Efficient Parallel Reasoning 🔥</p>\n","updatedAt":"2026-08-24T11:16:32.413Z","author":{"_id":"645b0c3ec35da9c7afd95421","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645b0c3ec35da9c7afd95421/vYBrCDagHsXAo6J2p-uG0.jpeg","fullname":"Yuling","name":"YerbaPage","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":110,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.49232062697410583},"editors":["YerbaPage"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/645b0c3ec35da9c7afd95421/vYBrCDagHsXAo6J2p-uG0.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.16425","authors":[{"_id":"6a8c27883d26296ea3091b87","name":"Xuteng Zhang","hidden":false},{"_id":"6a8c27883d26296ea3091b88","name":"Wenhao Zeng","hidden":false},{"_id":"6a8c27883d26296ea3091b89","name":"Xiaodong Gu","hidden":false},{"_id":"6a8c27883d26296ea3091b8a","name":"Chao Hu","hidden":false},{"_id":"6a8c27883d26296ea3091b8b","name":"Haotian Lin","hidden":false},{"_id":"6a8c27883d26296ea3091b8c","user":{"_id":"645b0c3ec35da9c7afd95421","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645b0c3ec35da9c7afd95421/vYBrCDagHsXAo6J2p-uG0.jpeg","isPro":false,"fullname":"Yuling","user":"YerbaPage","type":"user","name":"YerbaPage"},"name":"Yuling Shi","status":"claimed_verified","statusLastChangedAt":"2026-08-24T14:36:47.438Z","hidden":false},{"_id":"6a8c27883d26296ea3091b8d","name":"Min Wang","hidden":false},{"_id":"6a8c27883d26296ea3091b8e","name":"Beijun Shen","hidden":false}],"publishedAt":"2026-08-17T11:24:46.000Z","submittedOnDailyAt":"2026-08-24T00:00:00.000Z","title":"ParaTempo: Efficient Parallel Reasoning via Temporal Confidence","submittedOnDailyBy":{"_id":"645b0c3ec35da9c7afd95421","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645b0c3ec35da9c7afd95421/vYBrCDagHsXAo6J2p-uG0.jpeg","isPro":false,"fullname":"Yuling","user":"YerbaPage","type":"user","name":"YerbaPage"},"summary":"Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and branch count. Existing methods for managing these parallel paths typically rely on final-answer consensus, local token confidence, or isolated intermediate probes. However, these signals are often delayed, weakly tied to actual reasoning progress, or too noisy for dynamic, branch-level control. To address these limitations, we introduce ParaTempo, a training-free asynchronous parallel reasoning framework. ParaTempo is driven by temporal confidence, a branch-local measure of answer-space convergence. Each branch is periodically probed for a tentative answer probability distribution, and temporal confidence quantifies how sharply the recent intermediate probes concentrate on a dominant answer. Once sufficient evidence has accumulated, ParaTempo drives its entire control process from this single signal: low-confidence branches are pruned, branches that persistently commit to their dominant answer are retired early, freed computation is reallocated by forking new branches, and generation stops globally once the confidence-weighted vote concentrates. Without requiring synchronization among reasoning trajectories, ParaTempo adaptively allocates computation based on branch-level convergence. Experiments on challenging mathematical and scientific reasoning benchmarks show that ParaTempo reduces average latency by 21.8-32.2% and total token usage by 18.1-30.3% while maintaining competitive accuracy. Moreover, temporal confidence exhibits stronger temporal stability and predictive power for future branch convergence than token-level and instantaneous signals.","upvotes":25,"discussionId":"6a8c27883d26296ea3091b8f","githubRepo":"https://github.com/ScottZhang812/ParaTempo","githubRepoAddedBy":"user","ai_summary":"ParaTempo improves parallel reasoning efficiency by using temporal confidence to dynamically prune, retire, and reallocate reasoning branches without synchronization.","ai_keywords":["parallel reasoning","temporal confidence","answer-space convergence","branch-level control","asynchronous reasoning","confidence-weighted vote"],"ai_summary_model":"thinkingmachines/Inkling-Small","githubStars":1,"organization":{"_id":"63e5ef7bf2e9a8f22c515654","name":"SJTU","fullname":"Shanghai Jiao Tong University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/1676013394657-63e5ee22b6a40bf941da0928.