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Hugging Face Daily Papers·· 12 天前

Agent Harness 如何实现视频生成与智能体交互

Video Generation Meets Agent Harness: Agentic Video Generation, Agentic World Modeling and Recursive Self-Improvement

要点速览

Agent Harness 整合视频生成与世界模型,支持智能体在多镜头视频制作中进行规划、工具使用和递归自改进。该框架协调脚本、拍摄规划、资产管理、生产和编辑,并分析其评估挑战;同时研究智能体与世界模型在观察、控制、执行和状态维护中的交互,以及两者如何互相评估和改进。

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Published on Oct 29

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Abstract

Agent harnesses integrate video generation and world models to enable planning, tool use, and recursive self-improvement for autonomous multi-shot video production.

Video generation is shifting from end-to-end synthesis toward agentic generation, where planning, tool use, and feedback enable coherent, controllable multi-shot videos. In parallel, video world models provide action-conditioned observations and predicted futures, serving as interactive environments for agents. We study their intersection through the agent harness: an executable runtime that constructs context, exposes tools, maintains state, routes feedback, and manages budgets around video models. This report examines three directions. First, agentic video generation, where harnesses coordinate scripting, shot planning, asset management, production, and editing, alongside the evaluation challenges of these systems. Second, agentic world modeling, where harnesses mediate observation, control, execution, and state. We discuss hybrid designs in which game engines or agent-written programs maintain world state while video models render observations, and how agents and world models can mutually evaluate and improve one another. Third, recursive self-improvement (RSI) in video model development, distinguishing output revisions, updates to harness state, generator parameters, and evaluators, and changes to the improvement procedure itself. We identify how these improvements feed back into subsequent development. Across these directions, we analyze essential agent capabilities, including audiovisual perception, long-horizon planning, memory, code-based interaction, feedback-guided control, skill reuse, and multi-agent coordination. We conclude with challenges in autonomous video production, lifelong memory, programmable hybrid control, autonomous model development, provenance, cost accounting, and evaluation linking video quality and task performance to sustained improvements in models and harnesses.

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来源: Hugging Face Daily Papers · huggingface.co