{"total":83,"rows":[{"id":"2502.09992","title":"Large Language Diffusion Models","created":"2025-02-14","cited_by_count":912,"area":"diffusion"},{"id":"2310.16834","title":"Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution","created":"2023-10-25","cited_by_count":745,"area":"diffusion"},{"id":"2406.04329","title":"Simplified and Generalized Masked Diffusion for Discrete Data","created":"2024-06-06","cited_by_count":626,"area":"diffusion"},{"id":"2210.08933","title":"DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models","created":"2022-10-17","cited_by_count":450,"area":"diffusion"},{"id":"2505.19223","title":"LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models","created":"2025-05-25","cited_by_count":285,"area":"diffusion"},{"id":"2410.17891","title":"Scaling Diffusion Language Models via Adaptation from Autoregressive Models","created":"2024-10-23","cited_by_count":274,"area":"diffusion"},{"id":"2301.10677","title":"Imitating Human Behaviour with Diffusion Models","created":"2023-01-25","cited_by_count":263,"area":"diffusion"},{"id":"2211.15089","title":"Continuous diffusion for categorical data","created":"2022-11-28","cited_by_count":214,"area":"diffusion"},{"id":"2502.06768","title":"Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions","created":"2025-02-10","cited_by_count":214,"area":"diffusion"},{"id":"2210.17432","title":"SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control","created":"2022-10-31","cited_by_count":194,"area":"diffusion"},{"id":"2302.05737","title":"A Reparameterized Discrete Diffusion Model for Text Generation","created":"2023-02-11","cited_by_count":152,"area":"diffusion"},{"id":"2303.08137","title":"LayoutDM: Discrete Diffusion Model for Controllable Layout Generation","created":"2023-03-14","cited_by_count":143,"area":"diffusion"},{"id":"2506.10892","title":"The Diffusion Duality","created":"2025-06-12","cited_by_count":135,"area":"diffusion"},{"id":"2305.09515","title":"AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation","created":"2023-05-16","cited_by_count":118,"area":"diffusion"},{"id":"2211.04236","title":"Self-conditioned Embedding Diffusion for Text Generation","created":"2022-11-08","cited_by_count":117,"area":"diffusion"},{"id":"2212.09462","title":"Latent Diffusion for Language Generation","created":"2022-12-19","cited_by_count":115,"area":"diffusion"},{"id":"2512.05081","title":"Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compression","created":"2025-12-04","cited_by_count":85,"area":"diffusion"},{"id":"2212.11685","title":"Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise","created":"2022-12-22","cited_by_count":79,"area":"diffusion"},{"id":"2405.14664","title":"Fisher Flow Matching for Generative Modeling over Discrete Data","created":"2024-05-23","cited_by_count":72,"area":"diffusion"},{"id":"2306.02531","title":"PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model","created":"2023-06-05","cited_by_count":58,"area":"diffusion"},{"id":"2410.23405","title":"FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions","created":"2024-10-30","cited_by_count":53,"area":"diffusion"},{"id":"2511.05664","title":"KLASS: KL-Guided Fast Inference in Masked Diffusion Models","created":"2025-11-07","cited_by_count":47,"area":"diffusion"},{"id":"2210.16886","title":"DiffusER: Discrete Diffusion via Edit-based Reconstruction","created":"2022-10-30","cited_by_count":41,"area":"diffusion"},{"id":"2309.01372","title":"DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion","created":"2023-09-04","cited_by_count":37,"area":"diffusion"},{"id":"2506.20701","title":"Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models","created":"2025-06-25","cited_by_count":34,"area":"diffusion"},{"id":"2401.01808","title":"aMUSEd: An Open MUSE Reproduction","created":"2024-01-03","cited_by_count":32,"area":"diffusion"},{"id":"2508.13148","title":"MDPO: Overcoming the Training-Inference Divide of Masked Diffusion Language Models","created":"2025-08-18","cited_by_count":32,"area":"diffusion"},{"id":"2310.17680","title":"CodeFusion: A Pre-trained Diffusion Model for Code