VideoEvolve: Evolving Agent Harnesses for Video Temporal Grounding
Quick summary
arXiv:2610.01766v1 Announce Type: new Abstract: Video temporal grounding aims to localize events in videos from natural-language queries. For agents built around frozen video-language models, the harness determines how queries guide temporal predictions and how those predictions are refined. Manually refining these harnesses requires diagnosing grounding failures and coordinating changes to both agent workflows and instructions. We introduce VideoEvolve, a framework that automatically evolves agent harnesses for video temporal grounding. VideoEvolve uses a Cloze-Structured Harness Representati
Key takeaways
- arXiv:2610.01766v1 Announce Type: new Abstract: Video temporal grounding aims to localize events in videos from natural-language queries.
- For agents built around frozen video-language models, the harness determines how queries guide temporal predictions and how those predictions are refined.
- Manually refining these harnesses requires diagnosing grounding failures and coordinating changes to both agent workflows and instructions.
Why it matters
“VideoEvolve: Evolving Agent Harnesses for Video Temporal Grounding” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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