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ProAR: Learning Prospective Reasoning with Autoregressive Video Models

Linghui Shen, Tinghui Zhu, Sheng Zhang, Muhao Chen

arXiv:2610.03664v1cs.CV

TL;DR

Autoregressive video reasoning needs trajectories that reach a target through valid intermediate states, while next-chunk objectives focus on local continuations. ProAR adds goal-frame outcome guidance and future-representation transition guidance, improving visual-reasoning results and showing promising embodied-reasoning applicability.

  • Problem

    Next-chunk prediction optimizes locally plausible continuations without accounting for task completion, a mismatch for reasoning that must reach a target through valid intermediate states.

    Source: §1 · pp. 1–2

  • Method

    ProAR combines goal-frame prediction with asymmetric attention for long-range outcome guidance and future-representation self-alignment for short-range transition guidance.

    Source: §1 · p. 2

  • Results

    ProAR consistently improves performance across visual-reasoning benchmarks and surpasses the fully trained AR baseline on VBVR using only 25% of training steps.

    Source: §1 · p. 2, §5 · p. 9

  • Takeaways & Limitations

    Prospective modeling is a promising direction for advancing video-native reasoning, with potential relevance to physical and embodied intelligence.

    Source: §5 · p. 9

    When final frames are semantically uninformative, such as in post-completion idle states or process-driven dynamics, goal-frame prediction may provide weak supervision and limit meaningful long-range guidance.

    Source: §A.6 · p. 20

Abstract

from arXiv · show

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 Prospective Reasoning with Autoregressive Video Models (ProAR), a novel framework that transforms autoregressive video generation into a goal-oriented reasoning process. ProAR introduces two key components: (1) To anchor generation to the long-range outcome, we integrate goal-frame prediction into the autoregressive loop via an asymmetric attention mask, enabling the predicted goal frame to guide the generation of intermediate states without being disrupted by them. (2) To guide short-range transitions, we introduce future representation self-alignment to encourage current hidden states to anticipate upcoming temporal dynamics. By leveraging teacher-forcing in AR training, we extract clean future representations in a single forward pass and align current representations with them using a lightweight, training-only predictor. Together, these two mechanisms seamlessly combine explicit, sparse target supervision with implicit, dense step-wise guidance, promoting coherent, goal-directed reasoning progress with modest computational cost. Experiments show that ProAR's complementary components consistently improve performance across diverse visual reasoning benchmarks. The framework proves highly training-efficient, surpassing fully trained standard AR baselines using only 25% of the training steps. This paradigm also demonstrates promising applicability to embodied reasoning tasks.

1 INTRODUCTION

Autoregressive video reasoning must reach target outcomes through valid visual states, but next-chunk training focuses on locally plausible continuations. ProAR adds outcome and transition guidance, improving results across visual reasoning benchmarks and showing promising embodied-reasoning gains.

1 INTRODUCTION

  • In video-native reasoning, generating valid visual-state sequences is itself the reasoning process, extending video generation beyond visual synthesis.Autoregressive models generate sequentially, supporting long-horizon generation and low-latency streaming.Source: §1 · p. 1
  • Standard next-chunk training optimizes locally plausible continuations without accounting for eventual task completion, allowing short-sighted choices to compound and steer trajectories away from the goal.Figure 1 contrasts this local prediction with ProAR’s anticipation of immediate transitions and final outcomes.Source: §1 · pp. 1–2, Figure 1 · p. 2
  • ProAR frames prospective reasoning as anticipating future outcomes and state transitions from observed history, then using that foresight to guide ongoing generation.Outcome awareness supplies long-range direction, while transition awareness supports progress toward the goal.Source: §1 · p. 2
  • ProAR jointly predicts a goal frame to guide current generation and aligns current representations with clean future representations to anticipate upcoming dynamics.The asymmetric attention mask shields goal tokens from the current chunk; the training-only predictor needs no external encoder or extra backbone pass.Source: §1 · p. 2
  • On the 10-task VBVR subset, ProAR raises mean score from 0.663 to 0.801; on VideoRLVR, average success rate rises from 50.97 to 52.97.It also surpasses the fully trained AR baseline on VBVR using 25% of the training steps and improves on WorldArena.Source: §1 · p. 2

2 RELATED WORK

Related work covers advances in autoregressive generation, video-native visual reasoning, and representation learning. ProAR is positioned as lightweight prospective guidance for causal generation.

