An iterative metacognitive feedback loop lets a fast LLM match or outperform a standalone reasoning model on graph coloring and code debugging, with selective fallback to the reasoning model only when needed.
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Language Models Coupled with Metacognition Can Outperform Reasoning Models
An iterative metacognitive feedback loop lets a fast LLM match or outperform a standalone reasoning model on graph coloring and code debugging, with selective fallback to the reasoning model only when needed.