Reasoning large language models benefit from test-time scaling, but that scaling should be adaptive: spend extra tokens when they help and stop early when they do not. A new arXiv preprint (2610.25212) proposes a calibration-free early-stopping rule for chain-of-thought reasoning based on e-processes. The authors argue that existing stopping rules, which halt generation when a confidence or stability signal crosses a given level, are not sufficient for this adaptive control.
The paper's abstract is truncated in the source, so the full details of the e-process construction and experimental results are not available here. What is clear is the motivation: make the decision to continue or stop reasoning part of the model's inference policy, rather than a fixed budget or a post-hoc confidence check.
Since only this single source was provided, there are no independent findings to compare against; the summary above reflects only the preprint's abstract.