- ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
To overcome these issues, we propose ReAgent-V, a novel agentic video understanding framework that integrates efficient frame selection with real-time reward generation during inference
- ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
Specifically, ReAgent-V evaluates each trajectory across multiple axes—such as task success, temporal stability, visual grounding, and semantic precision—and performs multi-agent reflection to produce refined, high-fidelity reward scores for alignment
- ReAgent-V: A Reward-Driven Multi-Agent Framework for Video . . .
To overcome these issues, we propose ReAgent-V, a novel agentic video understanding framework that integrates efficient frame selection with real-time reward generation during inference
- ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
Workshop: Multi-Agent Systems in the Era of Foundation Models: Opportunities, Challenges and Futures ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
- dblp: ReAgent-V: A Reward-Driven Multi-Agent Framework for Video . . .
Bibliographic details on ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
- arxiv ReAgent-V: A Reward-Driven Multi-Agent Framework for Video . . .
阅 读 基本信息 讨论区 词汇表 译者 Star 0 名称 ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding 首页 https: yiyibooks cn arxiv 2506 01300v1 index html 原始地址 https: arxiv org pdf 2506 01300 描述
- ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
To improve video understanding and support dynamic reward feedback throughout the inference process, we propose a lightweight, general, and extensible agent framework, ReAgent-V
- ReAgent-V README. md at main · aiming-lab ReAgent-V · GitHub
Specifically, ReAgent-V evaluates each trajectory across multiple axes—such as task success, temporal stability, visual grounding, and semantic precision—and performs multi-agent reflection to produce refined, high-fidelity reward scores for alignment
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