eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion

ArXi:2606.02054v1 Announce Type: new While Large Language Models (LLMs) achieve impressive performance on multi-step reasoning tasks, their reliability is persistently hindered by critical limitations such as unconstrained hallucinations and poor numerical computation. Fundamentally, these issues arise because standard models treat reasoning as a transient, one-off generation process rather than retaining and refining successful procedural logic.