Evaluate correctness of LLM‑agent plans with potentially fabricated rationales

Develop interpretability and evaluation methods to determine the correctness of plans generated by large language model (LLM)–powered AI agents, specifically addressing cases where agents fabricate intermediate reasoning steps or rationales, and establish verifiable criteria for validating such plans.

Background

Within the discussion of reliability and safety for AI Agents, the paper emphasizes that LLM-based agents can fabricate intermediate steps or rationales, complicating downstream assessment of plan validity. This fabrication undermines interpretability and makes it difficult to verify agent reasoning and action sequences.

The authors highlight that, beyond hallucinations and brittleness, determining whether multi-step plans are correct is itself unresolved, particularly when internal reasoning traces cannot be trusted. This motivates research into rigorous, verifiable plan evaluation techniques for LLM-powered agents.