基于特质条件的多智能体系统在迭代法律论证中的战略说服
Strategic Persuasion with Trait-Conditioned Multi-Agent Systems for Iterative Legal Argumentation
AI总结：
提出战略法庭框架，通过特质条件化的大语言模型智能体模拟多轮法律辩论，发现异质团队表现更优，并引入强化学习特质编排器动态优化辩护策略。
摘要：
在诸如法律、外交和谈判等对抗性领域中的战略互动是通过语言中介的，然而大多数博弈论模型忽略了通过话语运作的说服机制。我们提出了战略法庭框架，这是一个多智能体模拟环境，其中由特质条件化的大语言模型（LLM）智能体组成的控方和辩方团队参与迭代的、基于轮次的法律论证。智能体使用九种可解释的特质进行实例化，这些特质被组织成四种原型，从而能够系统控制修辞风格和战略取向。我们在10个合成法律案例和84个三特质团队配置上评估该框架，使用DeepSeek-R1和Gemini 2.5 Pro进行了超过7,000次模拟试验。我们的结果表明，具有互补特质的异质团队始终优于同质配置，适度的交互深度产生更稳定的判决，并且某些特质（特别是量化和魅力型）对说服成功贡献不成比例。我们进一步引入了一个基于强化学习的特质编排器，该编排器根据案件和对手团队动态生成辩护特质，发现优于静态、人类设计的特质组合的策略。这些发现共同证明了语言可以被视为第一类战略行动空间，并为构建能够在多智能体环境中进行自适应说服的自主智能体提供了基础。
英文摘要：
Strategic interaction in adversarial domains such as law, diplomacy, and negotiation is mediated by language, yet most game-theoretic models abstract away the mechanisms of persuasion that operate through discourse. We present the Strategic Courtroom Framework, a multi-agent simulation environment in which prosecution and defense teams composed of trait-conditioned Large Language Model (LLM) agents engage in iterative, round-based legal argumentation. Agents are instantiated using nine interpretable traits organized into four archetypes, enabling systematic control over rhetorical style and strategic orientation. We evaluate the framework across 10 synthetic legal cases and 84 three-trait team configurations, totaling over 7{,}000 simulated trials using DeepSeek-R1 and Gemini~2.5~Pro. Our results show that heterogeneous teams with complementary traits consistently outperform homogeneous configurations, that moderate interaction depth yields more stable verdicts, and that certain traits (notably quantitative and charismatic) contribute disproportionately to persuasive success. We further introduce a reinforcement-learning-based Trait Orchestrator that dynamically generates defense traits conditioned on the case and opposing team, discovering strategies that outperform static, human-designed trait combinations. Together, these findings demonstrate how language can be treated as a first-class strategic action space and provide a foundation for building autonomous agents capable of adaptive persuasion in multi-agent environments.
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