The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning
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.
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Qiguang Chen
claimed_verified
2026-01-12T10:33:48.803Z
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Qiguang Chen
LightChen2333
user
LightChen2333
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Yantao Du
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Ziniu Li
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Jinhao Liu
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Songyao Duan
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Jiarui Guo
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Minghao Liu
claimed_verified
2026-02-25T17:33:23.250Z
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minghao
Liam-Liu
user
Liam-Liu
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Jiaheng Liu
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Tong Yang
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Ge Zhang
claimed_verified
2026-01-12T10:33:51.170Z
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Ge Zhang
zhangysk
user
zhangysk
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Libo Qin
6964644c138cc47cbd7652a6
Wanxiang Che
6964644c138cc47cbd7652a7
Wenhao Huang
The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning
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.
Large language models struggle with long chain-of-thought reasoning due to unstable structural patterns, but a molecular-inspired approach using effective semantic isomers and distribution-transfer-graph methods improves training stability and performance.
chain-of-thought large language models Long CoT fine-tuning entropy convergence semantic isomers distribution-transfer-graph molecular-like structures deep reasoning self-reflection self-exploration