[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-ai-agents-still-struggle-to-search-academic-literature":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},1758,"ai-agents-still-struggle-to-search-academic-literature","AI Agents Still Struggle to Search Academic Literature","A new benchmark called ScholarQuest tests AI research agents on real literature search tasks — and the best systems top out at 0.314 Recall@100.","A research team has released ScholarQuest, a benchmark designed to measure how well AI agents actually find academic papers in realistic conditions.\n\nScholarQuest draws on over 1,000 computer science topics and four types of research queries: method-oriented, setting-anchored, comparison-based, and scope-controlled. The benchmark includes a shared retrieval backend called ScholarBase to make results reproducible across different systems. Crucially, it tests agents in open literature environments — not curated, closed datasets that flatter the models being evaluated.\n\nThe results are a reality check. The best-performing agent hit 0.314 Recall@100 and 0.355 Recall@All, meaning it missed roughly two-thirds of relevant papers even when given 100 tries. Agentic methods — iterative, intent-driven search loops — did outperform single-shot retrieval, which at least validates the architectural direction even if the scores are modest.\n\nFor context, the AI research community has leaned heavily on agent-based pipelines as a fix for shallow retrieval, but this benchmark suggests the plumbing still leaks. Anyone selling an AI-powered literature review tool today is working with systems that miss most of what they're looking for.","[\"ai\",\"research\",\"benchmarks\",\"llm\"]","2026-06-19T04:00:00.000Z","2026-06-19T11:21:34.848Z","2026-06-19T14:22:18.585Z","published",null,[],"ai",[24,26,27,28],"research","benchmarks","llm",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.20235",0,{"sections":35},[36,40,44,49,54,59,64,68,72,77,82,87,92,97],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",491,"2026-06-19T14:59:11.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":18},"Security","security",132,{"name":45,"slug":46,"count":47,"latest_published_at":48},"Policy","policy",88,"2026-06-16T09:26:09.000Z",{"name":50,"slug":51,"count":52,"latest_published_at":53},"Consumer Tech","consumer-tech",78,"2026-06-16T17:58:24.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Hardware","hardware",62,"2026-06-18T15:24:16.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":63},"Deals","deals",58,"2026-06-19T14:43:50.000Z",{"name":65,"slug":66,"count":62,"latest_published_at":67},"Software","software","2026-06-16T20:00:00.000Z",{"name":69,"slug":70,"count":71,"latest_published_at":18},"Dev Tools","dev-tools",50,{"name":73,"slug":74,"count":75,"latest_published_at":76},"Science","science",38,"2026-06-18T04:00:00.000Z",{"name":78,"slug":79,"count":80,"latest_published_at":81},"Gaming","gaming",31,"2026-06-16T15:25:13.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"General","general",26,"2026-06-13T18:35:15.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"Startups","startups",23,"2026-06-16T15:00:00.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",19,"2026-06-14T08:00:00.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]