Autoregressive Video Generation

  • Autoregressive video work improves scalability (Teng et al., 2025), efficiency (Yang et al., 2025a; Chen et al., 2026c), and stability, while horizon extension alone misses target and transition guidance.Prior approaches also explore foresight alignment (Yu et al., 2026), hierarchical denoising (Qian et al., 2026), and future-aware training (Xu et al., 2026a).Source: §2 · p. 3

Native Reasoning with Video Generation Models

Representation Learning in Diffusion Models

  • Representation-learning approaches align generative features with pretrained spaces (Oquab et al., 2023) or use stronger internal representations to guide weaker ones (Jiang et al., 2025; Chefer et al., 2026).Video-Mirai (Yu et al., 2026) distills future bidirectional representations into causal features using an additional forward pass; ProAR obtains future representations within the model.Source: §2 · p. 3

3 PROAR

ProAR augments autoregressive video generation with long-range outcome guidance and short-range transition guidance. Together, these mechanisms address goal completion and valid progression beyond local next-chunk prediction.

3 PROAR

  • ProAR combines an evolving goal belief with future representation self-alignment to provide sparse outcome guidance and dense transition guidance.The two mechanisms are designed to guide long-range outcomes and short-range progression, respectively.Source: §3 · p. 3

3.1 PRELIMINARIES AND MOTIVATION

  • Next-chunk supervision does not directly ensure goal completion or valid subsequent transitions, and irreversible rollout allows early errors to compound.Prospective reasoning therefore needs both long-range outcome expectations and short-range anticipation of the next stage.Source: §3.1 · p. 4

3.2 OUTCOME: GOAL BELIEF FOR LONG-HORIZON PLANNING

  • Outcome guidance jointly predicts the current chunk and an evolving goal belief, with an asymmetric mask allowing the goal to guide current generation without being influenced by it.At inference, the goal belief is re-estimated from the expanded history at each step.Source: §3.2 · pp. 4–5

3.3 TRANSITION: REPRESENTATION ALIGNMENT FOR PROGRESSIVE REASONING

  • Transition guidance aligns noisy current-chunk representations with clean next-chunk representations, using the latter as a prospective teacher for upcoming dynamics.The predictor maps student features toward the fixed teacher while preserving the backbone token grid and dimension.Source: §3.3 · p. 5
  • Teacher-forcing training obtains the student and teacher states in one backbone pass, and removes the alignment predictor at inference.Outcome guidance adds joint denoising of one auxiliary goal frame with each current chunk in the same forward pass.Source: §3.3 · pp. 5–6

4 EXPERIMENTS

ProAR is evaluated on perceptual, spatial, abstract, and embodied reasoning tasks, with comparisons to autoregressive and bidirectional baselines. Results cover benchmark performance, training efficiency, component ablations, and WorldArena manipulation.

4.1 EXPERIMENTAL SETUP

  • The evaluation spans 10 perceptual and spatial VBVR tasks, three abstract VideoRLVR games, and 50 WorldArena manipulation tasks, including a Standard AR baseline trained under matched settings.VBVR test data were expanded to 50 examples per task; WorldArena follows its official protocol.Source: §4.1 · pp. 6–7

4.2 MAIN RESULTS

  • VBVR mean score rises from 0.663 to 0.801, while VideoRLVR average success rate rises from 50.97 to 52.97 and average precision from 61.56 to 67.17.These results compare ProAR with Standard AR.Source: Table 1 · p. 7, §4.2 · pp. 7–8, Table 3 · p. 8

4.3 TRAINING EFFICIENCY

  • At 2,500 steps, ProAR reaches mean score 0.708, exceeding Standard AR's 0.663 after 10,000 steps; ProAR reaches 0.801 by step 10,000.Figure 4 compares performance at matched training checkpoints on the VBVR 10-task subset.Source: Figure 4 · p. 8, §4.3 · p. 8

4.5 EXTENSION ON EMBODIED REASONING

  • On WorldArena, ProAR reaches EWMScore 60.95, the second-highest result among methods shown, and improves 8 of 9 component metrics over Standard AR.In the controlled comparison, EWMScore rises from 60.43 to 60.95.Source: Table 4 · p. 9, §4.5 · p. 9

4.4 ABLATIONS AND ANALYSIS

  • VBVR mean scores are 0.786 with Outcome, 0.683 with Transition, and 0.801 combined; activating Transition at Step 7500 is comparable or slightly better than Step 0.The late-start strategy is the default configuration.Source: Table 2 · p. 7, §4.4 · pp. 8–9

5 CONCLUSION

The conclusion presents ProAR as a framework for adding prospective reasoning to autoregressive video generation and reports gains across reasoning benchmarks with modest overhead. The accompanying statements describe research-tool use, ethical concerns, and reproducibility materials.

