[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83581-en":3,"doc-seo-83581-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83581,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Collaborative Disagreement Resolution for Scalable Oversight","Debate among AI agents has become a central approach to scalable oversight, yet it faces a conflict between persuasive incentives and epistemic honesty. This work introduces disagreement resolution as a collaborative truth-seeking paradigm that replaces adversarial adjudication. Using principles from human mediation, the method builds an automated pipeline where models identify points of disagreement, analyze evidence for conflicting claims, and converge to consensus or isolate the “crux.” Experiments show 62.1% judging accuracy versus 49.2% for standard debate.","Collaborative Disagreement Resolution for Scalable Oversight  \nYuyang Jiang * 1 Chacha Chen * 1 Teng Wu † 2 Liwen Sun † 3 Han Liu 1 Shi Feng 4 Chenhao Tan 1  \narXiv :2607 .0 125 1v 1 [ cs .CY] 2 Jun 2026  \nAbstract  \nDebate, where AI agents argue opposing positions, has emerged as a key approach to scalable oversight. However, debate faces a fundamental tension: models are incentivized to be persuasive to the judge, which may not always align with epistemic honesty. In this work, we propose an alternative paradigm: disagreement resolution, which reframes the interaction mechanism from adversarial debate to collaborative truth seeking.  \nDrawing on principles from human mediation and conflict resolution, where mediators facilitate dialogue to help disputing parties reach consensus rather than adjudicating between them, we design an automated pipeline that adapts these strategies to AI oversight. Unlike standard debate where models argue for fixed positions, our pipeline directs models to collaboratively identify points of disagreement, examine the evidence for conflicting claims, and converge toward consensus or isolate the specific “crux” of their disagreement. We find that Disagreement Resolution consistently helps non-expert models identify the truth, achieving 62.1% judging accuracy compared to 49.2% for standard debate. Our results provide encouraging empirical evidence for rethinking the scalable oversight protocol from adversarial persuasion to collaborative truth-seeking.  \n1. Introduction  \nScalable oversight is the problem of how humans can reliably supervise AI systems that become more capable than themselves (Bowman et al., 2022) . As LLMs begin solving  \n∗ Equal contribution as co-first authors. † Equal contribution asco-second authors. 1Department of Computer Science, University of Chicago, Chicago, IL, USA 2Microsoft, Redmond, WA, USA 3Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA 4Department of Computer Science, George Washington University, Washington, DC, USA. Correspondence to: Chenhao Tan \u003C[chenhao@uchicago.edu](chenhao@uchicago.edu) >.  \nProceedings of the 43 rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026 . Copyright 2026 by the author(s) .  \ncomplex mathematical proofs (Romera-Paredes et al., 2023), solving real-world software engineering tasks (Jimenez et al., 2023), or performing tricky medical diagnoses (McDuff et al., 2023), humans can no longer simply check the AI’s output for correctness (Zhou et al., 2024) . We need scalable oversight protocols to help supervise these systems.  \nDebate has emerged as the leading scalable oversight protocol. First proposed by Irving et al. (2018), it has two AI agents argue opposite positions on a question, with a human judge determining the winner. The central theoretical insight is that it is harder to lie than to refute a lie. However, Irving et al. explicitly acknowledge a key limitation: they question “will humans understand the debates” and admit that whether humans are sufficient judges remains an empirical question. A human judge may lack the capacity to reliably distinguish truth from a sophisticated lie once both exceed their reasoning horizon, or a debate could grow long enough that a human is unable to follow it. As the capability gap between human judges and frontier models widens, this limitation becomes increasingly critical.  \nMoreover, while some positive empirical results have been reported (Buhl et al., 2025 ; Irving et al., 2018 ; Kenton et al., 2024), most findings arise in information-asymmetric settings where the judge is denied access to reference materials, thus creating an aritificial barrier of scalable oversight. When this asymmetry is removed, recent work shows that debate can underperform even simple direct questionanswering baselines (Kenton et al., 2024) . This occurs because information asymmetry is a poor proxy for capability asymmetry: logical inconsistency is e","cbCaibJkjpgf6PoU","https://ap.wps.com/l/cbCaibJkjpgf6PoU","pdf",1268987,3,1,27,"English","en",105,"# Abstract\n# Introduction\n## Scalable oversight challenges\n## Limitations of debate protocols\n## Mediation and conflict resolution as an alternative\n## Disagreement Resolution (DR) overview","[{\"question\":\"What problem does the paper address in scalable oversight?\",\"answer\":\"Scalable oversight must reliably supervise AI systems that become more capable than human overseers, where humans can no longer verify correctness by simply checking outputs.\"},{\"question\":\"Why can standard AI debate be ineffective?\",\"answer\":\"Debate incentivizes models to be persuasive to a judge, and when the judge’s reasoning horizon is exceeded or information asymmetry is removed, even a knowledgeable judge can be misled.\"},{\"question\":\"How does disagreement resolution differ from standard debate?\",\"answer\":\"Disagreement resolution directs agents to collaboratively locate disagreement, examine evidence behind conflicting claims, and converge to consensus or isolate the specific crux, reducing the judge’s 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problem does the paper address in scalable oversight?","Question",{"text":75,"@type":76},"Scalable oversight must reliably supervise AI systems that become more capable than human overseers, where humans can no longer verify correctness by simply checking outputs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why can standard AI debate be ineffective?",{"text":80,"@type":76},"Debate incentivizes models to be persuasive to a judge, and when the judge’s reasoning horizon is exceeded or information asymmetry is removed, even a knowledgeable judge can be misled.",{"name":82,"@type":73,"acceptedAnswer":83},"How does disagreement resolution differ from standard debate?",{"text":84,"@type":76},"Disagreement resolution directs agents to collaboratively locate disagreement, examine evidence behind conflicting claims, and converge to consensus or isolate the specific crux, reducing the judge’s 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