Probing Perceptual Priors of MLLMs via Gibbs Sampling with Interpretable Generative Controls

arXiv cs.AIAugust 31, 2026en

arXiv:2608.27727v1 Announce Type: new Abstract: A model's behavior on a task is jointly determined by the input it receives and the prior it brings in, i.e. the distribution over stimuli it implicitly expects. Interpretability research has traditionally studied models by holding inputs fixed and examining model responses either mechanistically, probing how internal structure represents inputs, or behaviorally, measuring how variation in inputs leads to variation in outputs. Neither reconstructs the prior distribution itself, since internal structure shows what a model can represent, not what it expects, and any fixed stimulus set leaves most of the possible input space unseen. In particular,

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