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Removing unreliable messages recovers part of the lost accuracy, suggesting that communication should be selective based on upstream reliability and the evidence already available to the downstream agent.
CREDO reads the confidence from a dedicated token pair in the model's output distribution and trains it by differentiable regression, turning the trained confidence into a signal for accuracy, weighting rollouts by how far confidence and outcome disagree, so that accuracy and calibration improve together.
A reinforcement learning framework that converts rubric-level functional feedback into localized optimization signals over generated code and aligns evaluator-generated textual attributions with responsible code spans and generated tokens is proposed.
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