ReasoningLens：大型推理模型的层次化可视化与诊断审计

摘要

大型推理模型的出现引入了异常冗长的思维链痕迹，形成了信息透明负担——关键逻辑常常被淹没在大量程序性文本之中。针对这一问题，我们提出ReasoningLens这一开源框架，专为复杂推理链的分层可视化与诊断审计而设计。ReasoningLens通过以下方式实现信息剖析：（1）将推理痕迹组织为交互式分层结构，将高层策略与低层执行相分离；（2）利用智能代理审计器进行自动化错误检测及增强型工具验证；（3）综合系统化推理特征轮廓，揭示模型特有盲区。通过将无结构的文本壁垒转化为可操作洞察，ReasoningLens为解读、调试并优化下一代以推理为核心的AI提供了模块化基础。

English

The emergence of Large Reasoning Models has introduced exceptionally long Chain-of-Thought traces, creating a transparency burden where critical logic is often buried under massive procedural text. To address this, we present ReasoningLens, an open-source framework designed for the hierarchical visualization and diagnostic auditing of complex reasoning chains. ReasoningLens addresses information necropsy by: (1) structuring traces into interactive hierarchies that separate high-level strategy from low-level execution; (2) leveraging an agentic auditor for automated error detection and tool-augmented verification; and (3) synthesizing systemic reasoning profiles to reveal model-specific blind spots. By transforming unstructured walls of text into actionable insights, ReasoningLens provides a modular foundation for interpreting, debugging, and optimizing the next generation of reasoning-centric AI.