AVA-Encoder: Towards Agent-Native Video Representation Learning
Score
78提出全新的智能体原生视频表征范式，填补了电影级视频结构化表示在该领域的空白。
方法设计完整，包含理论、算法、实验与开源资源，实验数据扎实且对比充分。
大幅降低智能体视频处理对提示词的依赖，并提供可直接使用的框架与数据集。
当前生成式智能体对视频理解与操作需求迫切，论文选题紧跟前沿热点。
来自 arXiv AI/CL/LG 方向的论文预印本，技术细节详尽并附开源链接。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper introduces a novel framework at the intersection of video representation and agentic learning, with significant experimental gains and complete open-source resources.
AI commentary
「What stands out most is using knowledge graphs as an intermediate language between agents and video, bypassing the limits of end-to-end black boxes. If you are working on video agents or controllable generation, this framework and dataset are worth trying.」
Quick overview
This paper proposes AVA-Encoder, an agent-native video representation learning framework that transforms videos into knowledge graph representations and reconstructs them back, enabling hierarchical structured text storage and an editable asset layer. Its novel textual-gradient optimization mechanism allows agents to iteratively improve encoding policies via natural language instructions. Experiments show AVA-Encoder outperforms the strongest external baseline by 20.7 percentage points, and its pseudo-trained policy surpasses carefully human-tuned baselines while using 74.3% fewer system-prompt tokens. The framework also releases the first high-quality film KG representation dataset.
- AVA-Encoder converts video into a knowledge graph representation and reconstructs it back, forming a closed loop that agents can understand, query, and edit.
- A textual-gradient optimization framework expresses evaluation feedback as natural-language update directions for both outer-loop policy training and inner-loop test-time refinement.
- It outperforms the strongest external baseline by 20.7%, and the pseudo-trained policy beats human-tuned baselines with 74.3% fewer system-prompt tokens.
- The complete framework, a reliable agentic video reconstruction benchmark, and the first high-quality film KG dataset are released.
Full text
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