arXiv (All)
open access
What Generation Knows: Unlocking Fine-Grained Understanding in Unified Multimodal Models
Fine-grained visual understanding requires distinguishing subtle differences in local attributes and spatial relations, yet remains challenging for existing vision-language models. Unified multimodal models (UMMs) offer a new opportunity through generation pathways that preserve rich visual informat
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
- UniRect-CoT: Enhancing Generation in Unified Multimodal Models via Reflective Rectification with Inherent Understanding#1128576
- Recursive Self-Improvement in Unified Multimodal Models#1187680
- UReason: Benchmarking Reasoning-to-Generation Alignment in Unified Multimodal Models#798766
- UReason: Benchmarking Reasoning-to-Generation Alignment in Unified Multimodal Models#797352
- Visual Abstention in Unified Multimodal Models#1217622
Record · ID 1388553
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