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 every shape, yet the counts it states in the same response sum to 188. From Seeing to Reporting (FSR) is a theory of visual reporting and a set of inference-time procedures that improve the reports of frozen VLMs. It models reporting as a chain from responses to decoded records, constructed sets and task answers, linked by task-compatible matching. The chain gives exact relations for deleting rec...
| Authors | Junzhe Shi, Yuan Gan, Shida Jiang |
|---|---|
| Primary category | cs.CV |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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