arXiv AI By Bingjun Luo, Yuhuan Fan, Jialin Guo, Siqi Li

VideoEvolve: Evolving Agent Harnesses for Video Temporal Grounding

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VideoEvolve is a framework that automatically evolves agent harnesses for video temporal grounding, a task that localizes events in videos based on natural-language queries. It introduces a Cloze-Structured Harness Representation to keep stage interfaces stable while allowing agent workflows and instructions to change, and uses Branch-Guided Harness Evolution to preserve promising code branches, guide local edits with execution feedback, and validate improvements. Experiments show that this automated evolution improves grounding performance across multiple benchmarks, with instruction refinement consistently yielding gains.

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arXiv AI
Sep 1

FRAMEWORKERS: A Dynamic Multi-Agent Framework for AI-Generated Video Production

FRAMEWORKERS is a task‑centric, multi‑agent framework designed for end‑to‑end AI‑generated video production. It uses a central Director to dynamically manage a task stack and an Assistant to execute tasks within a shared Workspace, leveraging modular sub‑agents that can be added without redesigning the workflow. The system is fine‑tuned with supervised learning and policy optimization, outperforming existing LLM planners and fixed pipelines in routing accuracy, failure recovery, and overall video quality.

By Zhendong Li, Lei Sun, Letian Shi, Deheng Zhang, Ruibo Ming, Mengshun Hu, Dannong Xu, Jian Wang, Danda Paudel, Luc Van Gool, Jinjin Gu