5 CONCLUSION

  • ProAR combines outcome guidance for goal completion with transition guidance for valid intermediate progression, addressing next-chunk prediction's short-sightedness.The paper reports consistent improvements across perceptual, spatial, abstract, and embodied reasoning benchmarks with modest computational overhead.Source: §5 · p. 9
  • The authors report no specific ethical concerns arising from the methodology or reported experimental setting.Generative AI tools supported manuscript review, experimental planning, code development, and language editing; the authors say they verified the work and take responsibility for the manuscript.Source: ETHICS STATEMENT · p. 10, AI USE STATEMENT · p. 10
  • Reproducibility materials identify training and inference settings, computational costs, ablations, evaluation protocols, and WorldArena metrics across the main paper and appendices.The statement points readers to Section 3, Section 4.1, Section 4.4, and Appendices A.1, A.2, A.4, and A.5.Source: REPRODUCIBILITY STATEMENT · p. 10

A APPENDIX

The supplement collects implementation details, additional analyses, task protocols, WorldArena results, limitations, and qualitative examples.

A APPENDIX

  • The supplement covers implementation and computational details, additional Outcome and Transition ablations, and visual analyses of AR failure modes and future representation alignment.It also includes task specifications, evaluation protocols, complete WorldArena results, a limitations discussion, and further qualitative examples.Source: A APPENDIX · p. 15
  • Appendix A.4 specifies reasoning-task evaluation protocols, while Appendix A.5 provides complete WorldArena results.These supplement the main paper's task and embodied evaluations.Source: A APPENDIX · p. 15
  • Appendix A.6 contains a discussion of limitations, and Appendix A.7 provides further qualitative examples.Source: A APPENDIX · p. 15

A.1 IMPLEMENTATION DETAILS

Implementation details specify video configurations, WorldArena settings, and the added training and inference costs of ProAR.

A.1 IMPLEMENTATION DETAILS

  • VBVR uses 512×512 video with task-specific lengths, VideoRLVR uses 81 frames at 832 × 480, and inference uses 20 denoising steps per chunk.WorldArena videos are sampled to 121 frames at 640 × 480, with chunk size 8 and transition alignment starting at step 7000.Source: §A.1 · p. 15
  • ProAR adds 12%–14% total training cost; Outcome adds tokens and Transition uses same-pass representations with a 3-block predictor.The predictor has 236M parameters, 4.7% of the 5B backbone, and adds 2%–3% to training computation.Source: §A.1 · p. 15
  • Inference time increases approximately 12% for chunk size 4 and about 8% for 8-frame WorldArena chunks; the Transition predictor is removed at inference.Outcome jointly denoises the goal frame and current chunk without additional forward passes or sampling steps.Source: §A.1 · p. 15

A.2 ADDITIONAL ABLATION STUDIES

Additional ablations support the asymmetric attention mask, Layer 15 alignment, and a three-block predictor as ProAR defaults. The reported comparisons show gains over alternatives while retaining training-only predictor use.

Outcome: Asymmetric Attention Mask

  • 0.801 mean score with the asymmetric mask exceeds 0.799 without it, and the masked design performs better on 6 of 10 tasks.The asymmetric pattern is sparser and more computationally efficient; Table 5 reports the comparison.Source: §A.2 · p. 15, Table 5 · p. 16

Transition: Alignment Layer

  • Layer 15 achieves the highest mean score, 0.801, versus 0.788 at Layer 10 and 0.791 at Layer 20.All three tested layers outperform Standard AR (0.663) and Outcome guidance alone (0.786); Table 6 compares the layers.Source: §A.2 · p. 16, Table 6 · p. 16

Transition: Predictor Depth

  • Increasing predictor depth from 2 to 3 blocks raises mean score from 0.792 to 0.801.The predictor is training-only, so the three-block default does not increase inference cost; Table 7 reports the ablation.Source: §A.2 · p. 16, Table 7 · p. 16

A.3 ANALYSIS

The analysis illustrates two Standard AR failure modes and visualizes how future-representation alignment shifts predicted current representations toward upcoming video states.

