[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-open-weight-kozuchi-agent-leads-open-models-on-bug-repair-tests":10,"sections":41},{"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":36,"feedback":40,"feedback_at":22,"cost_usd":40,"total_tokens":40},5385,"open-weight-kozuchi-agent-leads-open-models-on-bug-repair-tests","Open-Weight Kozuchi Agent Leads Open Models on Bug Repair Tests","A 27B-parameter open-weight coding agent ranks first among open models on two SWE-bench tracks, placing 4th of 42 on Java and 12th of 135 on Python overall.","A research team has released Kozuchi Agent, an open-weight AI system that turns bug reports into code patches without any closed-source model access.\n\nKozuchi Agent runs on a locally hosted, unmodified Qwen3.5-27B model, with no fine-tuning, using eight-way test-time selection (TTS@8) to pick among candidate patches. On the official SWE-bench Verified evaluator, it resolved 374 of 500 instances. On Multi-SWE-bench, the same agent fixed 41 of 128 Java issues (32.03%), placing fourth of 42 overall and first among strict open-weight entrants; on Python it ranked 12th of 135, again first among open-weight systems. The researchers report per-phase behavior held steady within five percentage points across languages, and that a reusable CI pipeline cut the manual steps needed to run evaluations from five down to one across mixed internal clusters.\n\nThe result matters less as a leaderboard placement and more as a demonstration that open, locally run models can compete on structured, tool-heavy engineering tasks, not just chat benchmarks. Teams wary of sending proprietary code to a hosted API get a credible alternative, provided they have the GPU budget for a 27-billion-parameter model plus eight-way sampling per bug. The paper also localizes the remaining gap: mostly patches that pass tests but fail on semantic correctness, plus selection errors, not weaker code editing or model access, which quiets one common excuse for open-weight underperformance.\n\nThe paper frames this as favorable among open and local peers by parameter count, not a claim to beat the field outright, a distinction worth keeping since fourth of 42 and 12th of 135 still leave real ground between Kozuchi and the top closed-model entries.","[\"ai agents\",\"open-weight models\",\"coding benchmarks\",\"software repair\"]","2026-08-18T04:00:00.000Z","2026-08-18T18:03:05.686Z","2026-08-18T18:03:17.592Z","published",null,[24],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"The dek claims Kozuchi is 'matching much larger closed models' and the headline claims it 'rivals closed models,' but the source never states the parameter sizes of the higher-ranked closed models or otherwise supports a size\u002Fparity comparison — rewrite the headline and dek to stick to the verified ranking facts (e.g., 4th of 42 on Java, 12th of 135 on Python) without asserting an unsupported size comparison.","resolved","ai",[32,33,34,35],"ai agents","open-weight models","coding benchmarks","software repair",[37],{"name":38,"url":39},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.15579",0,{"sections":42},[43,47,51,56,61,66,71,76,81,85,90,95,100,105],{"name":44,"slug":30,"count":45,"latest_published_at":46},"AI",3293,"2026-08-20T04:00:00.000Z",{"name":48,"slug":49,"count":50,"latest_published_at":46},"Security","security",435,{"name":52,"slug":53,"count":54,"latest_published_at":55},"Policy","policy",210,"2026-08-19T09:32:27.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":60},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":62,"slug":63,"count":64,"latest_published_at":65},"Hardware","hardware",140,"2026-08-19T18:25:42.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Consumer Tech","consumer-tech",95,"2026-08-18T16:05:00.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Science","science",90,"2026-08-19T18:41:02.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Software","software",73,"2026-08-18T07:51:50.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":18},"Dev Tools","dev-tools",69,{"name":86,"slug":87,"count":88,"latest_published_at":89},"Startups","startups",47,"2026-08-19T19:13:46.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"General","general",33,"2026-08-18T22:18:13.000Z",{"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"]