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Reflection Steering cuts reasoning token usage by nearly 17% while maintaining accuracy, revolutionizing how LLMs handle reflection during inference.
Over-calling in language models can be reduced by 34% with a novel calibration method that preserves decision accuracy, revealing hidden dynamics in multi-teacher training.
