PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
PathCal is a training-free controller that adjusts how a large language model uses reflection markers like 'wait' or 'but'. It distinguishes between different marker types—continuation, revision, and alternative opening—and intervenes only when the model shows local uncertainty. This calibration improves the accuracy-length trade-off by favoring continuation over unnecessary branching.