Vision-language models often use descriptions of earlier visual states to make decisions about the current scene. When the scene changes, stale language can redirect an otherwise correct visual judgment toward an outdated answer. We study this failure as visual lock-in in a controlled grounding setting where only the verbalized prior varies. Across models, stronger lock-in accompanies smaller changes in the model representation before the final answer. This reversal suggests that lock-in depends not on how far this representation moves, but on how that movement is organized. In models that are harder to correct, prior-induced changes concentrate along a compact set of directions that repeatedly appear across examples. We call these recurrent axes the Prior Directions. They recur on held-out examples, while a descriptive four-model comparison associates greater concentration with stronger lock-in. Controlled interventions show that removing the component aligned with the Prior Directions restores visual grounding, whereas removing an equally large orthogonal component has little effect. Prior control thus arises when prior-induced changes form a coherent and reusable pattern in the representation used to produce the answer. This account explains why the same prior remains revisable in one model yet becomes dominant in another.
A small detector is trained to predict the internal development of answer readiness and stop reasoning at that point, connecting the internal development of answer readiness to a practical rule for allocating reasoning computation.
Visual distinctions are often finer than those reflected in linguistic conceptualization. Vision-language models exhibit a similar asymmetry: a distinction can remain discriminable in frozen image geometry while being weakly addressable through the native text interface. We study this gap by separating visual discrimin...
Woosang Jeon, Jiwon Yang, Chung Soo et al.· 0 citations
irection patching is addressed with direction patching, a class-conditioned causal intervention applied across layers, token positions, and prompt formats to reframe the encoding-grounding gap as a problem of conditional transport in VLMs.
The complexity of ensuring consistent behavior as models become increasingly multimodal and retrieval-augmented is illustrated, showing that the longer processing time associated with resolving visual entities prevents the suppression of the model's usual factual recall mechanism, thus resulting in more parametric answ...
GroundBench is introduced, a diagnostic benchmark that separates explanations through six branch-and-merge conditions, each adding a controlled information bundle, and a counterfactual re-ask targeting a real alternate part visible in the same image.
A controlled evaluation framework for interactive visual grounding in large vision-language models (LVLMs) is introduced, varying how much target information is provided upfront and how much must be acquired through dialogue.
Zhengxiang Wang, Owen Rambow· 0 citations
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