{"total":202,"rows":[{"id":"2505.14683","title":"Emerging Properties in Unified Multimodal Pretraining","created":"2025-05-20","cited_by_count":897,"area":null},{"id":"2506.18871","title":"OmniGen2: Towards Instruction-Aligned Multimodal Generation","created":"2025-06-23","cited_by_count":253,"area":null},{"id":"2506.15564","title":"Show-o2: Improved Native Unified Multimodal Models","created":"2025-06-18","cited_by_count":250,"area":"diffusion"},{"id":"2506.23918","title":"Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers","created":"2025-06-30","cited_by_count":199,"area":"lr"},{"id":"2505.00703","title":"T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT","created":"2025-05-01","cited_by_count":166,"area":"lr"},{"id":"2502.20321","title":"UniTok: A Unified Tokenizer for Visual Generation and Understanding","created":"2025-02-27","cited_by_count":153,"area":null},{"id":"2510.26583","title":"Emu3.5: Native Multimodal Models are World Learners","created":"2025-10-30","cited_by_count":132,"area":"wm"},{"id":"2507.22058","title":"X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again","created":"2025-07-29","cited_by_count":112,"area":"deeprl"},{"id":"2505.05472","title":"Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation","created":"2025-05-08","cited_by_count":109,"area":null},{"id":"2508.09987","title":"Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation","created":"2025-08-13","cited_by_count":100,"area":null},{"id":"2502.03444","title":"Masked Autoencoders Are Effective Tokenizers for Diffusion Models","created":"2025-02-05","cited_by_count":96,"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":"2510.08377","title":"UniVideo: Unified Understanding, Generation, and Editing for Videos","created":"2025-10-09","cited_by_count":87,"area":null},{"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":"2503.21979","title":"Harmonizing Visual Representations for Unified Multimodal Understanding and Generation","created":"2025-03-27","cited_by_count":74,"area":null},{"id":"2506.09344","title":"Ming-Omni: A Unified Multimodal Model for Perception and Generation","created":"2025-06-11","cited_by_count":66,"area":null},{"id":"2412.04332","title":"Liquid: Language Models are Scalable and Unified Multi-modal Generators","created":"2024-12-05","cited_by_count":63,"area":null},{"id":"2504.02782","title":"GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation","created":"2025-04-03","cited_by_count":58,"area":null},{"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":"2508.05606","title":"Uni-cot: Towards Unified Chain-of-Thought Reasoning Across Text and Vision","created":"2025-08-07","cited_by_count":56,"area":"lr"},{"id":"2509.20360","title":"EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning","created":"2025-09-24","cited_by_count":54,"area":null},{"id":"2505.05422","title":"TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation","created":"2025-05-08","cited_by_count":51,"area":null},{"id":"2509.07295","title":"Reconstruction Alignment Improves Unified Multimodal Models","created":"2025-09-08","cited_by_count":49,"area":null},{"id":"2505.20147","title":"FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities","created":"2025-05-26","cited_by_count":46,"area":"mllm"},{"id":"2505.23661","title":"OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation","created":"2025-05-29","cited_by_count":46,"area":null},{"id":"2507.23278","title":"UniLiP: Adapting CLIP for Unified Multimodal Understanding, Generation and Editing","created":"2025-07-31","cited_by_count":44,"area":null},{"id":"2503.14324","title":"DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies","created":"2025-03-18","cited_by_count":42,"area":null},{"id":"2502.06788","title":"EVEv2: Improved Baselines for Encoder-Free Vision-Language Models","created":"2025-02-10","cited_by_count":41,"area":"mllm"},{"id":"2509.06945","title":"Interleaving Reasoning for Better Text-to-Image Generation","created":"2025-09-08","cited_by_count":41,"area":null},{"id":"2503.19312","title":"ImageGen-CoT: Enhancing Text-to-Image In-context Learning with Chain-of-Thought Reasoning","created":"2025-03-25","cited_by_count":37,"area":"lr"},{"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":"2512.02014","title":"TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models","created":"2025-12-01","cited_by_count":34,"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":"2505.14682","title":"UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation","created":"2025-05-20","cited_by_count":32,"area":"mllm"},{"id":"2510.08485","title":"InstructX: