TimingLLM uses a fine-tuned LLM to generate structural timing cues from Verilog followed by a retrieval-augmented regressor with a learned steering vector to predict WNS and TNS with R values of 0.91 and 0.97.
Mage: A multi-agent engine for automated rtl code generation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
RTL-BenchMT is an agent-assisted framework for dynamically maintaining RTL generation benchmarks by fixing flaws and reducing overfitting in LLM-based EDA applications.
citing papers explorer
-
TimingLLM: A Two-Stage Retrieval-Augmented Framework for Pre-Synthesis Timing Prediction from Verilog
TimingLLM uses a fine-tuned LLM to generate structural timing cues from Verilog followed by a retrieval-augmented regressor with a learned steering vector to predict WNS and TNS with R values of 0.91 and 0.97.
-
RTL-BenchMT: Dynamic Maintenance of RTL Generation Benchmark Through Agent-Assisted Analysis and Revision
RTL-BenchMT is an agent-assisted framework for dynamically maintaining RTL generation benchmarks by fixing flaws and reducing overfitting in LLM-based EDA applications.