Knowing Isn't Always Saying: When Do Spatial Encodings Reach Answers in Vision-Language Models?
Vision-language models are known to encode spatial information in their hidden states, yet often fail to use it when answering. However, it remains unclear when and where this encoded information reaches the answer. We address this with direction patching, a class-conditioned causal intervention applied across layers, token positions, and prompt formats. Using spatial-ID directions constructed following prior encoding evidence, we find that causal influence on answer logits emerges only at mid-to-deep depths. Text chain-of-thought suppresses immediate object-word argmax-level transport in most models, while visually grounded prompts keep it open. Positive target-logit gain can remain belo...
| Authors | Zeyu Wang, Xinming Xu |
|---|---|
| Primary category | cs.CL |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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