🤖 AI Summary
This work addresses the tendency of multi-agent systems in value-laden tasks to overlook normative uncertainty embedded in disagreement by overemphasizing consensus. To bridge the gap between subsymbolic reasoning in large language models and symbolic knowledge, the authors propose a symbolic knowledge representation layer that abstracts agents’ reasoning trajectories and decisions into four distinct disagreement states grounded in consistency and conclusiveness. Building upon this framework, they introduce a defeasible policy routing mechanism that enables disagreement-aware agent scheduling in content moderation tasks. This approach significantly enhances the system’s strategic reasoning capabilities in value-sensitive scenarios by explicitly modeling and leveraging normative divergence among agents.
📝 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 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.