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This paper introduces a margin-based confidence ranking method for LLM-as-a-judge systems, learning a dedicated estimator to ensure monotonicity between confidence and human-disagreement risk, with generalization guarantees and improved ranking accuracy across datasets.
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.