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Consensus is Strategically Insufficient: Reasoning-Trace Disagreement as a Knowledge-Representation Signal

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arXiv:2606.04223v1 Announce Type: new Abstract: Multi-agent systems are commonly designed to reduce disagreement through voting, consensus protocols, debate, or fault-tolerant aggregation. We argue that this objective is insufficient for value-laden tasks, where disagreement may reflect genuine normative uncertainty rather than agent error. Building on prior work on reasoning-trace disagreement in human-AI collaborative moderation, we propose a knowledge-representation layer in which reasoning t

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    Computer Science > Artificial Intelligence [Submitted on 2 Jun 2026] Consensus is Strategically Insufficient: Reasoning-Trace Disagreement as a Knowledge-Representation Signal Michał Wawer, Jarosław A. Chudziak Multi-agent systems are commonly designed to reduce disagreement through voting, consensus protocols, debate, or fault-tolerant aggregation. We argue that this objective is insufficient for value-laden tasks, where disagreement may reflect genuine normative uncertainty rather than agent error. Building on prior work on reasoning-trace disagreement in human-AI collaborative moderation, we propose a knowledge-representation layer in which reasoning traces and agent decisions are abstracted into symbolic disagreement states. Given agents producing explicit reasoning traces and binary decisions, we distinguish four states according to reasoning similarity and conclusion agreement: convergent agreement, divergent agreement, convergent disagreement and divergent disagreement. These states support defeasible strategic routing rules. We instantiate the framework in content moderation and argue that disagreement-aware routing provides a bridge between sub-symbolic LLM deliberation and symbolic knowledge representation for multi-agent strategic reasoning. Comments: Accepted to LAMAS&SR workshop at FLoC 2026 (KR + ICPL + LICS + CP + FSCD) Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.04223 [cs.AI]   (or arXiv:2606.04223v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.04223 Focus to learn more Submission history From: Michał Wawer [view email] [v1] Tue, 2 Jun 2026 21:21:02 UTC (216 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Jun 04, 2026
    Archived
    Jun 04, 2026
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