{"total":110,"rows":[{"id":"2506.08009","title":"Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion","created":"2025-06-09","cited_by_count":625,"area":"diffusion"},{"id":"2502.01776","title":"Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity","created":"2025-02-03","cited_by_count":217,"area":"diffusion"},{"id":"2510.02283","title":"Self-Forcing++: Towards Minute-Scale High-Quality Video Generation","created":"2025-10-02","cited_by_count":192,"area":"diffusion"},{"id":"2506.20639","title":"DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation","created":"2025-06-25","cited_by_count":187,"area":"diffusion"},{"id":"2502.06764","title":"History-Guided Video Diffusion","created":"2025-02-10","cited_by_count":171,"area":"diffusion"},{"id":"2505.18875","title":"Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation","created":"2025-05-24","cited_by_count":106,"area":"diffusion"},{"id":"2505.13389","title":"VSA: Faster Video Diffusion with Trainable Sparse Attention","created":"2025-05-19","cited_by_count":104,"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":"2502.21079","title":"Training-free and Adaptive Sparse Attention for Efficient Long Video Generation","created":"2025-02-28","cited_by_count":69,"area":"diffusion"},{"id":"2506.19852","title":"Radial Attention: $O(n\\log n)$ Sparse Attention with Energy Decay for Long Video Generation","created":"2025-06-24","cited_by_count":69,"area":"diffusion"},{"id":"2509.24006","title":"SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention","created":"2025-09-28","cited_by_count":58,"area":"diffusion"},{"id":"2405.15330","title":"Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model","created":"2024-05-24","cited_by_count":48,"area":"diffusion"},{"id":"2405.17873","title":"MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization","created":"2024-05-28","cited_by_count":43,"area":"diffusion"},{"id":"2508.14148","title":"DPad: Efficient Diffusion Language Models with Suffix Dropout","created":"2025-08-19","cited_by_count":39,"area":"diffusion"},{"id":"2506.03065","title":"Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers","created":"2025-06-03","cited_by_count":34,"area":"diffusion"},{"id":"2602.07775","title":"Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion","created":"2026-02-08","cited_by_count":32,"area":"diffusion"},{"id":"2605.18739","title":"LongLive-2.0: An NVFP4 Parallel Infrastructure for Long Video Generation","created":"2026-05-18","cited_by_count":30,"area":"diffusion"},{"id":"2502.06155","title":"Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile","created":"2025-02-10","cited_by_count":26,"area":"diffusion"},{"id":"2505.20353","title":"FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximation","created":"2025-05-26","cited_by_count":24,"area":"diffusion"},{"id":"2505.18584","title":"Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activations","created":"2025-05-24","cited_by_count":23,"area":"diffusion"},{"id":"2411.07506","title":"FlowTS: Time Series Generation via Rectified Flow","created":"2024-11-12","cited_by_count":22,"area":"diffusion"},{"id":"2505.14708","title":"DraftAttention: Fast Video Diffusion via Low-Resolution Attention Guidance","created":"2025-05-17","cited_by_count":22,"area":"diffusion"},{"id":"2602.12675","title":"SLA2: Sparse-Linear Attention with Learnable Routing and QAT","created":"2026-02-13","cited_by_count":22,"area":"diffusion"},{"id":"2503.22796","title":"DiTFastAttnV2: Head-wise Attention Compression for Multi-Modality Diffusion Transformers","created":"2025-03-28","cited_by_count":20,"area":"diffusion"},{"id":"2510.15731","title":"Attention Sinks in Diffusion Language Models","created":"2025-10-17","cited_by_count":20,"area":"diffusion"},{"id":"2601.20499","title":"Efficient Autoregressive Video Diffusion with Dummy Head","created":"2026-01-28","cited_by_count":20,"area":"diffusion"},{"id":"2509.01085","title":"Bidirectional Sparse Attention