We introduce AgenticSimLaw, a role-structured, multi-agent debate framework that provides transparent and controllable test-time reasoning for high-stakes tabular decision-making tasks. Unlike black-box approaches, our courtroom-style orchestration explicitly defines agent roles (prosecutor, defense, judge), interaction protocols (7-turn structured debate), and private reasoning strategies, creating a fully auditable decision-making process. We benchmark this framework on young adult recidivism prediction using the NLSY97 dataset, comparing it against traditional chain-of-thought (CoT) prompting across almost 90 unique combinations of models and strategies. Our results demonstrate that structured multi-agent debate provides more stable and generalizable performance compared to single-agent reasoning, with stronger correlation between accuracy and F1-score metrics. Beyond performance improvements, AgenticSimLaw offers fine-grained control over reasoning steps, generates complete interaction transcripts for explainability, and enables systematic profiling of agent behaviors. While we instantiate this framework in the criminal justice domain to stress-test reasoning under ethical complexity, the approach generalizes to any deliberative, high-stakes decision task requiring transparency and human oversight. This work addresses key LLM-based multi-agent system challenges: organization through structured roles, observability through logged interactions, and responsibility through explicit non-deployment constraints for sensitive domains. Data, results, and code will be available on github.com under the MIT license.

翻译：本文提出AgenticSimLaw，一种角色结构化的多智能体辩论框架，为高风险表格决策任务提供透明且可控的测试时推理。区别于黑箱方法，我们的法庭式编排明确定义了智能体角色（公诉人、辩护人、法官）、交互协议（7轮结构化辩论）及私有推理策略，构建出完全可审计的决策流程。我们基于NLSY97数据集，在青年再犯预测任务上对该框架进行基准测试，在近90种模型与策略组合中与传统思维链提示进行对比。实验结果表明，相较于单智能体推理，结构化多智能体辩论能提供更稳定且可泛化的性能，其准确率与F1分数指标间呈现更强的相关性。除性能提升外，AgenticSimLaw支持对推理步骤的细粒度控制，生成完整的交互记录以实现可解释性，并能对智能体行为进行系统性分析。虽然我们在刑事司法领域实例化该框架以检验伦理复杂性下的推理能力，但该方法可泛化至任何需要透明度与人工监督的审议型高风险决策任务。本研究针对基于大语言模型的多智能体系统核心挑战提出解决方案：通过结构化角色实现组织化，通过交互日志实现可观测性，并通过敏感领域显式非部署约束实现责任界定。相关数据、结果与代码将在github.com以MIT许可证开源。