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This research maps Long CoT trajectories in LLMs as topological structures driven by deep-reasoning, self-reflection, and self-exploration interactions.

The Mole-Syn distribution-transfer-graph method synthesizes effective semantic isomers to facilitate fast entropy convergence and stabilize reinforcement learning.

This structural approach minimizes trajectory competition during fine-tuning and improves performance across reasoning benchmarks.

Screenshot 2026-03-09 at 1.54.53 PM.png Sources: Paper

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