for Faster Video Diffusion Training","created":"2025-09-01","cited_by_count":19,"area":"diffusion"},{"id":"2602.13515","title":"SpargeAttention2: Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning","created":"2026-02-13","cited_by_count":18,"area":"diffusion"},{"id":"2603.25730","title":"PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference","created":"2026-03-26","cited_by_count":17,"area":"diffusion"},{"id":"2607.20368","title":"Self Gradient Forcing: Native Long Video Extrapolation","created":"2026-07-22","cited_by_count":17,"area":"diffusion"},{"id":"2601.21896","title":"Past- and Future-Informed KV Cache Policy with Salience Estimation in Autoregressive Video Diffusion","created":"2026-01-29","cited_by_count":16,"area":"diffusion"},{"id":"2603.11647","title":"OmniForcing: Unleashing Real-time Joint Audio-Visual Generation","created":"2026-03-12","cited_by_count":16,"area":"diffusion"},{"id":"2603.13405","title":"Anchor Forcing: Anchor Memory and Tri-Region RoPE for Interactive Streaming Video Diffusion","created":"2026-03-12","cited_by_count":16,"area":"diffusion"},{"id":"2502.07590","title":"DSV: Exploiting Dynamic Sparsity to Accelerate Large-Scale Video DiT Training","created":"2025-02-11","cited_by_count":15,"area":"diffusion"},{"id":"2504.10317","title":"Analysis of Attention in Video Diffusion Transformers","created":"2025-04-14","cited_by_count":15,"area":"diffusion"},{"id":"2605.09681","title":"Forcing-KV: Hybrid KV Cache Compression for Efficient Autoregressive Video Diffusion Models","created":"2026-05-10","cited_by_count":15,"area":"diffusion"},{"id":"2605.22718","title":"WorldKV: Efficient World Memory with World Retrieval and Compression","created":"2026-05-21","cited_by_count":15,"area":"diffusion"},{"id":"2607.08766","title":"OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators","created":"2026-07-09","cited_by_count":14,"area":"diffusion"},{"id":"2503.18150","title":"LongDiff: Training-Free Long Video Generation in One Go","created":"2025-03-23","cited_by_count":13,"area":"diffusion"},{"id":"2411.15236","title":"Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps","created":"2024-11-21","cited_by_count":12,"area":"diffusion"},{"id":"2602.14027","title":"Train Short, Inference Long: Training-free Horizon Extension for Autoregressive Video Generation","created":"2026-02-15","cited_by_count":11,"area":"diffusion"},{"id":"2604.21221","title":"Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation","created":"2026-04-23","cited_by_count":11,"area":"diffusion"},{"id":"2605.30409","title":"SANA-Streaming: Real-time Streaming Video Editing with Hybrid Diffusion Transformer","created":"2026-05-28","cited_by_count":11,"area":"diffusion"},{"id":"2505.22918","title":"Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape","created":"2025-05-28","cited_by_count":10,"area":"diffusion"},{"id":"2512.23576","title":"LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation","created":"2025-12-29","cited_by_count":9,"area":"diffusion"},{"id":"2605.15741","title":"HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion","created":"2026-05-15","cited_by_count":9,"area":"diffusion"},{"id":"2512.21338","title":"HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming","created":"2025-12-24","cited_by_count":8,"area":"diffusion"},{"id":"2604.10103","title":"Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation","created":"2026-04-11","cited_by_count":8,"area":"diffusion"},{"id":"2407.08701","title":"Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models","created":"2024-07-11","cited_by_count":7,"area":"diffusion"},{"id":"2509.23681","title":"QuantSparse: Comprehensively Compressing Video Diffusion Transformer with Model Quantization and Attention Sparsification","created":"2025-09-28","cited_by_count":7,"area":"diffusion"},{"id":"2510.11538","title":"Massive Activations are the Key to Local Detail Synthesis in Diffusion