VideoEvoHarness: Self Evolving Agent Harness from Unlabeled Videos for Video Understanding
VideoEvoHarness jointly evolves a Questioner and a Solver harness from unlabeled videos with frozen model parameters.
ProofPaper ↗
Key points
- 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.
- This supports our foundational framework for harness evolution from unlabeled videos, opening a promising direction for self evolving video agents.
Sources (1)
- [1]VideoEvoHarness: Self Evolving Agent Harness from Unlabeled Videos for Video UnderstandingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 06:36 AM
VideoEvoHarness jointly evolves a Questioner and a Solver harness from unlabeled videos with frozen model parameters.
Video understanding agents rely on a harness to acquire evidence, use tools, and reason, but improving it requires substantial effort.
Extractive summary: sentences quoted from the sources.
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