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"645b0c3ec35da9c7afd95421","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/645b0c3ec35da9c7afd95421/vYBrCDagHsXAo6J2p-uG0.jpeg","isPro":false,"fullname":"Yuling","user":"YerbaPage","type":"user"},{"_id":"65684c80a9a1a6a50d779f58","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65684c80a9a1a6a50d779f58/it534ZdH5LxRub1M_o3uM.jpeg","isPro":false,"fullname":"Silin Chen","user":"Silin-Chen","type":"user"},{"_id":"6569acb47e172c34cabb5ba3","avatarUrl":"/avatars/a3442fe2466bd2c434511155de3849b7.svg","isPro":false,"fullname":"Mengfan Li","user":"JinNian0072","type":"user"},{"_id":"68831681240aa3d8ce43e1bf","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/iSjQ3OIkIvqm_0Wxv4Qib.png","isPro":false,"fullname":"Azzz","user":"azzzacs","type":"user"},{"_id":"68e3591ffd5ab6b77e32bc6d","avatarUrl":"/avatars/614c256df6a12e6acc3f9b58479c75a9.svg","isPro":false,"fullname":"hyperlynnx","user":"hyperlynnx","type":"user"},{"_id":"682e7c6c32ff4f6be0c87469","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/n1XT-2D5IplIhCawNq4eD.png","isPro":false,"fullname":"Scott Z","user":"se6ast1an","type":"user"},{"_id":"6964d2ff0064bf3b0aee3ee5","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/nBtxcqbnzPkS9LvfWrGQe.png","isPro":false,"fullname":"Logit","user":"logitworld","type":"user"},{"_id":"664aef59691370727c27ad2a","avatarUrl":"/avatars/2f3a319723d3995b1f3696ea0acd5edd.svg","isPro":false,"fullname":"Daisy Chen","user":"HuckleberryPopo","type":"user"},{"_id":"67e272b8cf3845b43173168b","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/CzeHu1HkKECAUpG0kflU1.png","isPro":false,"fullname":"qyq","user":"OdinKD","type":"user"},{"_id":"66bb6372e5904d821f745d5f","avatarUrl":"/avatars/a53b7e220ded1c07ee4a4c81a9146c8f.svg","isPro":false,"fullname":"Yaoning Wang","user":"Castria-cn","type":"user"},{"_id":"69770caa2810079eaf632ab1","avatarUrl":"/avatars/0d4099ea4eb0e382e33c075330851cb7.svg","isPro":false,"fullname":"M","user":"Resfeber226","type":"user"},{"_id":"69770e7cf9dde7a973e73185","avatarUrl":"/avatars/99b6d16ad8bdcf2224ba1f6d2680e79b.svg","isPro":false,"fullname":"m","user":"kpcure","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":3,"organization":{"_id":"63e5ef7bf2e9a8f22c515654","name":"SJTU","fullname":"Shanghai Jiao Tong University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/1676013394657-63e5ee22b6a40bf941da0928.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2608/2608.16425.md","query":{}}">
ParaTempo: Efficient Parallel Reasoning via Temporal Confidence
Abstract
ParaTempo improves parallel reasoning efficiency by using temporal confidence to dynamically prune, retire, and reallocate reasoning branches without synchronization.
Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and branch count. Existing methods for managing these parallel paths typically rely on final-answer consensus, local token confidence, or isolated intermediate probes. However, these signals are often delayed, weakly tied to actual reasoning progress, or too noisy for dynamic, branch-level control. To address these limitations, we introduce ParaTempo, a training-free asynchronous parallel reasoning framework. ParaTempo is driven by temporal confidence, a branch-local measure of answer-space convergence. Each branch is periodically probed for a tentative answer probability distribution, and temporal confidence quantifies how sharply the recent intermediate probes concentrate on a dominant answer. Once sufficient evidence has accumulated, ParaTempo drives its entire control process from this single signal: low-confidence branches are pruned, branches that persistently commit to their dominant answer are retired early, freed computation is reallocated by forking new branches, and generation stops globally once the confidence-weighted vote concentrates. Without requiring synchronization among reasoning trajectories, ParaTempo adaptively allocates computation based on branch-level convergence. Experiments on challenging mathematical and scientific reasoning benchmarks show that ParaTempo reduces average latency by 21.8-32.2% and total token usage by 18.1-30.3% while maintaining competitive accuracy. Moreover, temporal confidence exhibits stronger temporal stability and predictive power for future branch convergence than token-level and instantaneous signals.
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Efficient Parallel Reasoning 🔥
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Cite arxiv.org/abs/2608.16425 in a model README.md to link it from this page.
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