{"total":94,"rows":[{"id":"2412.04431","title":"Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis","created":"2024-12-05","cited_by_count":304,"area":null},{"id":"2412.03069","title":"TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation","created":"2024-12-04","cited_by_count":202,"area":null},{"id":"2410.10812","title":"HART: Efficient Visual Generation with Hybrid Autoregressive Transformer","created":"2024-10-14","cited_by_count":132,"area":"diffusion"},{"id":"2502.03444","title":"Masked Autoencoders Are Effective Tokenizers for Diffusion Models","created":"2025-02-05","cited_by_count":96,"area":"diffusion"},{"id":"2502.03930","title":"DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation","created":"2025-02-06","cited_by_count":57,"area":"diffusion"},{"id":"2504.08736","title":"GigaTok: Scaling Visual Tokenizers to 3 Billion Parameters for Autoregressive Image Generation","created":"2025-04-11","cited_by_count":57,"area":null},{"id":"2412.10958","title":"SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer","created":"2024-12-14","cited_by_count":56,"area":null},{"id":"2412.07720","title":"ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer","created":"2024-12-10","cited_by_count":45,"area":"diffusion"},{"id":"2502.06788","title":"EVEv2: Improved Baselines for Encoder-Free Vision-Language Models","created":"2025-02-10","cited_by_count":41,"area":"mllm"},{"id":"2411.17787","title":"Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient","created":"2024-11-26","cited_by_count":33,"area":null},{"id":"2505.07538","title":"Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning","created":"2025-05-12","cited_by_count":33,"area":"lr"},{"id":"2509.14476","title":"AToken: A Unified Tokenizer for Vision","created":"2025-09-17","cited_by_count":30,"area":null},{"id":"2412.06782","title":"CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction","created":"2024-12-09","cited_by_count":27,"area":null},{"id":"2503.08685","title":"\"Principal Components\" Enable A New Language of Images","created":"2025-03-11","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":"2502.20313","title":"FlexVAR: Flexible Visual Autoregressive Modeling without Residual Prediction","created":"2025-02-27","cited_by_count":23,"area":null},{"id":"2510.10575","title":"UniFlow: A Unified Pixel Flow Tokenizer for Visual Understanding and Generation","created":"2025-10-12","cited_by_count":23,"area":null},{"id":"2507.08441","title":"Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation","created":"2025-07-11","cited_by_count":21,"area":null},{"id":"2506.04421","title":"HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation","created":"2025-06-04","cited_by_count":20,"area":"diffusion"},{"id":"2507.15856","title":"Latent Denoising Makes Good Tokenizers","created":"2025-07-21","cited_by_count":20,"area":null},{"id":"2505.21473","title":"DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via Next-Detail Prediction","created":"2025-05-27","cited_by_count":18,"area":null},{"id":"2507.19946","title":"SCALAR: Scale-wise Controllable Visual Autoregressive Learning","created":"2025-07-26","cited_by_count":18,"area":null},{"id":"2507.13162","title":"Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models","created":"2025-07-17","cited_by_count":17,"area":"wm"},{"id":"2510.18457","title":"VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models","created":"2025-10-21","cited_by_count":17,"area":"diffusion"},{"id":"2509.24335","title":"Hyperspherical Latents Improve Continuous-Token Autoregressive Generation","created":"2025-09-29","cited_by_count":16,"area":"diffusion"},{"id":"2412.01824","title":"X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models","created":"2024-12-02","cited_by_count":15,"area":"mllm"},{"id":"2507.20454","title":"Frequency-Aware Autoregressive Modeling for Efficient High-Resolution Image Synthesis","created":"2025-07-28","cited_by_count":15,"area":"diffusion"},{"id":"2503.07076","title":"NFIG: Multi-Scale Autoregressive Image Generation via Frequency Ordering","created":"2025-03-10","cited_by_count":14,"area":null},{"id":"2412.01762","title":"XQ-GAN: An Open-source Image Tokenization Framework for Autoregressive Generation","created":"2024-12-02","cited_by_count":12,"area":null},{"id":"2412.06774","title":"Visual