ProAR: Learning Prospective Reasoning with Autoregressive Video Models

Forum topic · 小凯 · 2026-10-06

Summary

ProAR is a new framework that transforms autoregressive (AR) video generation from short-sighted, next-chunk prediction into a goal-oriented reasoning process. AR video models excel at causal generation but struggle with reasoning-oriented generation, where reaching a target outcome through valid intermediate states matters more than local visual plausibility. ProAR introduces two components: (1) goal-frame prediction integrated into the autoregressive loop via an asymmetric attention mask, so the predicted goal frame guides intermediate generation without being disturbed by it; and (2) future-representation self-alignment, which uses teacher forcing during AR training to extract clean future representations in a single forward pass and trains a lightweight predictor to align current representations with them. These mechanisms combine sparse explicit goal supervision with dense stepwise guidance at modest computational cost. Experiments show consistent improvements across diverse visual reasoning benchmarks, and ProAR surpasses fully trained standard AR baselines using only 25% of the training steps. The paradigm also shows promise for embodied reasoning tasks.

Paper Overview

Research Area: Computer Vision (CV) Authors: Linghui Shen, Tinghui Zhu, Sheng Zhang, Muhao Chen Published: 2026-10-02 arXiv: 2610.03664

Introduction

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.

The ProAR Framework

To address this challenge, ProAR transforms autoregressive video generation into a goal-oriented reasoning process, introducing two key components:

1. Goal-frame prediction: To anchor generation to the long-range outcome, goal-frame prediction is integrated into the autoregressive loop via an asymmetric attention mask, enabling the predicted goal frame to guide the generation of intermediate states without being disturbed by them.

2. Future-representation self-alignment: To steer short-range transitions, current hidden states are encouraged to predict upcoming temporal dynamics. Leveraging teacher forcing in AR training, clean future representations are extracted in a single forward pass, and a lightweight, prediction-only predictor aligns the current representation with them.

Together, these mechanisms seamlessly combine explicit sparse goal supervision with implicit dense stepwise guidance, promoting coherent goal-directed reasoning at modest computational cost.

Results

ProAR's complementary components consistently improve performance across diverse visual reasoning benchmarks.
The framework is highly training-efficient: it surpasses fully trained standard AR baselines using only 25% of the training steps.
The paradigm also shows promising applicability to embodied reasoning tasks.