[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-ai-models-can-spot-the-right-answer-then-pick-the-wrong-one":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},7321,"ai-models-can-spot-the-right-answer-then-pick-the-wrong-one","AI Models Can Spot the Right Answer, Then Pick the Wrong One","A new benchmark finds vision-language models spot right evidence but still choose wrong answers unless prompted to link evidence to the process stage.","Vision-language AI models can spot the right clue in an image and still give the wrong answer.\n\nResearchers built VPAC-Bench, a benchmark of nine real-image process families, including assembly, physical state transitions, navigation and traffic, and object-use affordance tasks, each labeled with its current stage and the next likely transition. When they tested several VLMs on ambiguous cases where the right answer depends on knowing that stage, the models over-committed to a single guess more than 95% of the time, even after correctly identifying the relevant visual evidence. A new prompting method called State-Relevance-Target (SRT) forces models to link what they see to the specific process stage before answering, cutting that error rate to under 13% without hurting performance on easier cases. The gains were biggest when the prompt named the exact stage transition in play, beating both generic process prompts and standard chain-of-thought reasoning.\n\nThat gap between recognizing evidence and acting on it is the real finding here. Most VLM benchmarks score whether a model can name what's in an image, not whether it can reason through what that image implies happens next, which is closer to what real applications like robotics or assistive navigation actually need. A model that can describe a scene perfectly but still guesses wrong is not ready for tasks where the guess has consequences.\n\nBenchmark leaderboards that only measure recognition are grading half the exam.","[\"vision-language models\",\"ai benchmarks\",\"computer vision\"]","2026-09-23T04:00:00.000Z","2026-09-23T07:23:04.602Z","2026-09-23T07:23:10.899Z","published",null,[],"ai",[26,27,28],"vision-language models","ai benchmarks","computer vision",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.22588",0,{"sections":35},[36,39,43,48,53,58,62,67,72,77,82,87,92,97],{"name":37,"slug":24,"count":38,"latest_published_at":18},"AI",4264,{"name":40,"slug":41,"count":42,"latest_published_at":18},"Security","security",707,{"name":44,"slug":45,"count":46,"latest_published_at":47},"Policy","policy",369,"2026-09-23T02:13:52.000Z",{"name":49,"slug":50,"count":51,"latest_published_at":52},"Deals","deals",202,"2026-09-22T23:00:04.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":57},"Hardware","hardware",168,"2026-09-22T23:56:03.000Z",{"name":59,"slug":60,"count":61,"latest_published_at":18},"Science","science",133,{"name":63,"slug":64,"count":65,"latest_published_at":66},"Consumer Tech","consumer-tech",110,"2026-09-22T20:00:00.000Z",{"name":68,"slug":69,"count":70,"latest_published_at":71},"Software","software",80,"2026-09-22T23:32:52.000Z",{"name":73,"slug":74,"count":75,"latest_published_at":76},"Dev Tools","dev-tools",79,"2026-09-22T22:21:13.000Z",{"name":78,"slug":79,"count":80,"latest_published_at":81},"Startups","startups",65,"2026-09-22T22:06:48.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"Gaming","gaming",45,"2026-09-22T15:35:06.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"General","general",43,"2026-09-21T23:48:56.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",27,"2026-09-22T13:00:00.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]