arXiv (All)
open access
From Seeing to Reporting: A Theory of Reliable Visual Set Generation in Vision-Language Models
Vision-language models (VLMs) are asked to report what they see: to count objects, box them, parse a screen or pick a click target (GUI grounding). We argue that reporting is part of the visual task, not merely its formatting. In a dense scene with 173 shapes, GPT-6.1 lists 173 records that match ev
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
- Focus Where It Counts: A Salience-Driven Vision-Language Model for Low Vision Assistance#784969
- Focus Where It Counts: A Salience-Driven Vision-Language Model for Low Vision Assistance#783379
- MG-VQA: Manipulation Grounded Visual Question Answering with VLMs#1198986
- From Knowing to Abstaining: Bridging the Representation-Action Gap in Vision-Language Models#1116984
- VisConf: Quality-Aware Consensus for Best-of-N in Vision-Language Model Reasoning#1389472
Record · ID 1400328
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