Your model already knows it's wrong. Asking costs 0.06 seconds and zero tokens.

Zero-Token Confidence (ZTC) is a proposed method for assessing language-model answer reliability by reading a model’s final hidden-state representation rather than asking it to state its confidence or generating a separate verification response. On the 2,018-item FINAL-Bench benchmark, the article reports that a Darwin-397B ZTC probe achieved the highest verifier AUC of 0.7394 while generating no tokens, compared with 0.5000 for self-reported confidence and 0.7335 for JEV; in self-readout tests on held-out items, it reports substantially higher accuracy for hidden-state readout than direct confidence questions. The approach uses a small calibrated linear probe distributed as a 45 KB file, requires a single forward pass, and is presented as much faster than token-generating verifiers, with a reported gate time of 0.0615 seconds. The authors argue that hidden-state width and the proportion of full-attention layers predict probe performance more effectively than model parameter count, citing weaker results from a larger 180B-class model. In a browser-based action-gating simulation, ZTC reportedly improved execution outcomes and throughput over both no gating and text-reading verification, while the project provides downloadable probes and a public interactive demonstration.

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