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, the authors present ReasoningLens, an open-source framework designed for the hierarchical visualization and diagnostic auditing of complex reasoning chains.
- Structures traces into interactive hierarchies that separate high-level strategy from low-level execution.
- Leverages an agentic auditor for automated error detection and tool-augmented verification.
- Synthesizes systemic reasoning profiles to reveal model-specific blind spots.
ReasoningLens provides a modular foundation for interpreting, debugging, and optimizing the next generation of reasoning-centric AI by transforming unstructured walls of text into actionable insights.