arXiv — cs.AI preprintsInternational2 October 2026
VideoEvolve: Evolving Agent Harnesses for Video Temporal Grounding
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
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
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Heavy-Tailed Memory Traces in Long-Horizon Language Agents2 October 2026
- When Do Causal World Models Help Modular LLM Agents2 October 2026
- From Proposal to Verified Effect: Praxa, an Evidence-Bound Harness for Governed AI Agent Execution2 October 2026
- What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA2 October 2026
- Measuring the Microtask Eligibility Gap: When Is an Off-the-Shelf SLM Enough for an Agent Harness?2 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.