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This paper proposes a metacognitive harness that separates monitoring from reasoning in LLMs, using pre-solve feeling-of-knowing and post-solve judgment-of-learning signals to control when to trust, retry, or aggregate answers, improving accuracy on text, code, and multimodal benchmarks without parameter updates.