VideoEvolve: Evolving Agent Harnesses for Video Temporal Grounding
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 Representation that preserves stage interfaces while leaving agent workflows and instructions open to evolution. Branch-Guided Harness Evolution preserves pr...
| Authors | Bingjun Luo, Yuhuan Fan, Jialin Guo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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