Lexicon: Rich Image Features in Language Space","created":"2024-12-09","cited_by_count":12,"area":"mllm"},{"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":"2503.08354","title":"Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis","created":"2025-03-11","cited_by_count":10,"area":null},{"id":"2505.23660","title":"D-AR: Diffusion via Autoregressive Models","created":"2025-05-29","cited_by_count":9,"area":null},{"id":"2508.05599","title":"WeTok: Powerful Discrete Tokenization for High-Fidelity Visual Reconstruction","created":"2025-08-07","cited_by_count":9,"area":null},{"id":"2602.07022","title":"Condition Errors Refinement in Autoregressive Image Generation with Diffusion Loss","created":"2026-02-02","cited_by_count":9,"area":"diffusion"},{"id":"2605.07915","title":"What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion","created":"2026-05-08","cited_by_count":9,"area":"diffusion"},{"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":"2507.10547","title":"Quantize-then-Rectify: Efficient VQ-VAE Training","created":"2025-07-14","cited_by_count":7,"area":null},{"id":"2509.15185","title":"Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation","created":"2025-09-18","cited_by_count":7,"area":null},{"id":"2511.20565","title":"DINO-Tok: Adapting DINO for Visual Tokenizers","created":"2025-11-25","cited_by_count":6,"area":null},{"id":"2601.01535","title":"Improving Flexible Image Tokenizers for Autoregressive Image Generation","created":"2026-01-04","cited_by_count":6,"area":null},{"id":"2505.21724","title":"OmniResponse: Online Multimodal Conversational Response Generation in Dyadic Interactions","created":"2025-05-27","cited_by_count":5,"area":null},{"id":"2506.21022","title":"Instella-T2I: Pushing the Limits of 1D Discrete Latent Space Image Generation","created":"2025-06-26","cited_by_count":5,"area":null},{"id":"2601.03955","title":"ResTok: Learning Hierarchical Residuals in 1D Visual Tokenizers for Autoregressive Image Generation","created":"2026-01-07","cited_by_count":5,"area":null},{"id":"2605.31215","title":"Fixed-Point Masked Generative Modeling","created":"2026-05-29","cited_by_count":5,"area":"inference"},{"id":"2412.02632","title":"Scaling Image Tokenizers with Grouped Spherical Quantization","created":"2024-12-03","cited_by_count":4,"area":null},{"id":"2507.01652","title":"Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective","created":"2025-07-02","cited_by_count":4,"area":null},{"id":"2601.05823","title":"Boosting Latent Diffusion Models via Disentangled Representation Alignment","created":"2026-01-09","cited_by_count":4,"area":"diffusion"},{"id":"2505.21530","title":"High-Fidelity Functional Ultrasound Reconstruction via A Visual Auto-Regressive Framework","created":"2025-05-23","cited_by_count":3,"area":null},{"id":"2601.05149","title":"Multi-Scale Local Speculative Decoding for Image Generation","created":"2026-01-08","cited_by_count":3,"area":"inference"},{"id":"2506.05240","title":"Aligning Latent Spaces with Flow Priors","created":"2025-06-05","cited_by_count":2,"area":null},{"id":"2506.09644","title":"DGAE: Diffusion-Guided Autoencoder for Efficient Latent Representation Learning","created":"2025-06-11","cited_by_count":2,"area":"diffusion"},{"id":"2510.00996","title":"SoftCFG: Uncertainty-guided Stable Guidance for Visual Autoregressive Model","created":"2025-10-01","cited_by_count":2,"area":null},{"id":"2511.14184","title":"GloTok: Global Perspective Tokenizer for Image Reconstruction and Generation","created":"2025-11-18","cited_by_count":2,"area":null},{"id":"2601.02256","title":"VAR RL Done Right: Tackling Asynchronous Policy Conflicts in Visual Autoregressive Generation","created":"2026-01-05","cited_by_count":2,"area":null},{"id":"2602.08337","title":"Language-Guided Transformer Tokenizer for Human Motion Generation","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2603.29634","title":"MacTok: Robust Continuous Tokenization for Image Generation","created":"2026-03-31","cited_by_count":2,"area":null},{"id":"2606.11096","title":"IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder","created":"2026-06-09","cited_by_count":2,"area":null},{"id":"2510.06673","title":"Heptapod: Language Modeling on Visual