Zhe Ye

BenchShield is presented, a model-backed instrumentation layer for reward integrity in LLM-agent evaluation that grounds detection in a finite lifecycle model of an evaluation's reward-relevant events and achieves 96% accuracy in detecting reward hacking from infrastructure-side evidence.

AI agents can only correctly synthesize implementations and proofs for less than two-thirds of tested multi-module software repositories, revealing significant gaps in current capabilities.