arXiv (All)
open access
DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering
Real-world degradations such as blur, shadow, distortion, and moire patterns severely impair the document question-answering capabilities of Multimodal Large Language Models (MLLMs). Applying restoration tools before Visual Question Answering (VQA) is an intuitive solution. However, existing restora
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering#789257
- HyperErase: Scale-Calibrated Hypernetwork for Multi-Concept Erasure in Text-to-Image Models#1105712
- DocPC: Document-Level Visual Retrieval via Representative Page Composition#780736
- DocPC: Document-Level Visual Retrieval via Representative Page Composition#779361
- Resolution as a First-Class Decision: Task-Conditioned Routing for Efficient Multimodal Large Language Models#1129550
Record · ID 793261
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive