[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-a-fix-for-ai-overthinking-cuts-token-use-by-half":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},11091,"a-fix-for-ai-overthinking-cuts-token-use-by-half","A Fix for AI Overthinking Cuts Token Use by Half","Researchers found a training-free way to keep AI reasoning models from rambling, trimming generated tokens by up to 53 percent without hurting accuracy.","AI reasoning models waste tokens overthinking simple problems, and a new paper offers a training-free fix for that without sacrificing accuracy.\n\nResearchers studying how large reasoning models (LRMs) generate step-by-step answers found that efficient reasoning chains cluster tightly in a model's latent space, while verbose, token-heavy chains drift away from that cluster. Instead of blunt fixes like banning reflective keywords or capping response length, which tend to cut off reasoning too early, the team built a training-free method that uses a quadratic program to pull wandering hidden states back toward the efficient cluster mid-generation. They tested it on four open models ranging from 1.5 billion to 14 billion parameters across six benchmarks in math, coding, and science QA. Generated tokens dropped by 11.8 percent to 52.8 percent, while accuracy improved by as much as 12.1 percent, according to the paper.\n\nToken bloat is not just an annoyance, it is a direct cost and latency problem for anyone running reasoning models at scale. Because this approach needs no retraining, it could be applied to models already in production instead of waiting on a new release.\n\nThe code is public on GitHub, but a result this clean, less computation and better accuracy, is exactly the kind of claim that deserves independent replication before anyone rewrites an inference pipeline around it.","[\"ai\",\"llms\",\"ai-research\",\"efficiency\"]","2026-10-09T04:00:00.000Z","2026-10-10T05:39:41.160Z","2026-10-10T05:39:46.757Z","published",null,[],"ai",[24,26,27,28],"llms","ai-research","efficiency",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.34181",0,{"sections":35},[36,39,43,48,53,57,61,66,71,76,80,85,90,95],{"name":37,"slug":24,"count":38,"latest_published_at":18},"AI",6804,{"name":40,"slug":41,"count":42,"latest_published_at":18},"Security","security",934,{"name":44,"slug":45,"count":46,"latest_published_at":47},"Policy","policy",486,"2026-10-08T22:40:11.000Z",{"name":49,"slug":50,"count":51,"latest_published_at":52},"Deals","deals",474,"2026-10-08T22:00:00.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":18},"Hardware","hardware",232,{"name":58,"slug":59,"count":60,"latest_published_at":18},"Science","science",194,{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",181,"2026-10-08T23:26:35.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Startups","startups",117,"2026-10-08T16:45:00.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Software","software",114,"2026-10-08T17:57:01.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":18},"Dev Tools","dev-tools",106,{"name":81,"slug":82,"count":83,"latest_published_at":84},"General","general",66,"2026-10-09T04:46:11.000Z",{"name":86,"slug":87,"count":88,"latest_published_at":89},"Gaming","gaming",58,"2026-10-08T20:08:45.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"Reviews","reviews",34,"2026-10-08T14:00:22.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"How-To","how-to",8,"2026-10-05T09:00:00.000Z"]