Generation","created":"2023-10-26","cited_by_count":31,"area":"diffusion"},{"id":"2303.03755","title":"DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer","created":"2023-03-07","cited_by_count":20,"area":"diffusion"},{"id":"2306.05423","title":"ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process","created":"2023-06-08","cited_by_count":20,"area":"diffusion"},{"id":"2308.15459","title":"ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer","created":"2023-08-29","cited_by_count":18,"area":"diffusion"},{"id":"2302.02070","title":"Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification","created":"2023-02-04","cited_by_count":16,"area":"diffusion"},{"id":"2504.10317","title":"Analysis of Attention in Video Diffusion Transformers","created":"2025-04-14","cited_by_count":15,"area":"diffusion"},{"id":"2605.06548","title":"Continuous Latent Diffusion Language Model","created":"2026-05-07","cited_by_count":14,"area":"diffusion"},{"id":"2605.22765","title":"Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation","created":"2026-05-21","cited_by_count":13,"area":"diffusion"},{"id":"2602.21185","title":"The Diffusion Duality, Chapter II: $\\Psi$-Samplers","created":"2026-02-24","cited_by_count":12,"area":"diffusion"},{"id":"2305.04465","title":"Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!","created":"2023-05-08","cited_by_count":11,"area":"diffusion"},{"id":"2605.07933","title":"How to Train Your Latent Diffusion Language Model Jointly With the Latent Space","created":"2026-05-08","cited_by_count":11,"area":"diffusion"},{"id":"2505.08622","title":"Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models","created":"2025-05-13","cited_by_count":10,"area":"diffusion"},{"id":"2509.00707","title":"Reward-Weighted Sampling: Enhancing Non-Autoregressive Characteristics in Masked Diffusion LLMs","created":"2025-08-31","cited_by_count":10,"area":"diffusion"},{"id":"2509.16842","title":"DoubleGen: Debiased Generative Modeling of Counterfactuals","created":"2025-09-20","cited_by_count":9,"area":"diffusion"},{"id":"2509.15188","title":"Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning","created":"2025-09-18","cited_by_count":8,"area":"diffusion"},{"id":"2509.22963","title":"Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces","created":"2025-09-26","cited_by_count":7,"area":"diffusion"},{"id":"2602.18176","title":"Improving Sampling for Masked Diffusion Models via Information Gain","created":"2026-02-20","cited_by_count":7,"area":"diffusion"},{"id":"2410.14758","title":"Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion","created":"2024-10-18","cited_by_count":5,"area":"diffusion"},{"id":"2502.09767","title":"Non-Markovian Discrete Diffusion with Causal Language Models","created":"2025-02-13","cited_by_count":5,"area":"diffusion"},{"id":"2603.26661","title":"GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation","created":"2026-03-27","cited_by_count":5,"area":"diffusion"},{"id":"2311.09741","title":"P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models","created":"2023-11-16","cited_by_count":4,"area":"diffusion"},{"id":"2409.03717","title":"Sample-Efficient Diffusion for Text-To-Speech Synthesis","created":"2024-09-01","cited_by_count":4,"area":"diffusion"},{"id":"2509.16518","title":"FG-Attn: Leveraging Fine-Grained Sparse Attention in Video Diffusion Models","created":"2025-09-20","cited_by_count":4,"area":"diffusion"},{"id":"2510.18072","title":"Fine-tuning Flow Matching Generative Models with Intermediate Feedback","created":"2025-10-20","cited_by_count":4,"area":"diffusion"},{"id":"2606.23489","title":"MeshFlow: Mesh Generation with Equivariant Flow Matching","created":"2026-06-22","cited_by_count":4,"area":"diffusion"},{"id":"2503.20429","title":"Latent Beam Diffusion Models for Generating Visual Sequences","created":"2025-03-26","cited_by_count":2,"area":"diffusion"},{"id":"2510.26200","title":"Don't Let It Fade: Preserving Edits in Diffusion Language Models via Token Timestep Allocation","created":"2025-10-30","cited_by_count":2,"area":"diffusion"},{"id":"2607.00588","title":"Low Perplexity is Repetition: A One-Dimensional Self-Conditioning Attractor in Continuous Diffusion LMs","created":"2026-07-01","cited_by_count":2,"area":"diffusion"},{"id":"2609.17691","title":"Accelerating Diffusion Sampling via Speculative Draft Trees","created":"2026-09-15","cited_by_count":2,"area":"diffusion"},{"id":"2602.18695","title":"Insertion Based Sequence