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An Investigation of Model Coherence: Narrow Finetunes Contradict Themselves Under Resampling
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Robert Graham, Yariv Barsheshat, Phil Blandfort, Sabri Alouache

· 1 min read

ResearcharXiv cs.AI

An Investigation of Model Coherence: Narrow Finetunes Contradict Themselves Under Resampling

arXiv:2610.12129v1 Announce Type: new Abstract: A large body of research measures model coherence based on output variance without adequately considering competing causes. We identify two such causes, ambiguity and indifference, and we introduce a set of 175 questions where contradicting answers cannot easily be explained by either. We then measure incoherence in terms of contradictions when resampling answers to the same question. In contrast to other methods our metric has high specificity, and only ranks models as incoherent when the issues are glaring. Even so, we find narrow finetunes score poorly. Inspecting inconsistencies flagged by our method, we find that model organisms from the literature display severe issues such as identity conflation, introspection failures and rationalizations. These findings suggest that the pathologies induced by narrow finetuning may limit what these models can tell us about coherent misaligned behaviour.

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This story was published by arXiv cs.AI and written by Robert Graham, Yariv Barsheshat, Phil Blandfort, Sabri Alouache. SyncAI.news shows a preview; the complete article is on the publisher's site.

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