LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation

Researchers have developed LLM4Cov, an offline learning framework that enables AI agents to generate high-coverage hardware verification testbenches without expensive online reinforcement learning. A compact 4B-parameter model achieved 69.2% coverage pass rate, outperforming larger models by demonstrating efficient learning from execution feedback in hardware verification tasks.