Is Token Confidence Just Chain Length? A Signal-Processing Audit of Reasoning Traces

The paper investigates whether token‑confidence scores used to prune reasoning chains in language models actually reflect model certainty or merely chain length. By treating per‑token confidence as a signal and applying spectral analysis and change‑point detection, the authors find that the windowed‑minimum confidence score largely correlates with how long a chain is, offering no additional predictive power for correctness once length is accounted for. Although the confidence spectrum contains a weak signal of rapid token‑to‑token fluctuation in incorrect chains, it is too faint to improve majority voting, and early‑stopping methods based on confidence or change detection do not outperform simply sampling fewer chains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

• llms
• benchmarks