Question-Guided Evidence Acquisition for Multimodal Visual Question Answering
Multimodal LLMs can see a document, but they often can't read it reliably. Small text, tables, visual cues, and topological elements still trip them up under direct visual inference, even when the page is already sitting in the model's context. Most document-VQA systems treat perception as fixed: they encode the page once, ask the question, and answer from whatever the model happened to extract in that single fast pass. We think document VQA needs slower, more deliberate perception: rather than answering from one fixed encoding, the model should spend a bit of extra compute at inference time working out what to look at next, and only then answer. We build this into \textbf{Q-Guide}, a sma...
| Authors | Alin-Ionut Popa |
|---|---|
| Primary category | cs.CV |
| Published | 20 August 2026 |
| Last updated at source | 24 August 2026 |
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