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.
MetaGPT: Meta program- ming for a multi-agent collaborative framework
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Enforcing role separation in agent teams reveals that prompt-only setups hide coordination failures, with verifiers approving 49% of failing work and teams sometimes harming performance when solo agents already succeed.
citing papers explorer
-
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.
-
TeamBench: Evaluating Agent Coordination under Enforced Role Separation
Enforcing role separation in agent teams reveals that prompt-only setups hide coordination failures, with verifiers approving 49% of failing work and teams sometimes harming performance when solo agents already succeed.