[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-llama-mirrors-human-cooperation-qwen-plays-it-rational":10,"sections":40},{"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":30,"tags":31,"sources":35,"feedback":39,"feedback_at":22,"cost_usd":39,"total_tokens":39},3307,"llama-mirrors-human-cooperation-qwen-plays-it-rational","Llama Mirrors Human Cooperation; Qwen Plays It Rational","A game-theory study of 121 dyadic games finds Llama replicates human cooperation patterns while Qwen tracks Nash equilibrium predictions instead.","Three open-source LLMs were put through 121 game-theory experiments — and they behaved very differently from each other.\n\nResearchers tested Llama, Mistral, and Qwen across four classical game types to see how closely each model mirrors human decision-making. Llama reproduced human cooperation patterns with high fidelity and shared what the study calls an \"envious\" decision profile with human participants. Qwen, by contrast, aligned closely with Nash equilibrium predictions — the coldly rational outcome game theorists calculate on paper. Mistral exhibited its own distinct behavioral profile, separate from both human-like cooperation and pure Nash logic. An attention-based analysis added a mechanistic angle: Llama processes payoff information in a structured, layer-dependent way that Qwen and Mistral both lack, which the researchers point to as a likely explanation for Llama's closer alignment with human behavior.\n\nThe practical stakes are real. LLMs are already being deployed as decision-making agents in high-stakes settings and as stand-ins for human participants in social-science simulations — a use that quietly assumes the models actually behave like people. This study suggests that assumption holds for some models and fails for others, which matters a lot depending on which one you pick.\n\nNotably, the human-level replication happened without persona-based prompting, which simplifies the simulation pipeline considerably — though it also raises the question of why Llama converged on human behavior in the first place, a question the attention analysis starts to answer but does not close.","[\"ai\",\"research\",\"llms\",\"game-theory\"]","2026-07-02T04:00:00.000Z","2026-07-02T07:01:47.899Z","2026-07-02T07:01:51.336Z","published",null,[24],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"The dek names 'Llama' and 'Qwen' without version strings, which is acceptable, but the body states Mistral 'landed somewhere in between, without a clean match to either humans or pure game theory' — this contradicts the source, which says Qwen and Mistral 'exhibit different profiles' (implying Mistral has its own distinct profile, not a midpoint), and the body omits that the attention-based analysis found Qwen and Mistral both lack Llama's structured payoff processing, making the 'in between' ch","resolved","ai",[30,32,33,34],"research","llms","game-theory",[36],{"name":37,"url":38},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.04500",0,{"sections":41},[42,46,51,56,61,66,71,76,81,86,91,95,100,105],{"name":43,"slug":30,"count":44,"latest_published_at":45},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":50},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":52,"slug":53,"count":54,"latest_published_at":55},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":60},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":62,"slug":63,"count":64,"latest_published_at":65},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":87,"slug":88,"count":89,"latest_published_at":90},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":92,"slug":93,"count":89,"latest_published_at":94},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":101,"slug":102,"count":103,"latest_published_at":104},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":106,"slug":107,"count":108,"latest_published_at":109},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]