AgentXRay: 通过工作流重建实现对代理系统的白盒化
AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction
AI总结：
AgentXRay通过基于搜索的框架实现对代理系统的白盒化，通过工作流重建生成可编辑的显式工作流，提高可解释性和可控性。
摘要：
大型语言模型在复杂问题解决中展现出强大的能力，然而许多代理系统仍然难以解释和控制，因为其内部工作流不透明。虽然一些框架提供了显式的协作架构，但许多部署的代理系统对用户来说仍然是黑盒。我们通过引入代理工作流重建（AWR）来解决这一问题，这是一种新的任务，旨在通过仅使用输入-输出访问来合成一个显式、可解释的替代工作流，以近似黑盒系统。我们提出了AgentXRay，一种基于搜索的框架，将AWR表述为一个离散代理角色和工具调用的组合优化问题，在链式结构的工作流空间中。与模型蒸馏不同，AgentXRay生成可编辑的白盒工作流，这些工作流在可观察的、基于输出的代理度量下匹配目标输出，而无需访问模型参数。为了在庞大的搜索空间中导航，AgentXRay采用增强的红黑剪枝机制的蒙特卡洛树搜索，该机制动态地将代理质量与搜索深度相结合。在跨多样领域的实验中，AgentXRay在代理相似性和减少token消耗方面优于未剪枝的搜索，使其在固定迭代预算下能够进行更深入的工作流探索。
英文摘要：
Large Language Models have shown strong capabilities in complex problem solving, yet many agentic systems remain difficult to interpret and control due to opaque internal workflows. While some frameworks offer explicit architectures for collaboration, many deployed agentic systems operate as black boxes to users. We address this by introducing Agentic Workflow Reconstruction (AWR), a new task aiming to synthesize an explicit, interpretable stand-in workflow that approximates a black-box system using only input--output access. We propose AgentXRay, a search-based framework that formulates AWR as a combinatorial optimization problem over discrete agent roles and tool invocations in a chain-structured workflow space. Unlike model distillation, AgentXRay produces editable white-box workflows that match target outputs under an observable, output-based proxy metric, without accessing model parameters. To navigate the vast search space, AgentXRay employs Monte Carlo Tree Search enhanced by a scoring-based Red-Black Pruning mechanism, which dynamically integrates proxy quality with search depth. Experiments across diverse domains demonstrate that AgentXRay achieves higher proxy similarity and reduces token consumption compared to unpruned search, enabling deeper workflow exploration under fixed iteration budgets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
