When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents
Tool-using LLM agents make early choices about what to search for, which evidence to trust, and what to do next. A wrong early choice can steer every later tool call, so an agent may produce a coherent but incorrect result. Existing evaluation does not reveal this process: final-answer scoring comes only after completion, and agreement across runs can be high even when every run is wrong. We ask whether hidden states reveal that repeated runs have already settled into stable behavior. Our contributions are threefold. First, we introduce representational commitment, which measures hidden-state similarity across repeated runs at the same step. On Llama-3.1-70B running ReAct on HotpotQA, we ...
| Authors | Aman Mehta, Anupam Datta |
|---|---|
| Primary category | cs.AI |
| Published | 22 June 2026 |
| Last updated at source | 12 October 2026 |
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