{"total":30,"rows":[{"id":"2412.07772","title":"From Slow Bidirectional to Fast Autoregressive Video Diffusion Models","created":"2024-12-10","cited_by_count":433,"area":"diffusion"},{"id":"2505.15809","title":"MMaDA: Multimodal Large Diffusion Language Models","created":"2025-05-21","cited_by_count":250,"area":"diffusion"},{"id":"2506.15564","title":"Show-o2: Improved Native Unified Multimodal Models","created":"2025-06-18","cited_by_count":250,"area":"diffusion"},{"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":"2410.01756","title":"ImageFolder: Autoregressive Image Generation with Folded Tokens","created":"2024-10-02","cited_by_count":94,"area":"diffusion"},{"id":"2510.06308","title":"Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding","created":"2025-10-07","cited_by_count":88,"area":"diffusion"},{"id":"2502.15894","title":"RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers","created":"2025-02-21","cited_by_count":66,"area":"diffusion"},{"id":"2412.03293","title":"Diffusion-VLA: Generalizable and Interpretable Robot Foundation Model via Self-Generated Reasoning","created":"2024-12-04","cited_by_count":57,"area":"diffusion"},{"id":"2505.23606","title":"Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model","created":"2025-05-29","cited_by_count":55,"area":"diffusion"},{"id":"2601.16208","title":"Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders","created":"2026-01-22","cited_by_count":53,"area":"diffusion"},{"id":"2412.07720","title":"ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer","created":"2024-12-10","cited_by_count":45,"area":"diffusion"},{"id":"2509.25162","title":"AlignTok: Aligning Visual Foundation Encoders to Tokenizers for Diffusion Models","created":"2025-09-29","cited_by_count":36,"area":"diffusion"},{"id":"2510.13721","title":"NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching","created":"2025-10-15","cited_by_count":21,"area":"diffusion"},{"id":"2603.23500","title":"UniGRPO: Unified Policy Optimization for Reasoning-Driven Visual Generation","created":"2026-03-24","cited_by_count":13,"area":"diffusion"},{"id":"2511.20123","title":"UltraViCo: Breaking Extrapolation Limits in Video Diffusion Transformers","created":"2025-11-25","cited_by_count":12,"area":"diffusion"},{"id":"2511.20520","title":"HBridge: H-Shape Bridging of Heterogeneous Experts for Unified Multimodal Understanding and Generation","created":"2025-11-25","cited_by_count":11,"area":"diffusion"},{"id":"2412.04432","title":"Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation","created":"2024-12-05","cited_by_count":9,"area":"diffusion"},{"id":"2504.08641","title":"Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization","created":"2025-04-11","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":"2509.21912","title":"Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching","created":"2025-09-26","cited_by_count":6,"area":"diffusion"},{"id":"2512.14008","title":"Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models","created":"2025-12-16","cited_by_count":6,"area":"diffusion"},{"id":"2505.12274","title":"Context-Aware Autoregressive Models for Multi-Conditional Image Generation","created":"2025-05-18","cited_by_count":5,"area":"diffusion"},{"id":"2605.25195","title":"Baton: Explicit Semantic Blueprints for Joint Video-Audio Generation","created":"2026-05-24","cited_by_count":4,"area":"diffusion"},{"id":"2511.16917","title":"UniModel: A Visual-Only Framework for Unified Multimodal Understanding and Generation","created":"2025-11-21","cited_by_count":3,"area":"diffusion"},{"id":"2511.12207","title":"Mixture of States: Routing Token-Level Dynamics for Multimodal Generation","created":"2025-11-15","cited_by_count":2,"area":"diffusion"},{"id":"2510.06131","title":"Discrete Diffusion Models with MLLMs for Unified Medical Multimodal Generation","created":"2025-10-07","cited_by_count":1,"area":"diffusion"},{"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":"2607.22531","title":"Twins: Learn to Predict Unified Representations with Focal Loss","created":"2026-07-24","cited_by_count":1,"area":"diffusion"},{"id":"2512.02794","title":"PhyCustom: Towards Realistic Physical Customization in Text-to-Image Generation","created":"2025-12-01","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}