arxiv
PublishedJune 2, 2026 at 4:00 AM
—neutral
LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation
Publisher summary· verbatim
arXiv:2602.16953v3 Announce Type: replace Abstract: Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making online reinforcement learning (RL) less practical in certain scenarios. High-coverage hardware veri
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivFederatedSkill: Federated Learning for Agentic Skill Evolution16harxivToward a Modular Architecture for Embedded AI Agent Systems at the Edge16harxivA Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation16harxivAnomalies in Multivariate Time Series Benchmarks Are Mostly Univariate16hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