rtl code generation,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CASS-RTL identifies correctness-linked attention heads, builds a steering subspace from them, and applies a geometry-aware intervention that raises pass@1/5/10 accuracy 10-20% on VerilogEval and 5% on CVDP across multiple LLMs without retraining or extra labels.
HORIZON applies repository-level self-evolution to hardware design artifacts and reports 100% completion on ChipBench, RTLLM, Verilog-Eval, and nine CVDP categories using a hands-free agent loop.
citing papers explorer
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LongRTL: Graph-Similarity-Guided LLM-driven Long Context RTL Optimization
LongRTL proposes a three-agent LLM system guided by graph similarity on ASTs to partition, optimize via multi-modal RAG, and reconstruct long RTL designs for functional equivalence.
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CASS-RTL: Correctness-Aware Subspace Steering for RTL Generation with LLMs
CASS-RTL identifies correctness-linked attention heads, builds a steering subspace from them, and applies a geometry-aware intervention that raises pass@1/5/10 accuracy 10-20% on VerilogEval and 5% on CVDP across multiple LLMs without retraining or extra labels.
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Agentic Hardware Design as Repository-Level Code Evolution
HORIZON applies repository-level self-evolution to hardware design artifacts and reports 100% completion on ChipBench, RTLLM, Verilog-Eval, and nine CVDP categories using a hands-free agent loop.