AI summaryⓘ
The authors studied why large language model (LLM) agents sometimes get stuck early on a single interpretation of information and keep defending it, a problem they call premature commitment. They measure this by looking at how similar the models' hidden internal states are across different runs at a certain reasoning step, which they name representational commitment. Their findings show that this commitment can predict if the model will stick to a consistent reasoning path but does not indicate whether the final answer is correct. They also created a monitoring tool to detect inconsistent reasoning paths and a prompt-based method to reduce variability without lowering accuracy. However, using this signal to guide self-consistency checking only helped a little, suggesting it’s mainly useful as a diagnostic rather than a way to improve accuracy generally.
long-horizon LLM agentspremature commitmenthidden-state convergencerepresentational commitmentLlama-3ReAct methodHotpotQAbehavioral consistencyruntime monitoringself-consistency
Abstract
Long-horizon LLM agents can fail quietly: they settle on one reading of the evidence early, then spend the rest of the run defending it. We call this premature commitment. Final-answer scoring misses the failure mode because it sees only the answer, not whether the process has already collapsed to a stable path. We define representational commitment as cross-run hidden-state convergence at a fixed reasoning step, and use it as an early diagnostic of trajectory consistency. On Llama-3.1-70B running ReAct on HotpotQA, step-4 hidden-state similarity predicts downstream behavioral consistency (r = -0.35, partial r = -0.45), with a localized temporal and layer-wise signature. The signal replicates across Qwen-2.5-72B and Phi-3-14B, and on StrategyQA (r = -0.83). It does not track correctness: committed-wrong and committed-correct questions are not separable in activation similarity. That boundary is central to the claim. Commitment tells us whether an agent has settled, not whether it is right. A runtime monitor detects inconsistent trajectories from hidden states at AUROC up to 0.97 (0.85--0.88 under a stricter split), and a prompting intervention cuts behavioral variance by 28% against a token-matched control while leaving accuracy statistically unchanged. We also test whether the signal can route self-consistency compute; on a harder benchmark it helps only modestly and is matched by a simpler output-based baseline. The result is a diagnostic for a hidden process failure, with clear limits rather than a general accuracy lever.