The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning

2026-01-09

arXiv.org

AI Summary

This work addresses the challenge that large language models struggle to effectively acquire long-chain-of-thought (Long CoT) reasoning capabilities through imitation of humans or non-Long CoT models. It introduces a novel perspective by modeling Long CoT trajectories as molecular-like topological structures, composed of three interacting components: deep reasoning, self-reflection, and self-exploration. The study further proposes the concept of “effective semantic isomers,” revealing that stable learning is achievable only when the bonding structure facilitates rapid entropy convergence. Building on this insight, the authors present Mole-Syn, a method that integrates trajectory distillation with distributional transfer graph analysis to automatically identify and synthesize efficient, stable Long CoT structures. Experiments demonstrate that Mole-Syn significantly enhances both long-chain reasoning performance and reinforcement learning training stability across multiple benchmarks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Large language models (LLMs) often fail to learn effective long chain-of-thought (Long CoT) reasoning from human or non-Long-CoT LLMs imitation. To understand this, we propose that effective and learnable Long CoT trajectories feature stable molecular-like structures in unified view, which are formed by three interaction types: Deep-Reasoning (covalent-like), Self-Reflection (hydrogen-bond-like), and Self-Exploration (van der Waals-like). Analysis of distilled trajectories reveals these structures emerge from Long CoT fine-tuning, not keyword imitation. We introduce Effective Semantic Isomers and show that only bonds promoting fast entropy convergence support stable Long CoT learning, while structural competition impairs training. Drawing on these findings, we present Mole-Syn, a distribution-transfer-graph method that guides synthesis of effective Long CoT structures, boosting performance and RL stability across benchmarks.

Problem

• Long Chain-of-Thought
• large language models
• reasoning
• imitation learning
• molecular structure

Innovation

• Long Chain-of-Thought
• Molecular Structure Analogy
• Effective Semantic Isomers
• Mole-Syn
• Entropy Convergence