[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-a-smarter-way-to-run-llms-at-test-time":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},3149,"a-smarter-way-to-run-llms-at-test-time","A Smarter Way to Run LLMs at Test Time","A new self-consistency variant called Blend-ASC cuts the number of model samples needed for accurate reasoning by nearly 5x, without any tuning required.","Researchers say they've found a more efficient way to make large language models think harder without spending a fortune on compute.\n\nSelf-consistency is a standard trick for improving LLM accuracy on reasoning tasks: generate a bunch of responses to the same question, then pick the most common answer. It works, but it's expensive — running dozens of samples per question across a large dataset adds up fast. A new paper from arXiv introduces Blend-ASC, a variant that dynamically decides how many samples each question actually needs rather than applying a fixed number to everything. The result is a claimed 4.8x reduction in samples used on average, while still matching or beating the accuracy of conventional self-consistency approaches. Crucially, Blend-ASC requires no hyperparameter tuning and supports batching, so it can slot into existing pipelines without much friction.\n\nThe efficiency gap matters because test-time compute has become one of the main levers AI labs pull to squeeze more performance out of existing models — OpenAI, Google, and others have all leaned on it as a way to boost benchmark scores without retraining. A principled, theoretically grounded approach to sampling efficiency could meaningfully reduce inference costs, which remain a real constraint for anyone running reasoning workloads at scale.\n\nThe paper also offers what the authors call the first formal power-law analysis of how self-consistency scales with sample count — useful context, though published benchmarks have a way of looking less impressive once they meet production traffic.","[\"ai\",\"llms\",\"inference\",\"research\"]","2026-07-01T04:00:00.000Z","2026-07-01T08:27:24.090Z","2026-07-01T08:27:27.051Z","published",null,[],"ai",[24,26,27,28],"llms","inference","research",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.12309",0,{"sections":35},[36,40,45,50,55,60,65,70,75,80,85,89,94,99],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":86,"slug":87,"count":83,"latest_published_at":88},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]