AI RESEARCH
Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning
arXiv CS.AI
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ArXi:2604.17399v1 Announce Type: new Large language models (LLMs) have nstrated strong reasoning capabilities, and as existing approaches for enhancing LLM reasoning continue to mature, increasing attention has shifted toward meta-reasoning as a promising direction for further improvement. However, most existing meta-reasoning methods remain episodic: they focus on executing complex meta-reasoning routines within individual instances, but ignore the accumulation of reusable meta-reasoning skills across instances, leading to recurring failure modes and repeatedly high metacognitive effort.