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LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation

Paper recorded by Signals 4 on 2026-10-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.AI · 人工智能 · first seen 2026-10-05

Abstract

Camera-controlled video models are rapidly advancing toward long generation horizons and complex camera control. A key failure mode is 3D inconsistency: as the camera moves, objects lose permanence and scene structures shift. Existing post-training techniques, which assign a single scalar reward to the entire generation, are poorly suited to correcting these inconsistencies over long horizons. We

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#511 most recent of 1020 cs.AI papers we have recorded · ↑ newer: Do Large Language Models Know Colombian Law? A Reliability Benchmark f · ↓ older: Credit Where It Matters: Dependency-Aware Policy Optimization for Term
Cite this page: LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation: the #511 most recent of 1020 cs.AI papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/logo-local-global-rewards-for-consistent-long-horizon-video-generation.html
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