VideoEvoHarness: Self Evolving Agent Harness from Unlabeled Videos for Video Understanding
Video understanding agents rely on a harness to acquire evidence, use tools, and reason, but improving it requires substantial effort. Recent self evolution methods seek to optimize harnesses through execution feedback to reduce human intervention. However, existing methods often rely on annotated video questions and answers or focus on updating model parameters, leaving harness evolution from unlabeled videos underexplored. VideoEvoHarness jointly evolves a Questioner and a Solver harness from unlabeled videos with frozen model parameters. The Questioner constructs questions and computes reference answers directly from video memory records. Question quality assessments and Solver executi...
| Authors | Weixin Xu, Yifan Liu, Yi Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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