[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-cheap-agent-pipelines-beat-fancy-training-on-arc-agi-1":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},4468,"cheap-agent-pipelines-beat-fancy-training-on-arc-agi-1","Cheap Agent Pipelines Beat Fancy Training on ARC-AGI-1","Researchers hit 67% on ARC-AGI-1 using an open-weight model and a two-stage agent harness for under a dollar per task.","A pair of agentic architectures reached near-state-of-the-art scores on a standard AI reasoning benchmark without expensive training or brute-force compute.\n\nResearchers tested DeepSeek V3.2 in non-thinking mode on the ARC-AGI-1 public evaluation set, which contains 400 pattern-reasoning tasks. Rather than fine-tuning on ARC data or running exhaustive sampling, they built two agent harnesses on top of the base model. The first, an Explorer-Definer Pipeline, splits the work into two stages: one agent discovers patterns, another synthesizes executable transformations. The second, a Reflective Orchestrator, adds a feedback loop that triggers fresh exploration when prior hypotheses fail. The pipeline scored 57.50% pass@2 at $0.25 per task; the orchestrator reached 67.25% pass@2 at $0.62 per task. Both beat a 15.50% one-shot baseline by roughly 52 points.\n\nMost published ARC-AGI-1 progress has come from one of two expensive routes: frontier models burned through with extended chain-of-thought and evolutionary search, or small models fine-tuned specifically on ARC data. This work carves out a third lane - structured agent architecture - and shows it can close most of the gap at a fraction of the cost. The finding also has a diagnostic edge: the analysis suggests the pipeline is bottlenecked by generation diversity, not by which candidates it selects, and the orchestrator's re-exploration loop confirms that prediction directly.\n\nOne ablation worth noting: removing the pipeline's internal think tool dropped pass@2 by 5.75 points, a reminder that even \"non-thinking\" setups benefit from explicit reasoning scaffolding. Whether these results hold outside a controlled benchmark is, as always, the open question.","[\"ai\",\"benchmarks\",\"agents\",\"reasoning\"]","2026-07-09T04:00:00.000Z","2026-07-09T04:29:12.826Z","2026-07-09T04:29:15.711Z","published",null,[],"ai",[24,26,27,28],"benchmarks","agents","reasoning",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.06764",0,{"sections":35},[36,40,45,50,55,60,65,70,75,80,84,88,93,98],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":18},"Gaming","gaming",41,{"name":85,"slug":86,"count":83,"latest_published_at":87},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":89,"slug":90,"count":91,"latest_published_at":92},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":94,"slug":95,"count":96,"latest_published_at":97},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":99,"slug":100,"count":101,"latest_published_at":102},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]