Bytedance just dropped a paper that might change how AI thinks.
— Bo Wang (@BoWang87) February 21, 2026
Literally.
They figured out why LLMs fail at long reasoning — and framed it as chemistry.
The discovery:
Chain-of-thought isn't just words. It's molecular structure.
Three bond types:
• Deep reasoning =… pic.twitter.com/uYwNeKp1zs
Qiguang Chen et al., The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning, arXiv:2601.06002v2 [cs.CL], https://doi.org/10.48550/arXiv.2601.06002
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.
