{"total":83,"rows":[{"id":"2506.15564","title":"Show-o2: Improved Native Unified Multimodal Models","created":"2025-06-18","cited_by_count":250,"area":"diffusion"},{"id":"2505.02567","title":"Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities","created":"2025-05-05","cited_by_count":85,"area":null},{"id":"2412.15119","title":"Parallelized Autoregressive Visual Generation","created":"2024-12-19","cited_by_count":60,"area":null},{"id":"2503.13436","title":"Unified Autoregressive Visual Generation and Understanding with Continuous Tokens","created":"2025-03-17","cited_by_count":51,"area":null},{"id":"2509.04394","title":"Transition Models: Rethinking the Generative Learning Objective","created":"2025-09-04","cited_by_count":34,"area":"diffusion"},{"id":"2503.10696","title":"Neighboring Autoregressive Modeling for Efficient Visual Generation","created":"2025-03-12","cited_by_count":29,"area":null},{"id":"2503.16430","title":"Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation","created":"2025-03-20","cited_by_count":29,"area":null},{"id":"2503.05305","title":"Frequency Autoregressive Image Generation with Continuous Tokens","created":"2025-03-07","cited_by_count":27,"area":null},{"id":"2412.15321","title":"Next Patch Prediction for Autoregressive Visual Generation","created":"2024-12-19","cited_by_count":26,"area":null},{"id":"2502.07737","title":"Next Block Prediction: Video Generation via Semi-Autoregressive Modeling","created":"2025-02-11","cited_by_count":26,"area":null},{"id":"2512.16922","title":"Next-Embedding Prediction Makes Strong Vision Learners","created":"2025-12-18","cited_by_count":23,"area":null},{"id":"2508.15772","title":"Visual Autoregressive Modeling for Instruction-Guided Image Editing","created":"2025-08-21","cited_by_count":22,"area":"diffusion"},{"id":"2508.03334","title":"Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation","created":"2025-08-05","cited_by_count":21,"area":"diffusion"},{"id":"2503.10568","title":"Autoregressive Image Generation with Randomized Parallel Decoding","created":"2025-03-13","cited_by_count":20,"area":"inference"},{"id":"2506.05289","title":"Towards Sequence Modeling Alignment between Tokenizer and Autoregressive Model","created":"2025-06-05","cited_by_count":20,"area":null},{"id":"2507.15856","title":"Latent Denoising Makes Good Tokenizers","created":"2025-07-21","cited_by_count":20,"area":null},{"id":"2505.10518","title":"Multi-Token Prediction Needs Registers","created":"2025-05-15","cited_by_count":16,"area":null},{"id":"2506.19935","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","created":"2025-06-24","cited_by_count":16,"area":"diffusion"},{"id":"2509.24335","title":"Hyperspherical Latents Improve Continuous-Token Autoregressive Generation","created":"2025-09-29","cited_by_count":16,"area":"diffusion"},{"id":"2512.15657","title":"SoFlow: Solution Flow Models for One-Step Generative Modeling","created":"2025-12-17","cited_by_count":13,"area":"diffusion"},{"id":"2504.00999","title":"MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization","created":"2025-04-01","cited_by_count":12,"area":"inference"},{"id":"2509.12474","title":"Image Tokenizer Needs Post-Training","created":"2025-09-15","cited_by_count":12,"area":null},{"id":"2510.03929","title":"Self-Speculative Masked Diffusions","created":"2025-10-04","cited_by_count":11,"area":"diffusion"},{"id":"2503.08354","title":"Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis","created":"2025-03-11","cited_by_count":10,"area":null},{"id":"2505.04594","title":"Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detection","created":"2025-05-07","cited_by_count":10,"area":null},{"id":"2507.01957","title":"Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation","created":"2025-07-02","cited_by_count":10,"area":"inference"},{"id":"2509.10140","title":"Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization","created":"2025-09-12","cited_by_count":10,"area":null},{"id":"2505.23660","title":"D-AR: Diffusion via Autoregressive