Strategic Persuasion with Trait-Conditioned Multi-Agent Systems for Iterative Legal Argumentation

TL;DR

This paper introduces a multi-agent simulation environment for iterative legal argumentation using trait-conditioned large language models, demonstrating how strategic traits influence persuasion and decision outcomes.

Contribution

It presents the Strategic Courtroom Framework with trait-based agent control, evaluates diverse configurations, and introduces a reinforcement learning trait orchestrator for adaptive persuasion strategies.

Findings

Heterogeneous teams outperform homogeneous teams in legal argumentation.

Moderate interaction depth leads to more stable verdicts.

Traits like quantitative and charismatic significantly boost persuasive success.

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.