Problem
Research questions and friction points this paper is trying to address.
video production
dynamic task orchestration
multimodal asset routing
task-centric framework
FRAMEWORKERS: A Dynamic Multi-Agent Framework for AI-Generated Video Production
🤖 AI Summary
本文提出FRAMEWORKERS框架，通过多代理动态协调解决AI视频制作中的任务管理和资产调度问题，提高视频质量和任务覆盖范围。
📝 Abstract
Modern video generators excel at synthesizing individual clips, but complete video production requires coordinating a long sequence of interdependent creative steps, including scripting, storyboarding, generation, and editing. It further demands persistent asset management and dynamic task orchestration as intermediate outputs, dependencies, and execution states evolve over time. Existing automated systems typically rely on rigid pipelines that are difficult to adapt to diverse inputs and changing workflows, while general-purpose large language models (LLMs) remain unreliable for long-horizon orchestration and multimodal asset routing. We introduce FRAMEWORKERS, a task-centric and workspace-grounded multi-agent framework for open-ended video production. A central Director formulates video creation as dynamic task management, continuously editing a Task Stack to determine which subtask to execute next and which sub-agent to invoke. An Assistant serves as the execution layer, grounding each selected task in a shared Workspace, retrieving the required assets and context, invoking the assigned sub-agent, and persisting the resulting artifacts. Execution capabilities are exposed through modular sub-agents with registered descriptors, allowing new sub-agents to be integrated without redesigning the orchestration workflow. To improve orchestration reliability, we fine-tune the Director via supervised fine-tuning (SFT) followed by Group Relative Policy Optimization (GRPO) for descriptor-conditioned task routing. Experiments show that FRAMEWORKERS outperforms strong LLM planners in routing accuracy, recovers reliably from runtime failures, generalizes to unseen sub-agents without retraining, and achieves higher end-to-end video quality and broader task coverage than fixed pipelines, single-agent systems, and prior multi-agent approaches.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Task Management
Multi-Agent Framework
Modular Sub-Agents
Supervised Fine-Tuning
Group Relative Policy Optimization
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