Models","created":"2025-05-29","cited_by_count":9,"area":null},{"id":"2503.16278","title":"Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling","created":"2025-03-20","cited_by_count":8,"area":null},{"id":"2506.01011","title":"Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack","created":"2025-06-01","cited_by_count":8,"area":"safety"},{"id":"2503.16194","title":"Improving Autoregressive Image Generation through Coarse-to-Fine Token Prediction","created":"2025-03-20","cited_by_count":7,"area":null},{"id":"2506.18226","title":"Make It Efficient: Dynamic Sparse Attention for Autoregressive Image Generation","created":"2025-06-23","cited_by_count":7,"area":"inference"},{"id":"2512.04084","title":"SimFlow: Simplified and End-to-End Training of Latent Normalizing Flows","created":"2025-12-03","cited_by_count":7,"area":null},{"id":"2512.19680","title":"VA-$\\pi$: Variational Policy Alignment for Pixel-Aware Autoregressive Generation","created":"2025-12-22","cited_by_count":7,"area":"alignment"},{"id":"2505.11196","title":"DiCo: Revitalizing ConvNets for Scalable and Efficient Diffusion Modeling","created":"2025-05-16","cited_by_count":6,"area":"diffusion"},{"id":"2507.01756","title":"Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis","created":"2025-07-02","cited_by_count":6,"area":"lr"},{"id":"2510.04450","title":"REAR: Rethinking Visual Autoregressive Models via Generator-Tokenizer Consistency Regularization","created":"2025-10-06","cited_by_count":6,"area":"lr"},{"id":"2512.14697","title":"Spherical Leech Quantization for Visual Tokenization and Generation","created":"2025-12-16","cited_by_count":6,"area":null},{"id":"2508.06044","title":"NEP: Autoregressive Image Editing via Next Editing Token Prediction","created":"2025-08-08","cited_by_count":5,"area":null},{"id":"2508.12811","title":"Next Visual Granularity Generation","created":"2025-08-18","cited_by_count":5,"area":null},{"id":"2602.04361","title":"SparVAR: Exploring Sparsity in Visual AutoRegressive Modeling for Training-Free Acceleration","created":"2026-02-04","cited_by_count":4,"area":null},{"id":"2602.14041","title":"BitDance: Scaling Autoregressive Generative Models with Binary Tokens","created":"2026-02-15","cited_by_count":4,"area":null},{"id":"2505.16324","title":"From Prediction to Perfection: Introducing Refinement to Autoregressive Image Generation","created":"2025-05-22","cited_by_count":3,"area":null},{"id":"2505.19656","title":"ReDDiT: Rehashing Noise for Discrete Visual Generation","created":"2025-05-26","cited_by_count":3,"area":"diffusion"},{"id":"2604.13030","title":"Generative Refinement Networks for Visual Synthesis","created":"2026-04-14","cited_by_count":3,"area":"diffusion"},{"id":"2507.06137","title":"NeoBabel: A Multilingual Open Tower for Visual Generation","created":"2025-07-08","cited_by_count":2,"area":null},{"id":"2508.10719","title":"Exploiting Discriminative Codebook Prior for Autoregressive Image Generation","created":"2025-08-14","cited_by_count":2,"area":null},{"id":"2509.16986","title":"VCE: Safe Autoregressive Image Generation via Visual Contrast Exploitation","created":"2025-09-21","cited_by_count":2,"area":null},{"id":"2510.00996","title":"SoftCFG: Uncertainty-guided Stable Guidance for Visual Autoregressive Model","created":"2025-10-01","cited_by_count":2,"area":null},{"id":"2511.23469","title":"Visual Generation Tuning","created":"2025-11-28","cited_by_count":2,"area":"mllm"},{"id":"2512.04857","title":"Autoregressive Image Generation Needs Only a Few Lines of Cached Tokens","created":"2025-12-04","cited_by_count":2,"area":"inference"},{"id":"2512.21867","title":"DPAR: Dynamic Patchification for Efficient Autoregressive Visual Generation","created":"2025-12-26","cited_by_count":2,"area":null},{"id":"2603.16373","title":"Semantic One-Dimensional Tokenizer for Image Reconstruction and Generation","created":"2026-03-17","cited_by_count":2,"area":null},{"id":"2604.07422","title":"Multimodal Large Language Models for Multi-Subject In-Context Image Generation","created":"2026-04-08","cited_by_count":2,"area":"mllm"},{"id":"2605.24292","title":"TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models","created":"2026-05-22","cited_by_count":2,"area":"diffusion"},{"id":"2504.17069","title":"Distilling