Researchers have unveiled Reson, an innovative AI model engineered to emulate metacognitive processes, granting it the ability to analyze its own reasoning. This marks a significant departure from conventional AI development, shifting focus from mere performance benchmarks to the underlying mechanisms of machine thought. Built upon a fine-tuned LLaMA-7B architecture, Reson distinguishes itself from standard language models by reflecting on its internal reasoning, recognizing cognitive patterns, and dynamically adjusting its strategies based on these self-aware insights. Training involved approximately 11,000 instruction-response pairs, leveraging decades of cognitive science research on metacognition, specifically adapted for large language models. Although Reson’s outputs may sometimes appear unconventional or speculative, this is because the primary goal is to foster metacognitive abilities and adaptive problem-solving, rather than perfect factual accuracy. Reson represents a foundational step towards more sophisticated reasoning simulations. The model is publicly accessible on Hugging Face for experimentation and further research.