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MASCAL introduces a confidence-aware weighting mechanism: during training it increases the learning weight on positions sensitive to upstream probabilities, enabling agents to learn more from decisions that actively use the signal.
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10/30/2026
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UnverifiedThe MASCAL method lets each agent jointly optimize task performance and confidence calibration during reinforcement learning, learning to generate calibrated correct probabilities for its answers.79% similarUnverifiedThe study finds that learning concentrates local sensitivity on task-relevant factors: measured by quantum Fisher information, the sensitivity of the representation to each input factor is redistributed during training, with task-relevant dimensions gaining higher sensitivity and irrelevant dimensions being compressed.71% similarUnverifiedHaving activation function parameters participate in optimization may introduce new training instability factors, making the process more sensitive to learning rate and initialization.71% similarUnverifiedDuring training, the metacognitive controller is optimized over agent trajectories via reinforcement learning or a differentiable decision objective to balance correctness against computational cost.71% similarUnverifiedIn-context learning in the tabular domain typically relies on attention mechanisms that let the prediction sample interact directly with the training samples in the context, equivalent to soft retrieval weighting at inference time.71% similar
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