Failure Modes of Standard AR

  • Standard AR can produce locally plausible rollouts that fail to reach the goal or violate task rules; successful trajectories preserve valid transitions and reach the goal.Figure 5 marks erroneous states in red and the successful path with green arrows.Source: §A.3 · p. 16, Figure 5 · p. 17

Visualization of Future Representation Alignment

  • The visualization compares noisy current representations, predictor outputs, and clean next-chunk teacher targets projected with a shared PCA basis.Representations come from Layer 15 at σ = 0.5; teacher-forcing processes chunks in parallel, and the clean target is stop-gradient.Source: §A.3 · pp. 16–17, Figure 6 · p. 17
  • In both examined transitions, predicted Student representations shift toward the clean Teacher representations, recovering the next chunk’s spatial layout and object positions.Figure 6 shows this for i to i + 1 and i + 1 to i + 2; the visualization suggests prediction maps current hidden states toward an upcoming state rather than merely reconstructing the current one.Source: §A.3 · p. 17, Figure 6 · p. 17

A.4 REASONING TASKS AND EVALUATION PROTOCOLS

The section describes the VBVR and VideoRLVR task sets and their official evaluation protocols. The metrics distinguish continuous task-completion scores from stricter success criteria.

Task Specifications

Evaluation Metrics

  • VBVR evaluators assign each video a continuous score from 0 to 1 based on task-specific measures such as final-state correctness, transition validity, and rule compliance.A higher score indicates greater task completion, but the score is not a binary success rate.Source: §A.4 · p. 18
  • VideoRLVR reports macroaveraged Precision, Recall, F1, and Success Rate (SR), with SR requiring strict task completion beyond local agreement with a reference.Maze, FlowFree, and Sokoban each impose task-specific validity and completion requirements; high F1 does not necessarily imply high SR.Source: §A.4 · p. 18

A.5 ADDITIONAL WORLDARENA EVALUATION

In the WorldArena evaluation, ProAR improves on the controlled Standard AR baseline across most base metrics and ranks highly overall. The section describes the evaluation dimensions and compared systems.

Metrics

  • WorldArena uses 16 higher-is-better metrics across 6 dimensions, with EWMScore averaging the normalized metrics into a unified measure.The measure covers visual fidelity, motion, content consistency, physical plausibility, 3D structure, and controllability.Source: §A.5 · pp. 18–19

Baselines

  • Compared systems include general video generators CogVideoX and Cosmos-Predict 2.5, plus the action-conditioned latent diffusion model IRASim.The section also includes text- and action-conditioned Cosmos-Predict 2.5 variants.Source: §A.5 · pp. 19–20
  • Other comparisons include embodied world models Vidar, Genie Envisioner, Ctrl-World, and FlowWAM, alongside a controlled Standard AR baseline.Standard AR adapts the Wan2.2-TI2V-5B backbone with causal attention and teacher-forcing under the same training and evaluation protocol as ProAR.Source: §A.5 · pp. 19–20

Results

  • ProAR scores 60.95 EWMScore, ranking first among autoregressive models and second overall; against Standard AR, it improves 13 of 16 base metrics and raises EWMScore from 60.43 to 60.95.Reported metric gains include Depth Accuracy 88.42 to 91.11, Instruction Following 80.88 to 81.96, Background Consistency 87.10 to 88.11, Interaction Quality 73.12 to 73.86, and Trajectory Accuracy 41.74 to 42.34.Source: Table 10 · p. 19, §A.5 · p. 20

A.6 LIMITATIONS AND DISCUSSIONS

The discussion notes that fixed final-frame targets can provide weak guidance when the ending is semantically uninformative, and suggests adaptive milestones as a future direction. The appendix also presents qualitative results across VBVR, VideoRLVR, and WorldArena.

A.6 LIMITATIONS AND DISCUSSIONS

  • When final frames are semantically uninformative, such as after-completion idle states or process-driven dynamics, predicting them may provide weak supervision and weaken long-range guidance.The final-frame heuristic is described as effective for most goal-oriented tasks where the last frame captures completion.Source: §A.6 · p. 20
  • Future work could replace fixed final-frame targets with adaptive salient keyframes or bottleneck states as flexible goals or intermediate subgoals.The proposed direction aims to extend prospective reasoning to more open-ended and complex physical environments.Source: §A.6 · p. 20

A.7 QUALITATIVE RESULTS

  • Additional qualitative results are presented for VBVR, VideoRLVR, and WorldArena in Figures 7–10.Figures 7 and 8 cover VBVR, Figure 9 covers VideoRLVR, and Figure 10 covers WorldArena.Source: §A.7 · p. 20, Figure 7 · p. 21, Figure 8 · p. 22
  • The supplementary qualitative material names tasks including Concentric Ring, Outline Match, Stable Sort, Star Match, Domino Chain, Ball Eater, Grid Shift, Pipe Puzzle, Slide Puzzle, and Balancing Vessels.Source: §A.7 · pp. 21–22

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