[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-ai-judges-infect-each-other-with-bias-in-multi-agent-systems":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},1769,"ai-judges-infect-each-other-with-bias-in-multi-agent-systems","AI Judges Infect Each Other With Bias in Multi-Agent Systems","New research shows that when LLMs evaluate each other's work, their biases spread across the agent network — but bigger review committees help.","AI evaluation bias turns out to be contagious.\n\nResearchers have published a formal framework called Contagion Networks that measures how evaluation biases spread when large language models act as judges in multi-agent pipelines. In a controlled experiment using three DeepSeek-chat agents — each given a different evaluator profile: structured, balanced, or evidence-based — they measured how strongly one agent's biases bled into another's. The answer: consistently, with contagion coefficients ranging from 0.157 to 0.352. That is lower than the 0.85-1.3 range seen in earlier cross-model work, but it still means bias is spreading even when every agent runs on the same underlying model.\n\nThis matters because multi-agent LLM systems are increasingly used to grade, filter, and rank AI-generated output — including in automated research pipelines and model evaluation benchmarks. If the evaluator agents share a systematic blind spot, that blind spot does not cancel out; it propagates. The finding puts a concrete number on a risk that most builders of these systems have treated as theoretical.\n\nThe paper also offers a practical fix: expanding the evaluator committee from one judge to three cuts effective contagion by 72.4%. That is a meaningful lever, though it also means tripling inference costs for every evaluation step. The researchers released their experimental framework as open-source, so teams can measure their own pipelines' contagion coefficients before assuming the problem does not apply to them.","[\"ai\",\"multi-agent\",\"llm\",\"research\"]","2026-06-19T04:00:00.000Z","2026-06-19T11:32:42.778Z","2026-06-19T14:22:18.868Z","published",null,[],"ai",[24,26,27,28],"multi-agent","llm","research",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.20493",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"]