VeriEvolve: Harness Evolution for Hardware Verification Agents

Self-improving agents need mechanisms for turning task experience into reusable improvements in reasoning and tool use. Hardware verification makes this challenge concrete: success depends on coordinating temporal reasoning, fault activation, discriminating checks, and preservation of previously supported behavior. We present VeriEvolve, a framework for EDA-guided harness evolution in multi-agent verification systems. The central idea is to make the organization of verification experiments an explicit, executable search space. An inner multi-agent system coordinates specification analysis, artifact construction, and experimental challenge, while an outer loop uses development experience to revise resource allocation, diagnostic priorities, and repair or recheck decisions. Verification-specific failure distinctions guide policy proposals, and a fixed execution kernel requires reproducible failure evidence and preservation checks before accepting repairs. This separates adaptation of a task artifact from improvement of a policy reused across designs. We instantiate the framework on stimulus generation, checker construction, assertion generation, and RTL repair in CVDP, and define matched comparisons of instruction-level and executable-policy search with selected policies frozen for transfer evaluation. The formulation treats domain structure as an inductive bias over agent policies and makes its value for self-improvement a testable question across design families and model backbones.