Towards Unified Visual Editing with MLLM Guidance","created":"2025-10-09","cited_by_count":32,"area":"mllm"},{"id":"2605.12500","title":"SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture","created":"2026-05-12","cited_by_count":32,"area":"mllm"},{"id":"2505.13031","title":"MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO","created":"2025-05-19","cited_by_count":30,"area":"lr"},{"id":"2510.06590","title":"Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer","created":"2025-10-08","cited_by_count":28,"area":null},{"id":"2412.15321","title":"Next Patch Prediction for Autoregressive Visual Generation","created":"2024-12-19","cited_by_count":26,"area":null},{"id":"2505.23380","title":"UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning","created":"2025-05-29","cited_by_count":24,"area":"mllm"},{"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":"2512.19693","title":"The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding","created":"2025-12-22","cited_by_count":22,"area":null},{"id":"2503.06764","title":"SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook for Multimodal Understanding and Generation","created":"2025-03-09","cited_by_count":21,"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":"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":"2502.12148","title":"HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation","created":"2025-02-17","cited_by_count":18,"area":"mllm"},{"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":"2604.24763","title":"Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation","created":"2026-04-27","cited_by_count":18,"area":null},{"id":"2506.09040","title":"Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better","created":"2025-06-10","cited_by_count":17,"area":"mllm"},{"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":"2508.03320","title":"Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding and Generation","created":"2025-08-05","cited_by_count":17,"area":null},{"id":"2509.24335","title":"Hyperspherical Latents Improve Continuous-Token Autoregressive Generation","created":"2025-09-29","cited_by_count":16,"area":"diffusion"},{"id":"2505.17534","title":"Co-Reinforcement Learning for Unified Multimodal Understanding and Generation","created":"2025-05-23","cited_by_count":15,"area":"lr"},{"id":"2502.04397","title":"Multimodal Medical Code Tokenizer","created":"2025-02-06","cited_by_count":14,"area":null},{"id":"2601.02204","title":"NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation","created":"2026-01-05","cited_by_count":14,"area":null},{"id":"2603.16870","title":"Demystifying Video Reasoning","created":"2026-03-17","cited_by_count":14,"area":"lr"},{"id":"2505.02471","title":"Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction","created":"2025-05-05","cited_by_count":13,"area":null},{"id":"2505.18142","title":"TokBench: Evaluating Your Visual Tokenizer before Visual Generation","created":"2025-05-23","cited_by_count":13,"area":null},{"id":"2506.02975","title":"HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation","created":"2025-06-03","cited_by_count":13,"area":null},{"id":"2507.04947","title":"DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer","created":"2025-07-07","cited_by_count":13,"area":null},{"id":"2509.09666","title":"Unified Multimodal Models as Auto-Encoders","created":"2025-09-11","cited_by_count":13,"area":null},{"id":"2509.18824","title":"Hyper-Bagel: A Unified Acceleration Framework for Multimodal Understanding and Generation","created":"2025-09-23","cited_by_count":13,"area":null},{"id":"2512.05112","title":"DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation","created":"2025-12-04","cited_by_count":13,"area":"lr"},{"id":"2512.16920","title":"EasyV2V: A High-quality Instruction-based Video Editing Framework","created":"2025-12-18","cited_by_count":13,"area":null},{"id":"2412.10443","title":"SweetTok: Semantic-Aware Spatial-Temporal Tokenizer for Compact Video Discretization","created":"2024-12-11","cited_by_count":12,"area":null},{"id":"2510.09012","title":"Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy","created":"2025-10-10","cited_by_count":12,"area":null},{"id":"2602.02140","title":"Quantifying the Gap between Understanding and Generation within Unified Multimodal Models","created":"2026-02-02","cited_by_count":12,"area":null},{"id":"2602.02437","title":"UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing","created":"2026-02-02","cited_by_count":12,"area":"lr"},{"id":"2605.18678","title":"Lance: