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

Paper recorded by Signals 4 on 2026-08-30 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

Abstract

Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt construction, rendering, and revision across interdependent scenes. Existing adaptive video systems primarily refine requests or reusable skills, leaving recurring production failures disconnected from persistent, stage-targeted improvements across stori

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#266 most recent of 300 cs.AI papers we have recorded · ↑ newer: AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Mu · ↓ older: Memory-First Fact-Checking: A Knowledge-Graph-Grounded Multi-Agent Sys
Cite this page: CineForge: Self-Improving Agents for Long-Horizon Video Generation: the #266 most recent of 300 cs.AI papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/cineforge-self-improving-agents-for-long-horizon-video-generation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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