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

Aman Mehta

arXiv:2606.22936v1cs.AI

TL;DR

Long-horizon LLM agents can prematurely settle on an interpretation, while final-answer and agreement metrics cannot reveal whether that commitment is correct. This paper measures cross-run hidden-state convergence to diagnose settled trajectories and finds that it predicts consistency across models and benchmarks, while detecting commitment without tracking correctness.

  • Problem

    Final-answer and cross-run agreement metrics do not reveal whether coherent agent trajectories remain open to evidence or reflect premature commitment.

  • Method

    The paper defines representational commitment as mean pairwise cosine similarity between cross-run hidden states at a fixed reasoning step.

  • Results

    Step-4 activation similarity predicts trajectory consistency across models and benchmarks, detects inconsistent trajectories at AUROC up to 0.97, and reduces behavioral variance by 28% without changing accuracy.

  • Takeaways & Limitations

    Commitment is a diagnostic of whether an agent has settled and can support variance reduction, but it should be paired with correctness checks rather than used alone.

  • Takeaways & Limitations

    The signal does not separate committed-wrong from committed-correct runs and is not yet a better compute router than output agreement.

Abstract

from arXiv · show

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.

1 Introduction

The paper defines premature commitment as early settling on an interpretation and measures it through cross-run hidden-state convergence. This diagnostic predicts trajectory consistency and detects process instability, but does not indicate correctness or reliably improve accuracy.

  • Motivation: Premature commitment occurs when an agent settles on one interpretation early and defends it through the remainder of a long-horizon task.Final-answer checks cannot reveal this process failure, while cross-run agreement can reflect either confidently correct or confidently wrong behavior.
  • Operational definition: Representational commitment is mean pairwise cosine similarity between cross-run hidden states at the last token of a fixed agent step.High similarity after different observations indicates a stable interpretation; activation similarity is the measurement, while commitment is the construct it indexes.
  • Empirical signature: r = −0.35 and partial r = −0.45 at step 4 predict trajectory consistency on Llama-HotpotQA, with a 4.1× quartile gap and d = 1.01.The signature localizes sharply in time and layers, replicating across Llama 70B, Qwen 72B, Phi-3 14B, and StrategyQA, where r = −0.83.
  • Limitation: Committed-wrong and committed-correct questions are not separable in activation similarity, showing that commitment tracks process consistency rather than correctness.The same internal convergence signature can accompany reliable success or reliable failure.
  • Detection and intervention: AUROC 0.97 detects inconsistent trajectories from step-4 hidden states, while a prompting intervention cuts variance by 28% versus filler without changing accuracy.Under a median split, detection reaches AUROC 0.85–0.88; the intervention raises convergence with d = 0.97 and p = .001.

2 Related work

Prior work measures agent reliability through behavioral consistency and output-level variance, while probing and representation-engineering studies examine what hidden states encode or how they can be controlled. This work extends those lines by studying commitment as cross-run convergence of internal trajectories.

  • Behavioral consistency in agents: Behavioral consistency links run agreement to reliability, but self-consistency uses output variance without examining internal representations.Mehta (2026a) reported a 32–55 percentage point accuracy gap between consistent and inconsistent HotpotQA runs, while Mehta (2026b) found consistency amplifies outcomes without guaranteeing correctness.
  • Probing LLM representations: Prior probing studies ask what hidden states encode at one processing point; this work asks whether representations remain stable across independent trajectories.Related work connects hidden states with truth and confidence and uses probes to detect incorrect generations, including in reasoning models.
  • Representation engineering: Representation engineering has used hidden-state structure for reading, behavioral steering, and trait extraction, motivating commitment as another candidate axis.Commitment is defined here as the degree to which multi-run trajectories collapse to similar internal states.

3 Method

The method uses repeated ReAct trajectories to test whether cross-run hidden-state similarity indexes trajectory consistency. It separates the measured activation statistic from representational commitment and evaluates both with defined behavioral metrics and statistical controls.

