Long-horizon agents can fail by settling too early. This paper introduces <strong>representational commitment</strong>: cross-run hidden-state convergence that diagnoses when an agent has already locked onto a trajectory.</p>\n<p>The key finding is that commitment predicts <strong>trajectory consistency</strong>, not correctness. Committed-wrong and committed-correct runs can share the same convergence signature. So agreement across runs is not always evidence that an agent is right; it may just mean the agent has become confidently settled.</p>\n<p>The practical use is monitoring: detect when an agent has settled, then decide whether to verify, resample, or defer—rather than treating consistency as trust.</p>\n","updatedAt":"2026-06-23T17:32:52.058Z","author":{"_id":"66663dcf15ec3fd28b3e1530","avatarUrl":"/avatars/33ce2dd4f27c7d9007f971b5b247d5c0.svg","fullname":"Aman Mehta","name":"amanmeh","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.9134740829467773},"editors":["amanmeh"],"editorAvatarUrls":["/avatars/33ce2dd4f27c7d9007f971b5b247d5c0.svg"],"reactions":[],"isReport":false}},{"id":"6a3aeebabb292c835dcfaeb0","author":{"_id":"66663dcf15ec3fd28b3e1530","avatarUrl":"/avatars/33ce2dd4f27c7d9007f971b5b247d5c0.svg","fullname":"Aman Mehta","name":"amanmeh","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false},"createdAt":"2026-06-23T20:38:18.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"@librarian-bot","html":"<p><span class=\"SVELTE_PARTIAL_HYDRATER contents\" data-target=\"UserMention\" data-props=\"{"user":"librarian-bot"}\"><span class=\"inline-block\"><span class=\"contents\"><a href=\"/librarian-bot\">@<span class=\"underline\">librarian-bot</span></a></span> </span></span></p>\n","updatedAt":"2026-06-23T20:38:18.352Z","author":{"_id":"66663dcf15ec3fd28b3e1530","avatarUrl":"/avatars/33ce2dd4f27c7d9007f971b5b247d5c0.svg","fullname":"Aman Mehta","name":"amanmeh","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7558995485305786},"editors":["amanmeh"],"editorAvatarUrls":["/avatars/33ce2dd4f27c7d9007f971b5b247d5c0.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.22936","authors":[{"_id":"6a3ac2930a86ac3098d5d4ec","name":"Aman Mehta","hidden":false}],"publishedAt":"2026-06-22T00:00:00.000Z","submittedOnDailyAt":"2026-06-23T00:00:00.000Z","title":"When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents","submittedOnDailyBy":{"_id":"66663dcf15ec3fd28b3e1530","avatarUrl":"/avatars/33ce2dd4f27c7d9007f971b5b247d5c0.svg","isPro":false,"fullname":"Aman Mehta","user":"amanmeh","type":"user","name":"amanmeh"},"summary":"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.","upvotes":1,"discussionId":"6a3ac2930a86ac3098d5d4ed","ai_summary":"Pre premature commitment in long-horizon LLM agents leads to silent failures where agents defend early interpretations without considering alternatives, and hidden-state convergence serves as an early diagnostic for trajectory consistency.","ai_keywords":["LLM agents","premature commitment","representational commitment","cross-run hidden-state convergence","ReAct","HotpotQA","StrategyQA","AUROC","self-consistency","token-matched control"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","organization":{"_id":"62cece4aa3a23014aca72499","name":"Snowflake","fullname":"Snowflake","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/64dc52cf858f8a41c12fc819/O9-MWzRjWzbNP_DQlMb-7.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"66663dcf15ec3fd28b3e1530","avatarUrl":"/avatars/33ce2dd4f27c7d9007f971b5b247d5c0.svg","isPro":false,"fullname":"Aman Mehta","user":"amanmeh","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"62cece4aa3a23014aca72499","name":"Snowflake","fullname":"Snowflake","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/64dc52cf858f8a41c12fc819/O9-MWzRjWzbNP_DQlMb-7.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2606/2606.22936.md","query":{}}">
When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents
Abstract
Pre premature commitment in long-horizon LLM agents leads to silent failures where agents defend early interpretations without considering alternatives, and hidden-state convergence serves as an early diagnostic for trajectory consistency.
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.
Community
Long-horizon agents can fail by settling too early. This paper introduces representational commitment: cross-run hidden-state convergence that diagnoses when an agent has already locked onto a trajectory.
The key finding is that commitment predicts trajectory consistency, not correctness. Committed-wrong and committed-correct runs can share the same convergence signature. So agreement across runs is not always evidence that an agent is right; it may just mean the agent has become confidently settled.
The practical use is monitoring: detect when an agent has settled, then decide whether to verify, resample, or defer—rather than treating consistency as trust.
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Cite arxiv.org/abs/2606.22936 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.22936 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.22936 in a Space README.md to link it from this page.
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