mlsanityData to 7 Oct 2026

ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models

arXiv 2606.23404 ↗First posted 22 Jun 2026cs.CL cs.AIcited by 1

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 34 of its 38 references that are in the map. First posted on arXiv 22 Jun 2026.

Builds on

34 of 38 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Modeling Hierarchical Thinking in Large Reasoning Models25 Oct
  2. 2025Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say25 Sep
  3. 2025Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt29 May
  4. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models20 Mar

Other references · 30

  1. 2025Qwen3 Technical Reportcited by 8,195
  2. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  3. 2025OpenAI GPT-5 System Cardcited by 910
  4. 2022PAL: Program-aided Language Modelscited by 635
  5. 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 533
  6. 2025Reasoning Models Don't Always Say What They Thinkcited by 388
  7. 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Modelscited by 291
  8. 2025Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetycited by 222
  9. 2025Chain-of-Thought Reasoning In The Wild Is Not Always Faithfulcited by 166
  10. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 123
  11. 2025DeepSeek-R1 Thoughtology: Let's think about LLM Reasoningcited by 91
  12. 2024ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLMcited by 73
18 more
  1. 2025Are DeepSeek R1 And Other Reasoning Models More Faithful?cited by 69
  2. 2025Safety in Large Reasoning Models: A Surveycited by 58
  3. 2026The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligencecited by 50
  4. 2025Monitoring Monitorabilitycited by 47
  5. 2025Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?cited by 43
  6. 2024CodeJudge: Evaluating Code Generation with Large Language Modelscited by 41
  7. 2025Leaky Thoughts: Large Reasoning Models Are Not Private Thinkerscited by 25
  8. 2025A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Modelscited by 22
  9. 2026Large Language Model Reasoning Failurescited by 20
  10. 2025Early External Safety Testing of OpenAI's o3-mini: Insights from the Pre-Deployment Evaluationcited by 13
  11. 2025Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Modelscited by 9
  12. 2025Emerging Cyber Attack Risks of Medical AI Agentscited by 8
  13. 2025Improving Human Verification of LLM Reasoning through Interactive Explanation Interfacescited by 7
  14. 2025ReasonGraph: Visualisation of Reasoning Pathscited by 6
  15. 2025Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Basecited by 6
  16. 2026ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Tracescited by 5
  17. 2025ReTrace: Interactive Visualizations for Reasoning Traces of Large Reasoning Modelscited by 3
  18. 2025An Empirical Study of Reasoning Steps in Thinking Code LLMscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Beneath the Surface of Chains-of-Thought: A Mechanistic Interpretation of Reasoning Operations in LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026When the Chain Breaks: Interactive Diagnosis of LLM Chain-of-Thought Reasoning Errors
  2. 2026Reasoning Structure of Large Language Models
  3. 2025ReasoningFlow: Semantic Structure of Complex Reasoning Traces
  4. 2026ZEBRAARENA: A Diagnostic Simulation Environment for Studying Reasoning-Action Coupling in Tool-Augmented LLMs
  5. 2026Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
  6. 2025The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
  7. 2026RAudit: A Blind Auditing Protocol for Large Language Model Reasoning
  8. 2026Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models