  • Agent procedure: The ReAct agent iterates Thought → Action → Observation triples, with runs ending at Finish or a 25-step cap.One triple constitutes one step, making step 4 the fourth reasoning cycle.
  • Representation: At each step, the method extracts the last output-token hidden state from the cumulative context containing instructions, the question, prior triples, and the current Thought and Action.The last-token representation follows a probing convention, while comparisons with pooled or trajectory-level representations remain open.
  • Representation: Activation similarity is the mean cross-run cosine similarity of last-token hidden states, whereas representational commitment is the construct this statistic is intended to index.The method explicitly keeps the measured quantity and the construct conceptually distinct.
  • Behavioral metrics: Trajectory consistency is measured across 10 runs using step-count coefficient of variation and action-sequence diversity rather than output agreement.The coefficient of variation is standard deviation divided by mean, while action-sequence diversity is the proportion of unique action sequences.
  • Experimental setup: The primary experiment runs Llama-3.1-70B-Instruct on 100 HotpotQA validation questions, producing 988 trajectories at temperature T=0.51 with a 25-step cap.The set contains 50 easy comparison questions and 50 hard multi-hop questions; step 4 has sufficient data for 99 of 100 questions.
  • Statistical analysis: The analysis combines Pearson and partial Pearson correlations, intervention tests, bootstrap mediation, LOOCV AUROC, and 10,000-iteration permutation tests with Bonferroni correction.The step-4/layer-40 signal was discovered through a 66-cell step×layer scan and is distinguished as exploratory or confirmatory according to analysis status.

4 Results

Step-4 hidden-state similarity is a robust, architecture- and benchmark-replicated diagnostic of downstream trajectory consistency, not correctness. Runtime detection is strong, while prompting reduces variance; routing extra self-consistency compute remains only modestly useful.

  • Core result: r = −0.348 at step 4 and layer 40 predicts lower behavioral CV, with the signal absent at steps 1–2, strengthening at step 3, and weakening at step 5.The effect spans layers 32–80 and peaks at layer 40; the top similarity quartile has 4.1× lower mean step-count CV.
  • Robustness: r = −0.45 after controlling for accuracy and difficulty, while document-identity control retains partial r = −0.31 and top-overlap questions retain r = −0.47.Question length, context size, and step-3 thought length do not predict CV, and the step-4/layer-40 result survives Bonferroni correction.
  • Limits: 0.935 vs. 0.903 activation similarity for committed-wrong versus committed-correct Llama questions is not significant, showing commitment does not identify correctness.Activation similarity also fails to predict cross-run answer agreement (r = −0.13, p = 0.22), and correctness probes perform at AUC 0.34–0.56.
  • Replication: r = −0.65 for Qwen at layer 64 and r = −0.58 for Phi-3 at step 5 replicate the relationship across architectures, while peak depth varies by model.On StrategyQA, the signal peaks one step earlier at step 3 with r = −0.83, consistent with shorter reasoning chains.
  • Runtime monitoring: AUROC 0.97 detects trajectory inconsistency under quintile labeling, while the stricter median split reaches 0.85 on Llama and 0.88 on Qwen.The fair pre-completion question-length baseline remains near chance at 0.52–0.65.
  • Intervention and compute routing: 28% lower CV versus the token-matched filler control shows that commitment framing, rather than extra tokens, reduces behavioral variance.Action diversity also falls by 24%, while the net effect versus the standard control is 15%; self-consistency routing helps only modestly on MuSiQue and is matched by a simpler output baseline.

5 Discussion

Premature commitment is a hidden process failure: internal-state convergence reveals when agents settle, but does not reveal whether they are correct. The diagnostic supports conditional operational choices while remaining limited in scope and motivating targeted validation and routing research.

  • Why name this failure mode: Premature commitment evades final-score and cross-run agreement checks because internally consistent behavior can still reflect the wrong reasoning.Internal-state diagnostics expose convergence directly, but cannot distinguish committed-wrong from committed-correct cases without a correctness check.
  • Operational use: A correctness-agnostic signal remains operationally useful because it identifies whether an agent has settled, not whether its answer is right.On committed inputs, deployers should defer to an external verifier or human rather than resample; on unsettled inputs, resampling can help, and committed inputs may be early-exited to save compute.
  • Limitations: The diagnostic was validated on three models spanning 14B–72B parameters and two reasoning benchmarks, with MuSiQue used only as a harder routing stress test.Code, math, and embodied tasks remain untested, all runs use one temperature, and CV and action diversity are coarse proxies.
  • Limitations: Mechanistic limitations include only partial disentanglement from observation overlap, inconsistent mediation, mixed results from single-layer steering, and reliance on an assumption about representations.These limitations constrain mechanistic interpretation and the generality of steering-based interventions.
  • Future directions: Future work should replay observations while varying reasoning, apply TOST to the correctness-agnostic claim, and test hidden-state routers against output-based adaptive consistency.The proposed router should outperform output-based adaptive consistency rather than merely fixed-sample self-consistency.

