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From Prediction to Perfection: Introducing Refinement to Autoregressive Image Generation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Auto-Regressive Image GenerationDiscrete Diffusion
Abstract:

Autoregressive (AR) models have emerged as a powerful framework for image generation, yet they remain bound by a fundamental limitation: once a prediction is made, it cannot be revised. Each step marches forward in a strict left-to-right sequence, causing small errors to accumulate and compromise the final image. In this work, we reimagine this process with TensorAR, a decoder-only AR model that shifts from predicting discrete tokens to predicting overlapping tensor windows. This simple change transforms image synthesis into a process of next-tensor prediction, enabling the model to refine earlier outputs while preserving the causal structure that defines autoregression. To guard against information leakage during training, we introduce a discrete tensor noising mechanism inspired by discrete diffusion theory, which injects categorical noise into input tensors. TensorAR is designed to be plug-and-play: unlike masked AR methods, it requires no architectural modifications, and unlike autoregressive diffusion, it preserves the familiar AR training paradigm. We evaluate TensorAR across both class-to-image and text-to-image tasks, showing consistent gains in generation quality and instruction-following ability, while achieving a superior balance between quality and latency. In doing so, TensorAR offers a new path forward for autoregressive generation---one where predictions are not just produced, but continually refined.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

TensorAR introduces a decoder-only autoregressive framework that predicts overlapping tensor windows rather than discrete tokens, enabling refinement of earlier outputs while preserving causal structure. The paper resides in the 'Continuous and Tensor-Based Autoregressive Models' leaf, which contains only three papers total (including TensorAR itself). This leaf sits within the broader 'Autoregressive Generation Architectures and Token Prediction Strategies' branch, indicating a relatively sparse research direction compared to the more populated discrete token-based methods (three papers) and diffusion-based branches (multiple subtopics with over fifteen papers combined).

The taxonomy reveals that TensorAR's immediate neighbors include DC-AR and another continuous-token method, while sibling branches explore discrete tokenization (VQ-VAE-based approaches), hierarchical multi-stage generation, and retrieval-augmented methods. The broader field context shows substantial activity in diffusion-based iterative refinement (latent diffusion, autoregressive diffusion, timestep tokenization) and GAN-based progressive synthesis (progressive growing, conditional GANs). TensorAR's tensor-window prediction approach diverges from both the discrete token paradigm of traditional autoregressive models and the denoising schedules of diffusion hybrids, positioning it at the intersection of continuous representation learning and causal generation.

Among twenty-two candidates examined, three contributions show potential prior overlap. The core TensorAR framework (Contribution A: two candidates examined, one refutable) appears to have at least one overlapping work in tensor-based prediction. The discrete tensor noising mechanism (Contribution B: ten candidates examined, one refutable) and the plug-and-play design claim (Contribution C: ten candidates examined, one refutable) each identify one candidate providing related prior work. The limited search scope means these statistics reflect top-K semantic matches and citation expansion, not exhaustive coverage. Contributions B and C appear more novel given the nine non-refutable candidates each, while Contribution A's novelty is less clear with only one non-refutable candidate among two examined.

Based on the analysis of twenty-two semantically similar papers, TensorAR occupies a sparsely populated research direction with modest prior overlap detected. The taxonomy structure confirms that continuous and tensor-based autoregressive methods remain less explored than discrete token or diffusion-based alternatives. However, the limited search scope and the presence of at least one refutable candidate per contribution suggest that claims of fundamental novelty should be tempered, particularly for the core framework design where only two candidates were examined.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
22
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: Autoregressive image generation with iterative refinement. This field encompasses methods that produce images by sequentially predicting tokens or features and then refining outputs through multiple passes. The taxonomy reveals several major branches: autoregressive architectures that focus on token prediction strategies (including discrete, continuous, and tensor-based variants like DC-AR[1] and TensorAR[24]); diffusion-based iterative refinement and hybrid models that blend denoising processes with autoregressive steps (e.g., Progressive Conditional Diffusion[5] and Nested Diffusion[22]); GAN-based progressive and multi-stage synthesis approaches (such as Progressive GANs[2] and HR-PrGAN[40]); task-specific applications targeting domains like medical imaging or sketch-to-image translation; reasoning and planning frameworks that incorporate multi-agent or chain-of-thought mechanisms (e.g., VideoGen-of-Thought[34]); multimodal integration and self-improvement techniques; and auxiliary methods supporting iterative generation. These branches reflect a spectrum from purely sequential token prediction to hybrid pipelines that combine autoregression with diffusion or adversarial training. A particularly active line of work explores continuous and tensor-based autoregressive models, which move beyond discrete tokenization to predict richer representations directly. Prediction to Perfection[0] sits within this branch, emphasizing iterative refinement in a continuous latent space rather than discrete token sequences. This contrasts with earlier cascaded or multi-stage methods like Multi-Stage Restoration[3] and Cascaded Refinement Networks[10], which typically refine outputs through separate network stages or resolution pyramids. Nearby works such as E-CAR[6] and TensorAR[24] similarly adopt tensor-level predictions, but Prediction to Perfection[0] distinguishes itself by tightly coupling autoregressive generation with iterative correction loops. Meanwhile, diffusion-hybrid approaches like Progressive Conditional Diffusion[5] and Nested Diffusion[22] offer alternative refinement strategies through denoising schedules. The central tension across these directions involves balancing generation speed, output fidelity, and the flexibility to handle diverse conditioning signals, with Prediction to Perfection[0] contributing a perspective that leverages continuous representations for more granular iterative control.

Claimed Contributions

TensorAR framework for next-tensor prediction with refinement

The authors introduce TensorAR, a framework that transforms autoregressive image generation from next-token to next-tensor prediction. By predicting overlapping tensors of consecutive tokens, the model can iteratively refine earlier outputs while preserving causal structure, enabling a coarse-to-fine generation process similar to diffusion models.

2 retrieved papers
Can Refute
Discrete tensor noising mechanism

To prevent information leakage during training caused by overlapping tokens in consecutive tensors, the authors propose a discrete tensor noising mechanism. This approach injects categorical noise into input tensors with token-wise modulated noise levels, stimulating an internal progressive denoising process within the autoregressive model.

10 retrieved papers
Can Refute
Plug-and-play design with minimal architectural changes

TensorAR is designed as a plug-and-play extension that integrates with existing autoregressive models through lightweight input encoder and output decoder modules with residual connections. Unlike masked AR or autoregressive diffusion approaches, it requires no base architecture modifications and preserves the standard classification-based AR training paradigm.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

TensorAR framework for next-tensor prediction with refinement

The authors introduce TensorAR, a framework that transforms autoregressive image generation from next-token to next-tensor prediction. By predicting overlapping tensors of consecutive tokens, the model can iteratively refine earlier outputs while preserving causal structure, enabling a coarse-to-fine generation process similar to diffusion models.

Contribution

Discrete tensor noising mechanism

To prevent information leakage during training caused by overlapping tokens in consecutive tensors, the authors propose a discrete tensor noising mechanism. This approach injects categorical noise into input tensors with token-wise modulated noise levels, stimulating an internal progressive denoising process within the autoregressive model.

Contribution

Plug-and-play design with minimal architectural changes

TensorAR is designed as a plug-and-play extension that integrates with existing autoregressive models through lightweight input encoder and output decoder modules with residual connections. Unlike masked AR or autoregressive diffusion approaches, it requires no base architecture modifications and preserves the standard classification-based AR training paradigm.

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