Signals","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2511.08416","title":"Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications","created":"2025-11-11","cited_by_count":1,"area":"diffusion"},{"id":"2512.16910","title":"SFTok: Bridging the Performance Gap in Discrete Tokenizers","created":"2025-12-18","cited_by_count":1,"area":null},{"id":"2601.01593","title":"Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation","created":"2026-01-04","cited_by_count":1,"area":null},{"id":"2602.03339","title":"Composable Visual Tokenizers with Generator-Free Diagnostics of Learnability","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2602.05534","title":"SSG: Scaled Spatial Guidance for Multi-Scale Visual Autoregressive Generation","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2603.25249","title":"Semantic-Aware Prefix Learning for Token-Efficient Image Generation","created":"2026-03-26","cited_by_count":1,"area":null},{"id":"2604.11707","title":"Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2604.24885","title":"VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations","created":"2026-04-27","cited_by_count":1,"area":"diffusion"},{"id":"2605.18115","title":"WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2605.18390","title":"Vision Foundation Models as Generalist Tokenizers for Image Generation","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2606.05552","title":"Balancing Image Compression and Generation with Bootstrapped Tokenization","created":"2026-06-04","cited_by_count":1,"area":null},{"id":"2607.19575","title":"VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers","created":"2026-07-21","cited_by_count":1,"area":null},{"id":"2503.16425","title":"Tokenize Image as a Set","created":"2025-03-20","cited_by_count":0,"area":null},{"id":"2503.17155","title":"D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens","created":"2025-03-21","cited_by_count":0,"area":null},{"id":"2504.04202","title":"Local-Order Auxiliary Losses Can Improve Autoencoder Reconstruction","created":"2025-04-05","cited_by_count":0,"area":null},{"id":"2509.26007","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2512.06421","title":"Rethinking Training Dynamics in Scale-wise Autoregressive Generation","created":"2025-12-06","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":"2603.22304","title":"Mitigating Premature Discretization with Progressive Quantization for Robust Vector Tokenization","created":"2026-03-17","cited_by_count":0,"area":"mllm"},{"id":"2605.06137","title":"Autoregressive Visual Generation Needs a Prologue","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.06148","title":"Learning Discrete Autoregressive Priors with Wasserstein Gradient Flow","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":"2605.27696","title":"Structure over Pixels: Learning Variable-Length Visual Programs","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2606.01543","title":"PathAR: Structure-First Autoregressive Synthesis of Multimodal Pathology Images","created":"2026-06-01","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":"2607.15898","title":"Orbis 2: A Hierarchical World Model for Driving","created":"2026-07-17","cited_by_count":0,"area":"wm"},{"id":"2607.24157","title":"UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2607.25527","title":"Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2609.02107","title":"A Unified Rate-Distortion Perspective on Vector, Product, and Scalar Quantization","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.09143","title":"Studying Image Tokenizers as Visual Languages in Unified Multimodal Models","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2609.24088","title":"A Latent Distribution Perspective on Evaluating and Improving Latent Generative Models","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.26103","title":"MIAR: Medical Image Super-Resolution With Autoregressive Modeling","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2610.00686","title":"SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation","created":"2026-09-30","cited_by_count":0,"area":"wm"},{"id":"2610.14162","title":"Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation","created":"2026-10-09","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}