6 Conclusion … D Partial correlation results

Representational commitment is a hidden-state convergence signal for diagnosing settled agent trajectories: it predicts consistency but not correctness. The paper presents it as useful for measurement and variance reduction, while showing mixed steering results and limits for compute routing.

  • 6 Conclusion: Representational commitment diagnoses when agents have settled by measuring cross-run hidden-state convergence.The signal is described as a compact diagnostic for a hidden process failure.
  • 6 Conclusion: The signal predicts trajectory consistency but does not distinguish committed-wrong from committed-correct runs.Both outcome types share the same convergence signature.
  • 6 Conclusion: The diagnostic is useful for measurement and variance reduction but is not yet a better compute router than output agreement.The conclusion explicitly states this limitation.
  • A Preliminary steering experiment: For 5 questions, steering added a commitment direction from step-4, layer-40 hidden states with α = 1.5 during inference.The direction contrasted mean hidden states from committed consistent-correct and uncommitted inconsistent runs.
  • A Preliminary steering experiment: On 2/5 questions, steering reduced step-count CV from 0.31→0.18 and 0.27→0.12.The reported reductions were mixed across the five questions.
  • B Temporal profile: The consistency signal peaks at step 4 in the temporal profile.Figure 5 reports the correlation between activation similarity and CV at layer 40 across steps.
  • C Permutation test results: At step 4, the permutation test reports significant results across layers 32–80 using 10,000 iterations.The supplied table caption states that all layers 32–80 are significant.
  • D Partial correlation results: At step 4, partial correlations strengthen after controlling for accuracy and difficulty label.The table caption attributes the strengthening to removing difficulty-related variance.

E Full cross-model layer-wise results … J Commitment as a linear direction

Across models, step-4 activation similarity tracks behavioral consistency with architecture- and difficulty-dependent signatures, while remaining agnostic to correctness. Additional geometric analyses identify prototype distance and a near-one-dimensional commitment direction as related consistency signals.

  • E Full cross-model layer-wise results: Layer-0 positive correlations reflect input-level similarity rather than the commitment signal: r = +0.47 Llama and +0.49 Qwen.Phi-3 lacks this pattern (r = −0.05, p = .63), likely because of its different tokenizer.
  • E Full cross-model layer-wise results: All three models show negative step-4 correlations, while Phi-3’s strongest signal occurs at step 5 (r = −0.58).The two 70B models peak at step 4; Phi-3 peaks one step later, suggesting an additional evidence-gathering step.
  • F Hard vs. easy breakdown: Easy questions show strong non-embedding correlations (r ≈−0.5 to −0.57), whereas hard questions collapse to near zero (|r| < 0.16).The pattern is consistent with committed-wrong states saturating similarity within hard questions.
  • G Commitment category visualization: Committed-correct and committed-wrong questions occupy overlapping hidden-state regions, while uncommitted-wrong questions are more diffuse.The t-SNE is qualitative illustration only, not inferential evidence of equivalence.
  • H Baseline predictors of behavioral CV: Table 5 distinguishes downstream behavioral consequences from pre-completion confounds and reports observation overlap across runs at steps 1–3.The observation-overlap measures use n = 94.
  • I Prototype distance signal for hard questions: Questions farther from the hard-question activation centroid tend to be more consistent (r = 0.43, permutation p = 0.004, 95% CI [0.24, 0.63], d = 0.64, n=44).The measure is cosine similarity between each question’s mean step-4, layer-32 state and the hard-question centroid.
  • J Commitment as a linear direction: The commitment direction nearly coincides with the first principal component (cos = −0.98; PC1 explains 53% of variance) and its projection correlates with behavioral CV (r = −0.32, p = 0.001).The direction is defined as the difference between committed-correct and uncommitted-wrong mean states at Llama layer 40.

K Consistency prediction: full results

The section evaluates consistency prediction across features, models, and labeling schemes, finding that hidden-state features outperform pre-completion surface baselines. Performance remains useful with fewer runs, reaching 0.81 ± 0.07 at k=3 and 0.97 at k=10, while early exit saves compute.

