Abstract

Legal mediation is an important mechanism for resolving social conflicts and handling disputes. It involves complex interpersonal interactions and unstructured decision-making processes, and therefore holds significant research value as a domain. Leveraging the outstanding logical reasoning capabilities of large language models, multi-agent systems for simulating complex social interactions have become a cutting-edge research direction in artificial intelligence, providing a new supporting vehicle and research pathway for the intelligent study and practical application of legal mediation. However, directly applying general-purpose multi-agent techniques or general-purpose, opaque LLMs to long-horizon, multi-party, and high-conflict professional mediation tasks exposes several deep-seated structural cognitive deficiencies, including a lack of process awareness, insufficient domain-specific intervention capabilities, and limited theory-of-mind reasoning. To address these challenges, this study proposes CogMed, a cognitively enhanced multi-agent framework for legal mediation simulation, which aims to compensate for the limitations of general models in professional strategic interactions through an explicit cognitive architecture. Rather than introducing entirely new individual reasoning modules, the proposed framework focuses on cognitively coordinated integration of process control, strategic intervention, and belief modeling mechanisms under legal mediation settings. CogMed models the mediation process as a Finite State Machine (FSM) to capture macro-level decision logic and introduces a Strategic Toolkit (STK) that serves as a set of action primitives for micro-level interventions. Meanwhile, a Dynamic Belief Tracking (DBT) mechanism is incorporated into party agents to simulate psychological anticipation and strategic reasoning during negotiation. Experimental results demonstrate that CogMed effectively improves both mediation success rates and the quality of negotiated outcomes. Furthermore, the findings suggest a preliminary framework-level compensation pattern under the current experimental setting, where cognitively structured coordination mechanisms may partially enhance the mediation capability of medium-scale models. These preliminary experimental observations suggest that cognitively structured coordination mechanisms may partially compensate for certain limitations associated with model scale under the current controlled mediation setting, thereby offering a potential research direction for cognitively structured legal mediation simulation systems under controlled experimental settings.

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