A self-trained multi-agent RL framework pairs Verilog and Python agents for oracle-free mutual verification in RTL generation and reports 75.0% / 80.1% pass@1 on VerilogEval V2 using 4B / 9B models.
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Successor-representation spectra of row-stochastic communication operators predict perturbation robustness, consensus speed, and error accumulation in multi-agent LLM topologies, with condition number showing perfect empirical rank correlation.
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ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
A self-trained multi-agent RL framework pairs Verilog and Python agents for oracle-free mutual verification in RTL generation and reports 75.0% / 80.1% pass@1 on VerilogEval V2 using 4B / 9B models.
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Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies
Successor-representation spectra of row-stochastic communication operators predict perturbation robustness, consensus speed, and error accumulation in multi-agent LLM topologies, with condition number showing perfect empirical rank correlation.