[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-researchers-teach-ai-to-pick-evidence-before-filling-tables":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},7951,"researchers-teach-ai-to-pick-evidence-before-filling-tables","Researchers Teach AI to Pick Evidence Before Filling Tables","TabSieve makes AI models select relevant rows as evidence before predicting a missing table value, beating prior methods by up to 4.45 percent.","A new AI framework called TabSieve makes a model show its work before it guesses a missing value in a spreadsheet.\n\nMost tabular prediction tools either look at one row in isolation or lean on an LLM prompted with a chunk of the table, and both approaches have problems: the first ignores useful patterns sitting elsewhere in the data, and the second gets confused by noisy or irrelevant rows. TabSieve splits the job into two explicit steps instead. Given a table and a query row, it first picks a small set of informative rows as evidence, then predicts the missing value using only that evidence. The researchers trained it on a synthetic dataset of 40,000 reasoning examples built from 331 real tables, plus a reinforcement learning recipe called TAB-GRPO that rewards good evidence selection and correct predictions separately. On a held-out set of 75 classification and 52 regression tables, it beat the next-best baseline by 2.92% and 4.45% respectively.\n\nThe real news here isn't the accuracy bump, it's the auditability. Anyone who has tried to explain a model's spreadsheet prediction to a skeptical analyst knows the pain of a black box. Making the evidence-selection step explicit means you can actually check which rows drove a prediction, which matters more for adoption than a couple of percentage points on a benchmark.\n\nThat said, this is one paper's benchmark numbers on curated tables, not a production system, so treat the gains as a promising lead rather than a settled result.","[\"ai\",\"machine-learning\",\"tabular-data\",\"research\"]","2026-09-25T04:00:00.000Z","2026-09-26T08:19:00.884Z","2026-09-26T08:19:07.116Z","published",null,[],"ai",[24,26,27,28],"machine-learning","tabular-data","research",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.11700",0,{"sections":35},[36,40,45,50,55,60,64,69,74,79,84,89,94,99],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",4624,"2026-09-25T21:57:05.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",748,"2026-09-26T01:30:00.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Policy","policy",392,"2026-09-25T18:44:30.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Deals","deals",258,"2026-09-26T09:00:00.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",185,"2026-09-25T15:00:22.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":54},"Science","science",144,{"name":65,"slug":66,"count":67,"latest_published_at":68},"Consumer Tech","consumer-tech",133,"2026-09-26T07:30:06.000Z",{"name":70,"slug":71,"count":72,"latest_published_at":73},"Software","software",90,"2026-09-25T20:55:00.000Z",{"name":75,"slug":76,"count":77,"latest_published_at":78},"Dev Tools","dev-tools",84,"2026-09-26T04:20:58.000Z",{"name":80,"slug":81,"count":82,"latest_published_at":83},"Startups","startups",76,"2026-09-25T18:33:59.000Z",{"name":85,"slug":86,"count":87,"latest_published_at":88},"Gaming","gaming",48,"2026-09-25T18:35:21.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",46,"2026-09-25T02:12:57.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",30,"2026-09-24T20:07:31.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]