Good Papers

Showing Autoregressive generation Show all papers

78%Highly rated
?Highly ratedVote to see the score
arXivAutoregressive generation

ProAR: Learning Prospective Reasoning with Autoregressive Video Models

ProAR introduces goal-frame prediction and future self-alignment to enable goal-directed reasoning in autoregressive video models, surpassing baselines with 25% training steps.

Linghui Shen, Tinghui Zhu, Sheng Zhang, Muhao Chen

Published Oct 2, 2026 · 0 citations · ▲ 26 on Hugging Face · Code ★ 2

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Du Xiaoman Technology (Beijing) Tomorrow Advancing LifeCanvaRenmin University of ChinaSangfor Technologies Inc.Autoregressive generation

Pixel-space Autoregressive Image Synthesis via Spectrum Serialization and Flow-based Refinement

Guiwei Zhang, Tianyu Zhang, Yalong Bai, Ying Ba and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026KyotoAutoregressive generation

BAL: Bidirectional Autoregression in Latent Space for Learning Human Movement Representations

Genki Kinoshita, Ko Nishino

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026The Hong Kong University of ScieTencentTsinghuaTencent AMSHong Kong University of Science Autoregressive generation

Collapse Hunter: Tackling the Dimensional Degeneration in Generative Ranking

Haoran Xin, Junwei Pan, Yongqi Zhou, Tianqu Zhuang and 7 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026YaleNational University of SingaporeYale University & Cisco FounAutoregressive generation

Demystifying Classifier-Free Guidance for Auto-Regressive Image Generation

Zhiling Zhou, Jiachun Pan, Fengzhuo Zhang, Dirk Bergemann and 1 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U California, BerkeleyU Southern CaliforniaOffice of Naval ResearchJohns HopkinsU California, Santa CruzAutoregressive generation

Context-Aware Autoregressive Image Generation for Emerging Reasoning Properties

Jixuan Ying, Haoyu Liu, Timing Yang, Tingyu Zhu and 6 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Harbin Institute of TechnologyU YorkCity University of Hong KongHarbin Institute of TechnologyAutoregressive generation

XTraj: A Coarse-to-Fine Autoregressive Framework for Transferable Trajectory Generation

wang chao, ZHIQUAN LAI, Xinwei Fang, Yuanshao Zhu and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Beijing University of AeronauticCarnegie MellonAutoregressive generation

Localizing Concepts in Visual Autoregressive Models

Nanxiang Jiang, Yang Liu, Yuanhao Wang, Min Xu

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026KAISTAutoregressive generation

Rethinking Memorization–Generalization Trade-Off in Generative Models

Jiseok Chae, Kyuwon Kim, Donghwan Kim

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026NortheasternAdobe ResearchAdobe SystemsAutoregressive generation

RefineTok: Scale-Wise Tokenization for Progressive Visual Refinement

Yitian Zhang, Long Mai, Yizhou Wang, Yun Fu

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Stony BrookETS MontrealState University of New York, StAutoregressive generation

Unlocking Any-Order Generation in Pretrained Autoregressive Image Models

Rishav Pramanik, Marco Pedersoli, Zhaozheng Yin

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
86%Must read
?Must readVote to see the score
NeurIPS 2026The Chinese University of Hong KShanghai Jiaotongnational university of singaore,Advanced Machine IntelligenceByteDance Inc.Autoregressive generation

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

BitDance is an autoregressive image generator that predicts binary visual tokens via a diffusion head and next-patch decoding, achieving state-of-the-art FID with far fewer parameters and much faster inference.

Yuang Ai, Jiaming Han, Shaobin Zhuang, Weijia Mao and 7 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 46 on Hugging Face · Code ★ 485

– ReadersNo votes yet
14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 2/5
74%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026Beijing Institute of TechnologyXidianNortheast University at QinhuangTsinghuaAutoregressive generation

ExtraVAR: Stage-Aware RoPE Remapping for Resolution Extrapolation in Visual Autoregressive Models

ExtraVAR proposes stage-aware RoPE remapping and entropy-driven attention calibration to eliminate repetition and detail degradation when extrapolating VAR models to higher resolutions without retraining.

Feihong Yan, Shaoyu Liu, Haixuan Wang, Shuai Lu and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 3/5
medium 6/10
strict 0/5
71%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026PekingERNIE Team, BaiduAutoregressive generation

Guiding Visual Autoregressive Models through Spectrum Weakening

Spectrum-weakening guidance constructs a weak visual autoregressive model via spectral selection to improve unconditional quality and conditional alignment without retraining or architectural changes.

Chaoyang Wang, Tianmeng Yang, Yunhai Tong

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 0/5
80%Must read
?Must readVote to see the score
NeurIPS 2026FudanTsinghuaU MarylandU Maryland, College ParkAutoregressive generation

IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder

IDEAL aligns discrete visual tokens with shallow and deep vision features to preserve both semantics and fine details, achieving 0.61 rFID and 1.89 gFID.

Yitong Chen, Zijie Diao, Junke Wang, Lingyu Kong and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 3 on Hugging Face · Code ★ 6

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5
71%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026SUN YAT-SEN UNIVERSITYAutoregressive generation

DrawingsDreamer: A Unified Multi-View Engineering Drawings Generation Model

DrawingsDreamer is a unified LLM-driven sequence model that generates multi-view engineering SVG drawings with high geometric fidelity and cross-view alignment via hierarchical tokenization and progressive training.

Shurui Liu, Weide Chen, Changwang Yi, Ancong Wu

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
6/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 0/5
74%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026ZhejiangFudanUCAS/EPFLJD Explore AcademyAlibaba GroupAutoregressive generation

Channel-wise Vector Quantization

Channel-wise Vector Quantization replaces patch tokens with channel tokens to achieve full codebook usage and improves reconstruction and text-to-image generation via sequential channel prediction.

Wei Song, Tianhang Wang, Yitong Chen, Zuxuan Wu and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 14 on Hugging Face · Code ★ 11

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 1/5
83%Must read
?Must readVote to see the score
NeurIPS 2026Huazhong University of Science aSouth China NormalMeituanAlibaba groupAutoregressive generation

StableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training

StableVQ decouples encoder-decoder and codebook training via Dynamic STE, Region VQ Loss, and independent schedules to stabilize VQ tokenizers and boost utilization and reconstruction.

Bao Tang, Jiahao Guo, Haoxiang Cao, Wenyu Liu and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 0/5
71%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026Hong Kong PolytechnicThe Hong Kong Polytechnic UnivAutoregressive generation

NSARM: Next-Scale Autoregressive Modeling for Robust Real-World Image Super-Resolution

NSARM uses two-stage next-scale autoregressive modeling for real-world super-resolution, achieving robust, fast, high-quality results across varying degradations.

Xiangtao Kong, Rongyuan Wu, Shuaizheng Liu, Lingchen Sun and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 0/5
78%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026The Hong Kong University of ScieDepartment of Computer Science aAnt GroupHong Kong University of Science Ant ResearchAutoregressive generation

CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives

CausalCine enables real-time interactive multi-shot video generation via causal modeling, content-aware memory routing, and few-step distillation, surpassing autoregressive baselines with streaming interactivity.

Yihao Meng, Zichen Liu, Hao Ouyang, Qiuyu Wang and 7 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 30 on Hugging Face

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
Show 20 more papers