Unified Multimodal Modeling by Multi-Task Synergy","created":"2026-05-18","cited_by_count":12,"area":null},{"id":"2505.19535","title":"TDVE-Assessor: Benchmarking and Evaluating the Quality of Text-Driven Video Editing with LMMs","created":"2025-05-26","cited_by_count":11,"area":null},{"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":"2505.03798","title":"Position: Foundation Models Need Digital Twin Representations","created":"2025-05-01","cited_by_count":10,"area":null},{"id":"2507.16663","title":"Turning Internal Gap into Self-Improvement: Promoting the Generation-Understanding Unification in MLLMs","created":"2025-07-22","cited_by_count":10,"area":"synth"},{"id":"2508.05580","title":"Follow-Your-Instruction: A Comprehensive MLLM Agent for World Data Synthesis","created":"2025-08-07","cited_by_count":10,"area":"mllm"},{"id":"2511.23386","title":"VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction","created":"2025-11-28","cited_by_count":10,"area":null},{"id":"2512.19686","title":"Visual-Aware CoT: Achieving High-Fidelity Visual Consistency in Unified Models","created":"2025-12-22","cited_by_count":10,"area":"lr"},{"id":"2508.05599","title":"WeTok: Powerful Discrete Tokenization for High-Fidelity Visual Reconstruction","created":"2025-08-07","cited_by_count":9,"area":null},{"id":"2511.18262","title":"MammothModa2: A Unified AR-Diffusion Framework for Multimodal Understanding and Generation","created":"2025-11-23","cited_by_count":9,"area":"diffusion"},{"id":"2511.19413","title":"UniGame: Turning a Unified Multimodal Model Into Its Own Adversary","created":"2025-11-24","cited_by_count":9,"area":null},{"id":"2601.22155","title":"UEval: A Benchmark for Unified Multimodal Generation","created":"2026-01-29","cited_by_count":9,"area":"lr"},{"id":"2602.09024","title":"Autoregressive Image Generation with Masked Bit Modeling","created":"2026-02-09","cited_by_count":9,"area":null},{"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":"2507.22920","title":"Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey","created":"2025-07-21","cited_by_count":8,"area":"mllm"},{"id":"2511.14760","title":"UniGen-1.5: Enhancing Image Generation and Editing through Reward Unification in Reinforcement Learning","created":"2025-11-18","cited_by_count":8,"area":"mllm"},{"id":"2512.06065","title":"EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing","created":"2025-12-05","cited_by_count":8,"area":null},{"id":"2602.12279","title":"UniT: Unified Multimodal Chain-of-Thought Test-time Scaling","created":"2026-02-12","cited_by_count":8,"area":"lr"},{"id":"2603.19228","title":"SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing","created":"2026-03-19","cited_by_count":8,"area":null},{"id":"2505.17726","title":"Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM","created":"2025-05-23","cited_by_count":7,"area":"mllm"},{"id":"2601.02046","title":"Agentic Retoucher for Text-To-Image Generation","created":"2026-01-05","cited_by_count":7,"area":"diffusion"},{"id":"2603.12267","title":"EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation","created":"2026-03-12","cited_by_count":7,"area":null},{"id":"2603.12829","title":"coDrawAgents: A Multi-Agent Dialogue Framework for Compositional Image Generation","created":"2026-03-13","cited_by_count":7,"area":null},{"id":"2502.03429","title":"On Fairness of Unified Multimodal Large Language Model for Image Generation","created":"2025-02-05","cited_by_count":6,"area":"mllm"},{"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":"2509.21912","title":"Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching","created":"2025-09-26","cited_by_count":6,"area":"diffusion"},{"id":"2512.03125","title":"Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models","created":"2025-12-02","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":"2603.08652","title":"CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation","created":"2026-03-09","cited_by_count":6,"area":"lr"},{"id":"2603.25741","title":"Vega: Learning to Drive with Natural Language Instructions","created":"2026-03-26","cited_by_count":6,"area":"wm"},{"id":"2606.30599","title":"Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video Editing","created":"2026-06-29","cited_by_count":6,"area":null},{"id":"2503.08120","title":"UniF$^2$ace: A Unified Fine-grained Face Understanding and Generation Model","created":"2025-03-11","cited_by_count":5,"area":null}],"next_cursor":"100"}