📄 中文摘要
法官智能体是代理式AI框架中的核心组件，负责提供自动化评估并促进推理过程的迭代自完善。JAF（Judge Agent Forest）框架提出了一种新颖的法官智能体运作模式，它不再孤立地评估主智能体生成的每个查询-响应对，而是对一组查询-响应对进行联合推理。通过这种协同推理机制，法官智能体从单一的局部评估器转变为一个全面的学习者。该框架的核心思想是利用智能体群体行为中的模式和相关性，从而更深入地理解主智能体的性能和潜在问题。当法官智能体分析一个由多个查询-响应对组成的“森林”时，它能够识别出主智能体在不同情境下的表现一致性、错误模式的重复性以及特定推理步骤的有效性。
📄 English Summary
JAF: Judge Agent Forest
Judge agents are pivotal components in agentic AI frameworks, responsible for automated evaluation and enabling iterative self-refinement of reasoning processes. JAF (Judge Agent Forest) introduces a novel paradigm where the judge agent conducts joint inference across a cohort of query-response pairs generated by a primary agent, rather than evaluating each in isolation. This approach elevates the judge from a local evaluator to a holistic learner. The core principle of JAF lies in leveraging patterns and correlations within the collective behavior of the agent cohort to gain a deeper understanding of the primary agent's performance and potential issues. By analyzing a 'forest' of multiple query-response pairs, the judge agent can identify consistency in the primary agent's behavior across different contexts, recurring error patterns, and the effectiveness of specific reasoning steps. For instance, if the primary agent consistently makes errors at a particular logical juncture when processing multiple queries of the same type but with varying parameters, traditional isolated evaluation might only flag individual errors. In contrast, JAF, by comparing these errors, can identify underlying systemic flaws. This global perspective enables the judge agent to conduct more nuanced and insightful evaluations, uncovering trends and weaknesses that single evaluations might miss. Through joint inference, JAF can more accurately diagnose reasoning bottlenecks in the primary agent and provide more precise guidance for subsequent self-correction.