Transformers","created":"2025-10-13","cited_by_count":7,"area":"diffusion"},{"id":"2606.10671","title":"FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion","created":"2026-06-09","cited_by_count":7,"area":"diffusion"},{"id":"2605.23445","title":"DFSAttn: Dynamic Fine-grained Sparse Attention for Efficient Video Generation","created":"2026-05-22","cited_by_count":6,"area":"diffusion"},{"id":"2511.19835","title":"Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation","created":"2025-11-25","cited_by_count":5,"area":"diffusion"},{"id":"2601.07894","title":"Revealing the Attention Floating Mechanism in Masked Diffusion Models","created":"2026-01-12","cited_by_count":5,"area":"diffusion"},{"id":"2507.09785","title":"Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow","created":"2025-07-13","cited_by_count":4,"area":"diffusion"},{"id":"2601.19657","title":"One Token Is Enough: Improving Diffusion Language Models with a Sink Token","created":"2026-01-27","cited_by_count":4,"area":"diffusion"},{"id":"2602.02128","title":"Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics","created":"2026-02-02","cited_by_count":4,"area":"diffusion"},{"id":"2603.05117","title":"SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation","created":"2026-03-05","cited_by_count":4,"area":"diffusion"},{"id":"2606.18702","title":"UniTemp: Unlocking Video Generation in Any Temporal Order via Bidirectional Distillation","created":"2026-06-17","cited_by_count":4,"area":"diffusion"},{"id":"2607.19139","title":"Text Template Tokens Are Implicit Semantic Registers in Diffusion Transformers","created":"2026-07-21","cited_by_count":4,"area":"diffusion"},{"id":"2607.27110","title":"FreqForcing: Autoregressive Long Video Generation via Spectral Self-Anchoring","created":"2026-07-29","cited_by_count":4,"area":"diffusion"},{"id":"2608.27922","title":"DensityKV: Density-Guided KV Cache Compression for Long Video Generation","created":"2026-08-28","cited_by_count":4,"area":"diffusion"},{"id":"2406.14130","title":"ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning","created":"2024-06-20","cited_by_count":3,"area":"diffusion"},{"id":"2601.01224","title":"Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment","created":"2026-01-03","cited_by_count":3,"area":"diffusion"},{"id":"2601.22275","title":"VMonarch: Efficient Video Diffusion Transformers with Structured Attention","created":"2026-01-29","cited_by_count":3,"area":"diffusion"},{"id":"2602.13136","title":"Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching","created":"2026-02-13","cited_by_count":3,"area":"diffusion"},{"id":"2603.06178","title":"Making Training-Free Diffusion Segmentors Scale with the Generative Power","created":"2026-03-06","cited_by_count":3,"area":"diffusion"},{"id":"2604.21592","title":"Sculpt4D: Generating 4D Shapes via Sparse-Attention Diffusion Transformers","created":"2026-04-23","cited_by_count":3,"area":"diffusion"},{"id":"2605.09313","title":"Attention Sinks in Diffusion Transformers: A Causal Analysis","created":"2026-05-10","cited_by_count":3,"area":"diffusion"},{"id":"2605.30349","title":"AdaState: Self-Evolving Anchors for Streaming Video Generation","created":"2026-05-28","cited_by_count":3,"area":"diffusion"},{"id":"2606.10537","title":"Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models","created":"2026-06-09","cited_by_count":3,"area":"diffusion"},{"id":"2606.19005","title":"Sumi: Open Uniform Diffusion Language Model from Scratch","created":"2026-06-17","cited_by_count":3,"area":"diffusion"},{"id":"2609.02108","title":"Predict, Don't Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Models","created":"2026-09-02","cited_by_count":3,"area":"diffusion"},{"id":"2509.22650","title":"RefAM: Attention Magnets for Zero-Shot Referral Segmentation","created":"2025-09-26","cited_by_count":2,"area":"diffusion"},{"id":"2509.25401","title":"FlashOmni: A Unified Sparse Attention Engine for Diffusion Transformers","created":"2025-09-29","cited_by_count":2,"area":"diffusion"},{"id":"2602.17664","title":"Sink-Aware