TL;DR: Correct Chain-of-Thought traces reproduce their answer under prefix regeneration more often than wrong ones, and weighting majority voting by this prefix consistency reaches plateau accuracy at up to 21x fewer tokens (median 4.6x).</p>\n","updatedAt":"2026-05-13T12:05:40.174Z","author":{"_id":"66548d20e7865ffd5ecb5e7e","avatarUrl":"/avatars/24b31a44cb7b51d4b161f27230a95ed9.svg","fullname":"Naoto Iwase","name":"niwase","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8814356923103333},"editors":["niwase"],"editorAvatarUrls":["/avatars/24b31a44cb7b51d4b161f27230a95ed9.svg"],"reactions":[],"isReport":false}},{"id":"6a048aed06d319b43c26ad62","author":{"_id":"661ab1f1fa3b144a381fa454","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/661ab1f1fa3b144a381fa454/IlpZBb9NCjo7ntFwMIH53.png","fullname":"Urro","name":"urroxyz","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":9,"isUserFollowing":false},"createdAt":"2026-05-13T14:30:05.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"High-ROI. I like it.","html":"<p>High-ROI. I like it.</p>\n","updatedAt":"2026-05-13T14:30:05.692Z","author":{"_id":"661ab1f1fa3b144a381fa454","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/661ab1f1fa3b144a381fa454/IlpZBb9NCjo7ntFwMIH53.png","fullname":"Urro","name":"urroxyz","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":9,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9770863056182861},"editors":["urroxyz"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/661ab1f1fa3b144a381fa454/IlpZBb9NCjo7ntFwMIH53.png"],"reactions":[{"reaction":"❤️","users":["niwase"],"count":1}],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.07654","authors":[{"_id":"6a04676586b054ce2fa4119e","name":"Naoto Iwase","hidden":false},{"_id":"6a04676586b054ce2fa4119f","name":"Yuki Ichihara","hidden":false},{"_id":"6a04676586b054ce2fa411a0","name":"Mohammad Atif Quamar","hidden":false},{"_id":"6a04676586b054ce2fa411a1","name":"Junpei Komiyama","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/66548d20e7865ffd5ecb5e7e/Z7OxKviusvsxq7ArXudxD.gif"],"publishedAt":"2026-05-08T00:00:00.000Z","submittedOnDailyAt":"2026-05-13T00:00:00.000Z","title":"Reliable Chain-of-Thought via Prefix Consistency","submittedOnDailyBy":{"_id":"66548d20e7865ffd5ecb5e7e","avatarUrl":"/avatars/24b31a44cb7b51d4b161f27230a95ed9.svg","isPro":false,"fullname":"Naoto Iwase","user":"niwase","type":"user","name":"niwase"},"summary":"Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain-of-Thought (CoT) traces and aggregating them with majority voting (MV), a test-time technique called self-consistency. When we truncate a CoT partway through and regenerate the remainder, we observe that traces with correct answers reproduce their original answer more often than traces with wrong answers. We use this difference as a reliability signal, prefix consistency, that weights each candidate answer by how often it reappears under regeneration. It requires no access to token log-probabilities or self-rating prompts. Across five reasoning models and four math and science benchmarks, prefix consistency is the best correctness predictor in most settings, and reweighting votes by it reaches Standard MV plateau accuracy at up to 21x fewer tokens (median 4.6x). Our code is available at https://github.com/naoto-iwase/prefix-consistency.","upvotes":1,"discussionId":"6a04676586b054ce2fa411a2","projectPage":"https://naoto-iwase.github.io/prefix-consistency-page","githubRepo":"https://github.com/naoto-iwase/prefix-consistency","githubRepoAddedBy":"user","ai_summary":"Prefix consistency uses answer reproduction rates under trace regeneration to weight candidate responses, achieving high accuracy with significantly fewer tokens than standard majority voting.","ai_keywords":["Chain-of-Thought","self-consistency","majority voting","prefix consistency","answer reproduction","token regeneration","correctness prediction","test-time technique"],"githubStars":0,"organization":{"_id":"61fb9e24dc607a42af5f193f","name":"MBZUAI","fullname":"Mohamed Bin Zayed University of Artificial Intelligence","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/1643879908583-603ab5664a944b99e81476e8.jpeg"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"661ab1f1fa3b144a381fa454","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/661ab1f1fa3b144a381fa454/IlpZBb9NCjo7ntFwMIH53.png","isPro":true,"fullname":"Urro","user":"urroxyz","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"61fb9e24dc607a42af5f193f","name":"MBZUAI","fullname":"Mohamed Bin Zayed University of Artificial Intelligence","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/1643879908583-603ab5664a944b99e81476e8.jpeg"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.07654.md"}">
Reliable Chain-of-Thought via Prefix Consistency
Abstract
Prefix consistency uses answer reproduction rates under trace regeneration to weight candidate responses, achieving high accuracy with significantly fewer tokens than standard majority voting.
AI-generated summary
Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain-of-Thought (CoT) traces and aggregating them with majority voting (MV), a test-time technique called self-consistency. When we truncate a CoT partway through and regenerate the remainder, we observe that traces with correct answers reproduce their original answer more often than traces with wrong answers. We use this difference as a reliability signal, prefix consistency, that weights each candidate answer by how often it reappears under regeneration. It requires no access to token log-probabilities or self-rating prompts. Across five reasoning models and four math and science benchmarks, prefix consistency is the best correctness predictor in most settings, and reweighting votes by it reaches Standard MV plateau accuracy at up to 21x fewer tokens (median 4.6x). Our code is available at https://github.com/naoto-iwase/prefix-consistency.
Community
TL;DR: Correct Chain-of-Thought traces reproduce their answer under prefix regeneration more often than wrong ones, and weighting majority voting by this prefix consistency reaches plateau accuracy at up to 21x fewer tokens (median 4.6x).
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Cite arxiv.org/abs/2605.07654 in a model README.md to link it from this page.
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