Strategic Persuasion with Trait-Conditioned Multi-Agent Systems for Iterative Legal Argumentation
arXiv:2604.07028v2 Announce Type: replace-cross 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
The rapid advancement of explainable AI and multi-agent system research is enabling more sophisticated applications, pushing the boundaries of AI in complex adversarial environments.
This development indicates a significant step towards AI systems capable of nuanced, strategic interaction and persuasion, which has profound implications for white-collar work and professional domains.
AI is moving beyond mere information processing to become an active, strategic participant in complex argumentative and negotiation scenarios previously exclusive to human experts.
- · AI agents developers
- · Legal tech industry
- · Large Language Model researchers
- · Organisations seeking automated strategic argumentation
- · Traditional legal advocacy services
- · Human-only arbitration/negotiation services
- · Software companies without AI integration
AI systems will begin to play more active roles in legal and diplomatic processes, shifting human involvement to oversight and final decision-making.
The development of highly persuasive AI could lead to new ethical and regulatory challenges regarding manipulation and the nature of truth in discourse.
These systems could accelerate the resolution of complex disputes and negotiations, potentially streamlining international relations and legal proceedings.
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