arXiv — Machine Learning · · 3 min read

Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading

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Computer Science > Machine Learning

arXiv:2608.05675 (cs)
[Submitted on 6 Aug 2026]

Title:Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading

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Abstract:Label-free reliability for vision-language models rests on invariance: perturb the input and a faithful reader's answer should not change. This has a known blind spot, a systematic misreading survives the perturbation and gets certified wrong, which we show is computable, not just real: an error is invisible to an edit exactly when the two commute, so the errors a suite cannot reach form its joint centralizer, a set that shrinks as edits are added and can be written down rather than guessed at. We act on the complementary relation, equivariance: edit a figure's data and the correct answer must change by a computable amount. Two matched edits are provably complete for affine reading errors; no suite of swap edits is complete for label permutations, and cyclic relabeling closes most of that gap. We instantiate the theory as the Equivariance-Consistency Score, a label-free, training-free detector, and release REND-EQUIV, pairing matched invariance and equivariance sets over identical data. The predicted ordering holds across three models and a hand-labeled population immune to the one circularity in how it is selected; a second invariance-family method confirms the blind spot belongs to the relation, not to any implementation; and cyclic relabeling delivers its predicted gain on a matched real sample. The same characterization explains a reported inversion of this ordering in the classifier metamorphic-testing literature: detectability is a joint property of the relation and the fault class, never of the relation alone.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2608.05675 [cs.LG]
  (or arXiv:2608.05675v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.05675
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Rasul Khanbayov [view email]
[v1] Thu, 6 Aug 2026 07:14:58 UTC (266 KB)
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