ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Jun Zhang, Jiasheng Zheng, Boxi Cao, Yaojie Lu, Hongyu Lin, Jia Zheng, Xianpei Han, Le Sun
• Venue: arXiv: cs.CL
• Published: 2026-06-22
• Collected: 2026-06-23
• URL: http://arxiv.org/abs/2606.23404v1
• PDF: https://arxiv.org/pdf/2606.23404v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces ReasoningLens, an open-source framework designed to address the transparency issue in Large Reasoning Models by creating hierarchical visualizations and diagnostic auditing tools for complex reasoning chains.

What the Document Says

• The emergence of Large Reasoning Models results in exceptionally long Chain-of-Thought traces, causing a transparency burden.
• ReasoningLens is an open-source framework designed to hierarchically visualize and audit complex reasoning chains from these models.
• ReasoningLens addresses the issue of information necropsy by structuring traces into interactive hierarchies, separating high-level strategy from low-level execution.
• An agentic auditor is used in ReasoningLens for automated error detection and tool-augmented verification.
• ReasoningLens synthesizes systemic reasoning profiles to reveal model-specific blind spots.
• By transforming unstructured text into actionable insights, ReasoningLens provides a modular foundation for interpreting, debugging, and optimizing the next generation of reasoning-centric AI.

Key Concepts Mentioned

• Large Reasoning Models
• Chain-of-Thought traces
• transparency burden
• information necropsy
• hierarchical visualization
• diagnostic auditing
• agentic auditor
• systemic reasoning profiles
• modular foundation

Claims Made by the Document

• The long Chain-of-Thought traces in Large Reasoning Models cause a transparency burden.
• ReasoningLens can address this issue by structuring the traces and providing hierarchical visualizations and diagnostic tools.
• ReasoningLens uses an agentic auditor for automated error detection and verification.

Evidence Used

• The text explains that the long Chain-of-Thought traces in Large Reasoning Models cause a transparency burden and introduces ReasoningLens as a solution.
• The text states that ReasoningLens addresses information necropsy by structuring traces into interactive hierarchies and using an agentic auditor.

Methods / Approach

• Hierarchical visualization
• Diagnostic auditing
• Use of an agentic auditor

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The data does not specify any limitations of ReasoningLens by the AI.

Open Questions

• What is the exact methodology used for hierarchical visualization?
• How does the agentic auditor work in detail?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The text does not provide detailed information on how ReasoningLens's hierarchical visualization and agentic auditor work.
• It is unclear what specific techniques are used for synthesizing systemic reasoning profiles.

Raw Abstract / Excerpt

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.