The Knowing-Saying Gap: When Probes See Errors that Confidence Misses
Linear probes detect corrupted context in language models with near-perfect accuracy, yet this does not translate into reliable failure prediction. The result is a dissociation with direct implications for deployment monitoring. Across multi-hop arithmetic chains, probes that detect corruption turn out to be uninformative about final answer correctness; models forced into structured confidence formats collapse to two values with indistinguishable error rates; and probe persistence across hops fails to separate correct from incorrect outcomes, refuting our pre-registered "persistence beats peak" hypothesis. This pattern of knowing but not saying generalises across model families including ...
| Authors | Jyotin Goel, Ipshita Bandyopadhyay, Justin Shenk |
|---|---|
| Primary category | cs.AI |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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