[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-ai-method-uses-leftover-errors-to-find-better-equations":10,"sections":35},{"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":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},10997,"new-ai-method-uses-leftover-errors-to-find-better-equations","New AI Method Uses Leftover Errors to Find Better Equations","RISR teaches language models to read the pattern of their prediction errors, helping them propose more accurate scientific equations than earlier AI methods.","Researchers have built an AI system that studies its own mistakes to write better scientific formulas.\n\nSymbolic regression is the search for compact mathematical equations that fit a dataset, an alternative to opaque neural-network models. A new method called RISR changes how large language models tackle that search: instead of judging a candidate formula only by its overall fit score, RISR encodes the pattern of leftover errors - the residuals - across every data point and feeds that information back into the model as it proposes revisions. A second component then predicts whether a candidate correction is actually worth fitting before the system commits computing power to it. On the LLM-SRBench benchmark, RISR hit 63.57% accuracy within a 1% error tolerance and 38.50% within a stricter 0.1% tolerance on in-distribution problems, with 56.07% and 38.24% on out-of-distribution problems, beating baselines that use the same underlying language model.\n\nMost LLM-based symbolic regression tools treat a good-enough fit as success and discard information about where a formula goes wrong. RISR's residual-reading approach lets it target the specific part of an equation that is failing, a more surgical fix than the usual generate-and-rescore loop. That matters for fields like physics and biology, where a formula that is accurate everywhere except one regime is often useless.\n\nStill, these are modest gains, not a breakthrough: even the best in-distribution score tops out below two-thirds accuracy at a loose 1% tolerance, and it drops below 40% once the tolerance tightens. Reading your own errors helps, but it does not yet solve equation discovery.","[\"symbolic regression\",\"large language models\",\"scientific discovery\",\"equation discovery\"]","2026-10-09T04:00:00.000Z","2026-10-10T01:14:36.394Z","2026-10-10T01:14:42.017Z","published",null,[],"ai",[26,27,28,29],"symbolic regression","large language models","scientific discovery","equation discovery",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.11387",0,{"sections":36},[37,40,44,49,54,58,62,67,72,77,82,87,92,97],{"name":38,"slug":24,"count":39,"latest_published_at":18},"AI",6707,{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",931,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",486,"2026-10-08T22:40:11.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Deals","deals",474,"2026-10-08T22:00:00.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":18},"Hardware","hardware",231,{"name":59,"slug":60,"count":61,"latest_published_at":18},"Science","science",192,{"name":63,"slug":64,"count":65,"latest_published_at":66},"Consumer Tech","consumer-tech",181,"2026-10-08T23:26:35.000Z",{"name":68,"slug":69,"count":70,"latest_published_at":71},"Startups","startups",117,"2026-10-08T16:45:00.000Z",{"name":73,"slug":74,"count":75,"latest_published_at":76},"Software","software",114,"2026-10-08T17:57:01.000Z",{"name":78,"slug":79,"count":80,"latest_published_at":81},"Dev Tools","dev-tools",105,"2026-10-07T16:59:11.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"General","general",66,"2026-10-09T04:46:11.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"Gaming","gaming",58,"2026-10-08T20:08:45.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",34,"2026-10-08T14:00:22.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",8,"2026-10-05T09:00:00.000Z"]