LLM Agent Verification
LLM: Large Language Model
Overview
LLM agent verification studies how to establish that an agent did the right thing, followed required procedures, and has evidence for its claims—not merely that it produced a plausible answer. Common approaches include execution-grounded tests and replay, formal methods such as contracts and SMT checks, runtime policy monitors, and learned or independent verifiers that inspect trajectories and tool evidence. These methods are evaluated in settings such as coding and computer-use benchmarks, multi-step tool workflows, formal mathematics and hardware design, and high-stakes domains. A central tension is that checks can improve reliability but only for the requirements and evidence they cover; self-verification and LLM judges can miss errors, while stronger verification adds cost and still does not prove claims beyond its scope.
Latest papers
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