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ProAR: Learning Prospective Reasoning with Autoregressive Video Models

Paper recorded by Signals 4 on 2026-10-02 in cs.CV. 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.CV · 计算机视觉 · first seen 2026-10-05

Abstract

Autoregressive (AR) video models excel at causal generation, but their reliance on next-chunk prediction confines them to a short-sighted, reactive paradigm. This limitation is particularly consequential for reasoning-oriented generation, where achieving a target outcome through valid intermediate states matters more than local visual plausibility. To address this challenge, we propose Learning Pr

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#369 most recent of 756 cs.CV papers we have recorded · ↑ newer: SigLIP2 for aerial fire risk classification · ↓ older: DEPICT: Scoring Text-to-Image Alignment by Answer Agreement
Cite this page: ProAR: Learning Prospective Reasoning with Autoregressive Video Models: the #369 most recent of 756 cs.CV papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/proar-learning-prospective-reasoning-with-autoregressive-video-models.html
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