HalluTracer: Pre-Decoding Truthfulness Prediction via Depth-Averaged Probe-Logit
Quick summary
arXiv:2608.16353v2 Announce Type: replace-cross Abstract: Internal-state probes enable truthfulness prediction before a large language model generates an answer. When detectors change both the layers they read and the rules used to combine them, the source of improved prediction becomes difficult to identify. We separate these choices and find that retaining more layers improves prediction even under fixed equal weighting. An exact Fisher-ratio decomposition explains why the additional benefit of linear reweighting is limited on these probe scores: information inlayer-wise differences largely
Key takeaways
- arXiv:2608.16353v2 Announce Type: replace-cross Abstract: Internal-state probes enable truthfulness prediction before a large language model generates an answer.
- When detectors change both the layers they read and the rules used to combine them, the source of improved prediction becomes difficult to identify.
- We separate these choices and find that retaining more layers improves prediction even under fixed equal weighting.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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