Generation with Learnable Order Dynamics","created":"2026-02-21","cited_by_count":1,"area":"diffusion"},{"id":"2602.19619","title":"Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models","created":"2026-02-23","cited_by_count":1,"area":"diffusion"},{"id":"2603.02230","title":"Generalized Discrete Diffusion with Self-Correction","created":"2026-02-13","cited_by_count":1,"area":"diffusion"},{"id":"2603.19146","title":"D5P4: Partition Determinantal Point Process for Diversity in Parallel Discrete Diffusion Decoding","created":"2026-03-19","cited_by_count":1,"area":"diffusion"},{"id":"2606.01024","title":"DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs","created":"2026-05-31","cited_by_count":1,"area":"diffusion"},{"id":"2607.04140","title":"DELTA-TTS: Adapting Autoregressive Model into Diffusion Language Model for Text-to-Speech","created":"2026-07-05","cited_by_count":1,"area":"diffusion"},{"id":"2607.24763","title":"CaRE Compute-aware Remasking Evaluation Protocol for Masked Diffusion Language Models","created":"2026-06-04","cited_by_count":1,"area":"diffusion"},{"id":"2608.00024","title":"Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance","created":"2026-07-10","cited_by_count":1,"area":"diffusion"},{"id":"2609.01043","title":"From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion","created":"2026-09-01","cited_by_count":1,"area":"diffusion"},{"id":"2609.35553","title":"Simplex Diffusion Models","created":"2026-09-28","cited_by_count":1,"area":"diffusion"},{"id":"2305.15025","title":"Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation","created":"2023-05-24","cited_by_count":0,"area":"diffusion"},{"id":"2402.10107","title":"Quantized Embedding Vectors for Controllable Diffusion Language Models","created":"2024-02-15","cited_by_count":0,"area":"diffusion"},{"id":"2409.13739","title":"Table-to-Text Generation with Pretrained Diffusion Models","created":"2024-09-10","cited_by_count":0,"area":"diffusion"},{"id":"2510.01028","title":"Syntax-Guided Diffusion Language Models with User-Integrated Personalization","created":"2025-10-01","cited_by_count":0,"area":"diffusion"},{"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":"2601.02799","title":"Stratified Hazard Sampling: Minimal-Variance Event Scheduling for CTMC/DTMC Discrete Diffusion and Flow Models","created":"2026-01-06","cited_by_count":0,"area":"diffusion"},{"id":"2602.17602","title":"MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models","created":"2026-02-19","cited_by_count":0,"area":"diffusion"},{"id":"2605.13681","title":"Sampling from Flow Language Models via Marginal-Conditioned Bridges","created":"2026-05-13","cited_by_count":0,"area":"diffusion"},{"id":"2605.27971","title":"Semantic Flow Regularization: Teaching LLMs to Generate Diverse Yet Coherent Responses","created":"2026-05-27","cited_by_count":0,"area":"diffusion"},{"id":"2606.06031","title":"NAVIRA: Decoupled Stochastic Remasking for Masked Diffusion Language Models","created":"2026-06-04","cited_by_count":0,"area":"diffusion"},{"id":"2607.10535","title":"Improving Sample Diversity in Autoregressive Text-to-Image Generation via Cluster Truncation","created":"2026-07-12","cited_by_count":0,"area":"diffusion"},{"id":"2607.10853","title":"Diversify Diffusion with Temperature Sampling and Variance-Corrective Time Shifting","created":"2026-07-12","cited_by_count":0,"area":"diffusion"},{"id":"2609.17535","title":"DANTINOX: A Unified Framework for Multi-Paradigm Language Modeling","created":"2026-07-11","cited_by_count":0,"area":"diffusion"},{"id":"2609.33698","title":"One Latent, Many Tokens: Jointly Learning Compressed Embeddings for Efficient Language Diffusion","created":"2026-09-27","cited_by_count":0,"area":"diffusion"},{"id":"2609.33947","title":"Simple Diffusion Language Models Are More Effective Few-Step Generators Than Reported","created":"2026-09-27","cited_by_count":0,"area":"diffusion"},{"id":"2609.38536","title":"Does This Action Still Explain the Task? Reverse Scoring for Diffusion Language Model Agents","created":"2026-09-29","cited_by_count":0,"area":"diffusion"},{"id":"2610.01275","title":"Know When to Hold 'em: Correct-Token Retention in Uniform-State Diffusion Language Models","created":"2026-10-01","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}