Mole-Syn: Synthesizing Long-Horizon LLM Reasoning скачать в хорошем качестве

Mole-Syn: Synthesizing Long-Horizon LLM Reasoning

In this AI Research Roundup episode, Alex discusses the paper: 'The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning' This paper addresses the difficulty of instilling robust Long Chain-of-Thought reasoning in LLMs, which often suffer from incoherence during standard supervised fine-tuning. The authors propose a molecular hypothesis, viewing reasoning trajectories as stable macromolecular structures formed by three distinct types of behavioral bonds. These bonds categorize reasoning steps into deep-reasoning for continuity, self-reflection for consistency, and self-exploration for hypothesis probing. The research introduces Mole-Syn, a framework designed to synthesize effective reasoning structures by analyzing the topology of logical folding. Using sparse auto-encoders and 3D semantic mapping, the study demonstrates how these structural bonds improve long-horizon reasoning performance. Paper URL: https://arxiv.org/abs/2601.06002 #AI #MachineLearning #DeepLearning #LLM #ChainOfThought #Reasoning #GraphTheory #NLP