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

This paper introduces a diagnostic for identifying premature commitment in long-horizon LLM agents, where agents settle on a single interpretation of evidence too early in the reasoning process. The authors define representational commitment through cross-run hidden-state convergence and demonstrate its predictive power for behavioral consistency across models like Llama-3.1-70B, Qwen-2.5-72B, and Phi-3-14B, with high AUROC scores for detecting inconsistent trajectories. This research highlights a critical failure mode that impacts reasoning reliability, emphasizing the need for runtime monitoring and intervention strategies to mitigate variance without sacrificing accuracy.