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One Extra Look Tightened the Box
A frozen vision-language model used its own first prediction to route a higher-resolution second pass.
Summary
A frozen vision-language model used its own first prediction to route a higher-resolution second pass.
Label-Free Precision Refinement sends predicted-small regions through one localized re-observation, then accepts a candidate only under fixed geometric guards. It improved multiple grounding datasets and two released specialist models, with the strongest strict-IoU gains in prospective and specialist tests, at roughly twice the latency. An unguarded control regressed, showing that the routing rule—not simply another pass—carried the result.
Why it matters
A frozen vision-language model used its own first prediction to route a higher-resolution second pass.
Limits and context
- Label-Free Precision Refinement sends predicted-small regions through one localized re-observation, then accepts a candidate only under fixed geometric guards.
- It improved multiple grounding datasets and two released specialist models, with the strongest strict-IoU gains in prospective and specialist tests, at roughly twice the latency.
- An unguarded control regressed, showing that the routing rule—not simply another pass—carried the result.
Key claims
A frozen vision-language model used its own first prediction to route a higher-resolution second pass.
Qualification: Label-Free Precision Refinement sends predicted-small regions through one localized re-observation, then accepts a candidate only under fixed geometric guards.
Evidence: source-2026-08-22-012
Sources
- arXiv preprint 2608.19553arXiv · primary research
Corrections
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