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arXiv AI By Aman Mehta, Anupam Datta

When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents

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The paper introduces representational commitment, a metric that quantifies hidden‑state similarity across repeated runs of a tool‑using LLM agent at the same step. Experiments on Llama‑3.1‑70B with ReAct on HotpotQA and other models show that higher step‑4 similarity correlates with lower overall trajectory variability, indicating when agent behavior stabilizes. The study also demonstrates that this metric captures stability rather than correctness, and explores monitoring and intervention techniques that can predict and reduce trajectory variability, though they do not significantly improve accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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