CineForge: Self-Improving Agents for Long-Horizon Video Generation

Long-horizon story-driven video generation requires coordinating narrative decomposition, state tracking, shot design, prompting, rendering, and revision across interdependent scenes. Adaptive video systems primarily refine requests or reusable skills, leaving recurring failures disconnected from persistent, stage-targeted improvements across stories. We introduce CineForge, a self-evolving video-production agent framework coupling CineForge-Produce for generation with CineForge-Evolve for cross-story policy evolution. CineForge-Produce structures stories into typed narrative, character, spatial, and cinematic states to coordinate asset and clip generation, recording canonical production trajectories. CineForge-Evolve applies Case-to-Pattern-to-Policy Evolution (CPPE) to review trajectory evidence, consolidate recurrent findings into bounded stage-local patches, and deploy validated updates through structural replay and confidence-controlled paired evaluation. To measure complete story realization, CineScope combines 100-script CineScope-Data with human-aligned, multiscale CineScope-Metric covering causal state, directorial orchestration, pacing and resource allocation, and character arc. Across CineScope-Data and two public benchmarks, the evolved policy improves CineScope-Metric from 4.02 to 4.38, outperforms three long-video baselines with consistent gains under ScriptAgent, and reduces review LLM calls by 37.0% on new stories. Results establish production trajectories as actionable experience for cumulative improvement across long-form storytelling.