Pruning for Diffusion Language Models","created":"2026-02-19","cited_by_count":2,"area":"diffusion"},{"id":"2603.17825","title":"Steering Video Diffusion Transformers with Massive Activations","created":"2026-03-18","cited_by_count":2,"area":"diffusion"},{"id":"2604.12056","title":"LoSA: Locality Aware Sparse Attention for Block-Wise Diffusion Language Models","created":"2026-04-13","cited_by_count":2,"area":"diffusion"},{"id":"2605.06366","title":"Layer Collapse in Diffusion Language Models","created":"2026-05-07","cited_by_count":2,"area":"diffusion"},{"id":"2605.16147","title":"Registers Matter for Pixel-Space Diffusion Transformers","created":"2026-05-15","cited_by_count":2,"area":"diffusion"},{"id":"2605.18346","title":"Focused Forcing: Content-Aware Per-Frame KV Selection for Efficient Autoregressive Video Diffusion","created":"2026-05-18","cited_by_count":2,"area":"diffusion"},{"id":"2605.29233","title":"BlockBatch: Multi-Scale Consensus Decoding for Efficient Diffusion Language Model Inference","created":"2026-05-28","cited_by_count":2,"area":"diffusion"},{"id":"2606.23019","title":"ScalingAttention: Discovering Intrinsic Sparse Attention Topology for Video Diffusion Transformers","created":"2026-06-22","cited_by_count":2,"area":"diffusion"},{"id":"2511.22146","title":"C$^2$DLM: Causal Concept-Guided Diffusion Large Language Models","created":"2025-11-27","cited_by_count":1,"area":"diffusion"},{"id":"2603.09582","title":"BinaryAttention: One-Bit QK-Attention for Vision and Diffusion Transformers","created":"2026-03-10","cited_by_count":1,"area":"diffusion"},{"id":"2605.05206","title":"Taming Outlier Tokens in Diffusion Transformers","created":"2026-05-06","cited_by_count":1,"area":"diffusion"},{"id":"2605.19726","title":"Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention","created":"2026-05-19","cited_by_count":1,"area":"diffusion"},{"id":"2605.30325","title":"Veda: Scalable Video Diffusion via Distilled Sparse Attention","created":"2026-05-28","cited_by_count":1,"area":"diffusion"},{"id":"2606.12273","title":"Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models","created":"2026-06-10","cited_by_count":1,"area":"diffusion"},{"id":"2606.14732","title":"Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion","created":"2026-06-02","cited_by_count":1,"area":"diffusion"},{"id":"2607.03012","title":"HyperVAttention: Efficient Sparse Attention with Spatio-Temporal Clustering for Video Diffusion","created":"2026-07-03","cited_by_count":1,"area":"diffusion"},{"id":"2603.16077","title":"MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Scaling of Diffusion Language Models","created":"2026-03-17","cited_by_count":0,"area":"diffusion"},{"id":"2603.22241","title":"MemDLM: Memory-Enhanced DLM Training","created":"2026-03-23","cited_by_count":0,"area":"diffusion"},{"id":"2605.06169","title":"Mean Mode Screaming: Mean--Variance Split Residuals for 1000-Layer Diffusion Transformers","created":"2026-05-07","cited_by_count":0,"area":"diffusion"},{"id":"2605.20813","title":"PulseCol: Periodically Refreshed Column-Sparse Attention for Accelerating Diffusion Language Models","created":"2026-05-20","cited_by_count":0,"area":"diffusion"},{"id":"2606.06309","title":"RhymeFlow: Training-Free Acceleration for Video Generation with Asynchronous Denoising Flow Scheduling","created":"2026-06-04","cited_by_count":0,"area":"diffusion"},{"id":"2607.02968","title":"Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation","created":"2026-07-03","cited_by_count":0,"area":"diffusion"},{"id":"2608.03335","title":"SPADE: An Input-Adaptive Sparse Attention Engine for Fast Video Diffusion Models Inference","created":"2026-08-04","cited_by_count":0,"area":"diffusion"},{"id":"2608.04557","title":"VoxStruct3D: Structure-Leading Flow Matching for Voxel-Space 3D MRI Synthesis","created":"2026-08-05","cited_by_count":0,"area":"diffusion"}],"next_cursor":"100"}