Specialized Orders for Visual Generation","created":"2025-04-23","cited_by_count":1,"area":null},{"id":"2512.03796","title":"LSRS: Latent Scale Rejection Sampling for Visual Autoregressive Modeling","created":"2025-12-03","cited_by_count":1,"area":null},{"id":"2601.14246","title":"Soft Tail-dropping for Adaptive Visual Tokenization","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2605.21195","title":"RankE: End-to-End Post-Training for Discrete Text-to-Image Generation with Decoder Co-Evolution","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2608.00562","title":"Beyond Token-Level Cross-Entropy: Fr\\'echet Distributional Post-Training for Autoregressive Image Generation","created":"2026-08-01","cited_by_count":1,"area":null},{"id":"2509.23008","title":"ARSS: Taming Decoder-only Autoregressive Visual Generation for View Synthesis From Single View","created":"2025-09-27","cited_by_count":0,"area":"wm"},{"id":"2509.23919","title":"Token Painter: Training-Free Text-Guided Image Inpainting via Mask Autoregressive Models","created":"2025-09-28","cited_by_count":0,"area":null},{"id":"2510.04220","title":"MASC: Boosting Autoregressive Image Generation with a Manifold-Aligned Semantic Clustering","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2511.17089","title":"Spanning Tree Autoregressive Visual Generation","created":"2025-11-21","cited_by_count":0,"area":null},{"id":"2511.21415","title":"DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models","created":"2025-11-26","cited_by_count":0,"area":"diffusion"},{"id":"2511.22991","title":"Guiding Visual Autoregressive Models through Spectrum Weakening","created":"2025-11-28","cited_by_count":0,"area":"diffusion"},{"id":"2512.06421","title":"Rethinking Training Dynamics in Scale-wise Autoregressive Generation","created":"2025-12-06","cited_by_count":0,"area":null},{"id":"2602.03811","title":"Progressive Checkerboards for Autoregressive Multiscale Image Generation","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.14178","title":"UniWeTok: An Unified Binary Tokenizer with Codebook Size $\\mathit{2^{128}}$ for Unified Multimodal Large Language Model","created":"2026-02-15","cited_by_count":0,"area":"mllm"},{"id":"2602.21944","title":"Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2603.19122","title":"Revisiting Autoregressive Models for Generative Image Classification","created":"2026-03-19","cited_by_count":0,"area":"diffusion"},{"id":"2605.06137","title":"Autoregressive Visual Generation Needs a Prologue","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.06207","title":"Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.26089","title":"Channel-wise Vector Quantization","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2607.00371","title":"MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts","created":"2026-07-01","cited_by_count":0,"area":"moe"},{"id":"2607.02963","title":"Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning","created":"2026-07-03","cited_by_count":0,"area":"inference"},{"id":"2608.25386","title":"Efficient Training with Foresight: Multi-Token Auxiliary Supervision for Autoregressive Image Generation","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.13245","title":"SJD-SV: Speculative Jacobi Decoding with Semantics Verification for Autoregressive Image Generation","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2609.26774","title":"StableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training","created":"2026-09-22","cited_by_count":0,"area":null},{"id":"2609.30647","title":"Conditional Predictive Sufficient Statistics for Visual Representation Learning","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.34824","title":"Generative Residual Factorization","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36227","title":"One-Step Next-Latent Prediction Is Not a World Model","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36756","title":"NesTok: Nested Self-Aligned 1D Tokenizer for Autoregressive Image Generation","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}