🤖 AI Summary
Existing game-theoretic models in adversarial domains such as law often overlook language as a mechanism of persuasion, failing to capture the nuanced, discourse-driven nature of strategic interaction. This work proposes a Strategic Courtroom Framework that treats language as a first-class strategic action space. It introduces a heterogeneous multi-agent system grounded in nine interpretable personality traits and incorporates a reinforcement learning–driven dynamic trait orchestrator to generate adaptive persuasive strategies tailored to opponents and case specifics. Evaluated using DeepSeek-R1 and Gemini 2.5 Pro across 10 synthetic cases, 84 three-trait combinations, and over 7,000 simulated trials, the framework demonstrates that heterogeneous agent teams significantly outperform homogeneous ones, and dynamically composed traits surpass handcrafted strategies, with quantitative and charismatic traits contributing most prominently to persuasive efficacy.
📝 Abstract
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.