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

Long-horizon LLM agents often operate in a 'quiet' failure mode where they settle on a specific interpretation of evidence early in a multi-step process and spend the remainder of the run defending that initial, potentially flawed, decision. The author investigates whether this 'premature commitment' leaves a measurable signature in the model's internal hidden states, and whether this signature can be used to diagnose or influence agent behavior.

The study defines 'representational commitment' as the convergence of hidden states across multiple independent runs of the same input at a fixed reasoning step. Using a ReAct agent on benchmarks like HotpotQA and StrategyQA, the author measures pairwise cosine similarity of hidden states at specific layers and steps. The research validates this signal across multiple architectures (Llama-3.1-70B, Qwen-2.5-72B, and Phi-3-14B) and tests its utility through a runtime monitor and a prompting intervention designed to induce or reduce commitment.

• Predictive Signature: Activation similarity at intermediate steps (e.g., step 4 for Llama-3.1-70B) significantly predicts downstream behavioral consistency. This signal is robust, replicating across different model architectures and benchmarks.
• Process vs. Accuracy: The diagnostic tracks whether an agent has 'settled' on a path, not whether that path is correct. Committed-wrong and committed-correct trajectories are indistinguishable via activation similarity, confirming that the signal measures internal confidence rather than truth.
• Detection and Intervention: A runtime monitor can detect inconsistent trajectories with high AUROC (up to 0.97), and a prompting intervention can reduce behavioral variance by 28% compared to a token-matched control, though this intervention does not improve overall accuracy.
• Compute Routing: While the signal provides a useful diagnostic, it does not consistently outperform simpler, output-based baselines for routing test-time compute (e.g., self-consistency), suggesting it is a tool for process monitoring rather than a general accuracy lever.