From Vision to Language: Investigating Causal Information Flow in Multimodal Decision-Making
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Computer Science > Computation and Language
arXiv:2609.05149 (cs)
[Submitted on 4 Sep 2026]
Title:From Vision to Language: Investigating Causal Information Flow in Multimodal Decision-Making
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Abstract:Vision-Language Models are commonly evaluated through their final predictions, but understanding whether these decisions are grounded in visual evidence requires tracing how visual information contributes to language-based decisions. With this purpose in mind, we investigate cross-modal information flow in a video-based generative multiple-choice-like setting by applying a layer-wise causal intervention on video-text attention pathways. We target spatial, causal, and temporal visual reasoning. Our results show that visual information is mainly integrated while the model processes the candidate answer options, which serve as the primary textual grounding sites for the final decision. We further show that nouns play an important role as semantic anchors during multimodal enrichment, while verbs are more relevant when temporal relations are processed. Finally, we identify a distinct pattern in temporal reasoning, suggesting that VLMs struggle to reconstruct sequential information across video frames, but we remark that such fragility may also reflect linguistic biases associated with specific temporal expressions used for defining the relation between events within a scene.
| Comments: | Accepted at Findings of EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2609.05149 [cs.CL] |
| (or arXiv:2609.05149v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.05149
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled From Vision to Language: Investigating Causal Information Flow in Multimodal Decision-Making, by Davide Testa and 4 other authors
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