  • Full evaluation: Consistency prediction is evaluated across features, models, and labeling schemes, including step-4 hidden-state features.Table 6 reports the full evaluation, with Features A–E using step-4 hidden states.
  • Full evaluation: Hidden-state features outperform pre-completion surface baselines on quintile-labeled consistency prediction.The ROC curves distinguish hidden-state features from dashed pre-completion surface baselines.
  • Fewer runs, and a practical recipe: 0.81 ± 0.07 AUROC is achieved with k=3 runs, increasing to 0.87 at k=4, 0.91 at k=5, and 0.97 at k=10.At k=3, the 95% CI is [0.68, 0.94].
  • Fewer runs, and a practical recipe: 70.2% accuracy is reached in an early-exit simulation while saving 29% of compute.The simulation used 5-fold threshold selection and improved 20 percentage points over majority.

L StrategyQA cross-benchmark results

On StrategyQA, the commitment signal peaks at step 3, one step earlier than on HotpotQA, and spans all non-embedding layers with strong, accuracy-controlled correlations. The signal peaks around layer 72, while partial correlations remain essentially unchanged given 93.2% accuracy.

  • Accuracy control: Partial correlations controlling for accuracy are virtually unchanged, with StrategyQA accuracy at 93.2%.The high accuracy is cited as the reason accuracy control has little effect on the correlations.
  • Layer-wise signal: The signal peaks at layer 72 and plateaus across layers 56–72.Table 7 describes the layer-wise peak and plateau for Llama-3.1-70B on StrategyQA.
  • Temporal and cross-benchmark pattern: Step 3 concentrates the StrategyQA signal, one step earlier than HotpotQA’s step-4 peak, consistent with shorter reasoning chains.The cross-benchmark comparison reports a stronger StrategyQA signal and an earlier peak.
  • Layer-wise signal: |r| > 0.72 across layers 8–80 at step 3, with all correlations significant at p < 10−8.The signal spans all reported non-embedding layers.

M Summary of all results

Across conditions, the strongest activation-similarity–CV correlation occurs at condition-specific peak step/layer locations, while cross-model comparison uses the step-4/L16 cell. Summary metrics use quintile labeling with layer-profile features for AUROC and commitment-versus-filler comparisons for CV reduction.

  • Activation-similarity signal: Phi-3’s strongest signal is at step 5, with r = −0.58.The cross-model-comparable step-4/L16 cell is r = −0.36, as reported in Table 4.
  • Evaluation conditions: AUROC uses quintile labeling with layer-profile features, while median-split values are reported in Table 6.The table distinguishes this AUROC setup from the CV-reduction comparison.
  • Evaluation conditions: CV reduction compares commitment against filler.This comparison defines the CV-reduction summary condition.

N Intervention details · O Intervention prompt text

The step-3 commitment prompt reduces behavioral variance relative to a token-matched filler, with effects concentrated on initially inconsistent questions and no degradation of already-correct performance. Activation trajectories diverge only at step 4, while question-level variance reduction does not systematically improve accuracy.

  • N Intervention details: 15% net CV reduction versus control did not survive correction (d = 0.20, Holm p = .088).Commitment tightened behavior beyond baseline, while the filler condition trended toward higher variance than control.
  • N Intervention details: 28% lower variance for filler versus commitment isolates framing from token count (d = 0.33, Holm p = .003).Control versus filler increased variance by +18% (CV = 0.132 vs. 0.112, n.s.), indicating disruption from prompt insertion.
  • N Intervention details: Activation-similarity curves were identical through step 3 and diverged at step 4, with commitment highest at 0.995 and control lowest at 0.922.The one-step delay localizes the representational shift to the commitment juncture rather than a pre-existing subset difference.
  • N Intervention details: Among originally inconsistent questions, filler-versus-commitment reduced CV by ∆CV = 0.069 (d = 0.44, p = .018).Already-consistent questions showed a smaller, non-significant trend (d = 0.27, p = .102).
  • N Intervention details: 88 of 89 consistent-correct questions remained consistent-correct under commitment, all 4 consistent-wrong questions remained so, and none became consistent-wrong.The intervention never degraded already-correct performance.
  • N Intervention details: CV reduction correlated negatively with accuracy change (r = −0.32, p = .001), indicating lower variance without a systematic accuracy gain.Questions with the largest CV reductions tended to show slight accuracy decreases, consistent with a correctness-agnostic commitment signal.
  • O Intervention prompt text: The commitment prompt instructed the agent to choose and clearly state one reasoning strategy, then follow it without changing strategies or starting over.It was appended at step 3.
  • O Intervention prompt text: The filler prompt matched token length and asked the agent to proceed normally, consider gathered information, and take whatever next step seemed appropriate.It was appended at step 3 and provided no explicit commitment instruction.
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