ReasoningLens：大型推理模型的層次化可視化與診斷性審計

摘要

大型推理模型的興起帶來了極長的思維鏈（Chain-of-Thought）軌跡，導致大量程序性文字淹沒關鍵邏輯，形成透明度負擔。為解決此問題，我們提出 ReasoningLens——一個專為複雜推理鏈設計的開源框架，可進行層級式視覺化與診斷性審計。ReasoningLens 透過以下方式實現資訊剖析：（1）將推理軌跡結構化為互動式層級，區分高層策略與低層執行；（2）利用代理型審計器進行自動錯誤檢測與工具輔助驗證；（3）綜合系統性推理特徵，揭示模型特定盲點。透過將非結構化的文字牆轉化為可操作洞察，ReasoningLens 為解讀、除錯與優化下一代推理導向人工智慧提供了模組化基礎。

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.