# Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLM

arXiv: https://arxiv.org/abs/2505.13890 · HTML: https://mlsanity.com/paper/2505.13890/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 20 May 2025.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 12 of 12)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs](https://mlsanity.com/paper/2412.21187/) (2024)
- [Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models](https://mlsanity.com/paper/2503.16419/) (2025)
- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [From System 1 to System 2: A Survey of Reasoning Large Language Models](https://mlsanity.com/paper/2502.17419/) (2025)
- [Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning](https://mlsanity.com/paper/2502.18080/) (2025)
- [The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer](https://mlsanity.com/paper/2502.15631/) (2025)
- [Code Comprehension then Auditing for Unsupervised LLM Evaluation](https://mlsanity.com/paper/2410.03131/) (2024)

## Built on by (most cited first, 21 of 21)

- [Unveiling the Potential of Diffusion Large Language Model in Controllable Generation](https://mlsanity.com/paper/2507.04504/) (2025)
- [VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraft](https://mlsanity.com/paper/2508.18722/) (2025)
- [ContextNav: Towards Agentic Multimodal In-Context Learning](https://mlsanity.com/paper/2510.04560/) (2025)
- [The Shape of Reasoning: Topological Analysis of Reasoning Traces in Large Language Models](https://mlsanity.com/paper/2510.20665/) (2025)
- [Do LLM Agents Know How to Ground, Recover, and Assess? A Benchmark for Epistemic Competence in Information-Seeking Agents](https://mlsanity.com/paper/2509.22391/) (2025)
- [Gated Differentiable Working Memory for Long-Context Language Modeling](https://mlsanity.com/paper/2601.12906/) (2026)
- [Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures](https://mlsanity.com/paper/2601.19928/) (2026)
- [ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning](https://mlsanity.com/paper/2512.00831/) (2025)
- [Characterizing, Evaluating, and Optimizing Complex Reasoning](https://mlsanity.com/paper/2602.08498/) (2026)
- [Modeling Hierarchical Thinking in Large Reasoning Models](https://mlsanity.com/paper/2510.22437/) (2025)
- [Transformers in the Dark: Navigating Unknown Search Spaces via Bandit Feedback](https://mlsanity.com/paper/2603.24780/) (2026)
- [SliceGraph: Mapping Process Isomers in Multi-Run Chain-of-Thought Reasoning](https://mlsanity.com/paper/2605.14619/) (2026)
- [Bridging the Plausibility-Validity Gap by Fine-Tuning a Reasoning-Enhanced LLM for Chemical Synthesis and Discovery](https://mlsanity.com/paper/2507.07328/) (2025)
- [MINERVA-Cultural: A Benchmark for Cultural and Multilingual Long Video Reasoning](https://mlsanity.com/paper/2601.10649/) (2026)
- [Do LLMs Experience an Internal Polylogue? Investigating Reasoning through the Lens of Personas](https://mlsanity.com/paper/2605.09159/) (2026)
- [TraceGraph: Shared Decision Landscapes for Diagnosing and Improving Agent Trajectories](https://mlsanity.com/paper/2605.31308/) (2026)
- [Auditing Privacy Risks in LLM-Enhanced Graph Neural Networks](https://mlsanity.com/paper/2608.25727/) (2026)
- [From Concentration to Differentiation and Back: Routing Effective Rank in MoE Reasoning Cohorts](https://mlsanity.com/paper/2609.06403/) (2026)
- [When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces](https://mlsanity.com/paper/2609.21247/) (2026)
- [Selecting Diverse SFT Traces Improves Post-RL Generalization](https://mlsanity.com/paper/2609.33780/) (2026)
- [TRACE: Trajectory Selection for Parallel Scaling of Search Agents](https://mlsanity.com/paper/2609.39912/) (2026)

## Abstract (arXiv)

Recent advances in test-time scaling have enabled Large Language Models (LLMs) to display sophisticated reasoning abilities via extended Chain-of-Thought (CoT) generation. Despite their potential, these Reasoning LLMs (RLMs) often demonstrate counterintuitive and unstable behaviors, such as performance degradation under few-shot prompting, that challenge our current understanding of RLMs. In this work, we introduce a unified graph-based analytical framework for better modeling the reasoning processes of RLMs. Our method first clusters long, verbose CoT outputs into semantically coherent reasoning steps, then constructs directed reasoning graphs to capture contextual and logical dependencies among these steps. Through comprehensive analysis across models and prompting regimes, we reveal that structural properties, such as exploration density, branching, and convergence ratios, strongly correlate with reasoning accuracy. Our findings demonstrate how prompting strategies substantially reshape the internal reasoning structure of RLMs, directly affecting task outcomes. The proposed framework not only enables quantitative evaluation of reasoning quality beyond conventional metrics but also provides practical insights for prompt engineering and the cognitive analysis of LLMs. Code and resources will be released to facilitate future research in this direction.