Question-Guided Evidence Acquisition for Multimodal Visual Question Answering
Quick summary
arXiv:2608.19739v2 Announce Type: replace-cross Abstract: 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 mod
Key takeaways
- arXiv:2608.19739v2 Announce Type: replace-cross Abstract: 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.
Why it matters
“Question-Guided Evidence Acquisition for Multimodal Visual Question Answering” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
Member comments