A gent C ourt: Simulating Court with Adversarial Evolvable Lawyer Agents (2025)

Abstract: Current research in LLM-based simulation systems lacks comprehensive solutions for modeling real-world court proceedings, while existing legal language models struggle with dynamic courtroom interactions. We present **AgentCourt**, a comprehensive legal simulation framework that addresses these challenges through adversarial evolution of LLM-based agents. Our AgentCourt introduces a new adversarial evolutionary approach for agents called **AdvEvol**, which performs dynamic knowledge learning and evolution through structured adversarial interactions in a simulated courtroom program, breaking the limitations of the traditional reliance on static knowledge bases or manual annotations. By simulating 1,000 civil cases, we construct an evolving knowledge base that enhances the agents’ legal reasoning abilities. The evolved lawyer agents demonstrated outstanding performance on our newly introduced **CourtBench** benchmark, achieving a 12.1% improvement in performance compared to the original lawyer agents. Evaluations by professional lawyers confirm the effectiveness of our approach across three critical dimensions: cognitive agility, professional knowledge, and logical rigor. Beyond outperforming specialized legal models in interactive reasoning tasks, our findings emphasize the importance of adversarial learning in legal AI and suggest promising directions for extending simulation-based legal reasoning to broader judicial and regulatory contexts.

## introduction large language models (llms) have shown remarkable success in simulating real-world professional scenarios, from medical consultations to educational interactions @xcite . however, in the legal domain, comprehensive simulation of court proceedings remains an underexplored challenge. while existing legal language models excel at static tasks such as legal provision retrieval and question answering @xcite , they struggle with dynamic courtroom interactions. for instance, these models can accurately recite articles of civil law and regulations but often fail to leverage them effectively in adversarial court debates. more critically, models like chatlaw-33b @xcite exhibit severe overfitting to standardized legal tasks, sometimes losing the ability to generate coherent responses in interactive courtroom scenarios. to address these limitations, we present agent-court, an innovative framework for simulating civil court proceedings through llm-based agents. unlike previous approaches that focus on specific legal tasks, our system creates a complete courtroom environment where multiple agents-including judges, attorneys, and other participants-engage in structured legal discourse, as shown in figure 1 . at its core, agentcourt employs an adversarial evolution (advevol) method that enables continuous knowledge acquisition through simulated court interactions, eliminating the need for extensive manual annotation, fine-tuning, or specialized legal pre-training. a key innovation of our approach is its automated knowledge evolution mechanism. by simulating court proceedings, lawyer agents construct and refine three specialized knowledge bases: a legal provisions memory for statutory understanding, an experience base for debate strategies, and a case library for precedent analysis. starting with only complaints and defense statements from real cases, agents engage in adversarial debates to autonomously build and evolve their legal knowledge. this multi-faceted knowledge structure, combined with our adversarial learning strategy, enables agents to develop sophisticated legal reasonhighlight key components corresponding to formula (1) and formula (7) in section 3.3, which utilize knowledge from previous cases to assist in answering questions and enable continuous learning through post-trial reflection. through the simulation of 1,000 civil cases, we demonstrate significant improvements in the legal capabilities of our agents. our evolved agents achieve performance comparable to gpt-4o on dynamic courtroom tasks while significantly outperforming specialized legal models. a particularly noteworthy finding is the contrast in model behavior: while existing legal models like chatlaw-33b perform well on standardized tasks, they struggle significantly with dynamic courtroom dialogue, often failing to generate valid responses. to facilitate systematic evaluation of such capabilities, we introduce courtbench, a dedicated benchmark designed to assess interactive legal reasoning. our findings underscore the importance of interactive learning in developing robust legal ai systems capable of handling dynamic legal scenarios. the main contributions of our work include: through self-play court debates, agents autonomously construct and refine legal expertise across three specialized knowledge bases: a legal provisions memory for statutory understanding, an experience base for debate strategies, and a case library for precedent analysis. this self-evolving approach facilitates continuous expansion of knowledge without manual annotation, offering a scalable solution for future acquisition of legal knowledge. ## conclusion in this paper, we introduced agentcourt, a simulation framework for courtroom scenarios that leveraged large language models (llms) and agent-based adversarial evolution. our approach enabled legal agents to dynamically acquire knowledge and refine argumentation strategies, addressing the limitations of traditional static legal ai systems. through adversarial evolution, agents improved their legal reasoning capabilities by continuously adapting to new case contexts. the integration of a structured three-tier knowledge base, comprising legal provisions, case precedents, and strategic experience, allowed for more comprehensive legal understanding and reasoning. empirical results demonstrated that gpt-4o-mini-1000 consistently outperformed both general-purpose models and specialized legal models, achieving stateof-the-art performance in dynamic courtroom dia-logues. additionally, we introduced courtbench, a benchmark designed to evaluate legal ai models based on real courtroom interactions rather than static legal knowledge retrieval, further validating the effectiveness of our approach. while our work provided significant advancements in legal ai, future research could focus on handling more complex legal scenarios, improving role adaptability, and extending the framework to diverse legal systems. by open-sourcing our privacy-anonymized datasets and implementation, we aimed to facilitate further research in this domain.