Agentic video creation systems remain far from cinematic-grade quality: their underlying models struggle to plan how story, characters, visuals, and sound fit together, and high-quality creation trajectories to learn such planning abilities are scarce.
Finished human films embody this knowledge, but in a tightly coupled audiovisual form that agents cannot directly read or manipulate.
We Propose the Agentic Video Auto-Encoder (AVA-Encoder)
We propose the Agentic Video Auto-Encoder (AVA-Encoder), which works backward from finished films, turning them into agent-native representations with reconstruction itself as supervision.
It encodes a film into a Film Knowledge Graph (Film KG) of structured-text nodes, typed dependency edges, and linked multimodal assets, then regenerates the film from this graph with a fixed decoder.
Reconstruction residuals are distilled into textual gradients that drive dual-loop self-evolution: an outer loop learns a shared encoding policy across videos, and an inner loop refines each video's Film KG at test time.
AVA-Encoder Improves Reconstruction Fidelity
AVA-Encoder improves reconstruction fidelity by 20.7 percentage points over the strongest baseline;
its learned policy outperforms a finely human-tuned policy with over 70 percent fewer system-prompt tokens;
and Film KGs support coordinated editing and improve all tested downstream creation systems.
We Release the Framework
We release the framework, a reconstruction benchmark, and a Film KG dataset.