{"total":61,"rows":[{"id":"2509.26328","title":"Fast-dLLM v2: Efficient Block-Diffusion LLM","created":"2025-09-30","cited_by_count":139,"area":null},{"id":"2607.05722","title":"Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding","created":"2026-07-07","cited_by_count":24,"area":null},{"id":"2510.10481","title":"UltraLLaDA: Scaling the Context Length to 128K for Diffusion Large Language Models","created":"2025-10-12","cited_by_count":13,"area":null},{"id":"2601.15892","title":"Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model","created":"2026-01-22","cited_by_count":13,"area":null},{"id":"2510.03274","title":"Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models","created":"2025-09-27","cited_by_count":12,"area":null},{"id":"2606.24773","title":"Posterior Refinement: Fast Language Generation via Any-Order Flow Maps","created":"2026-06-23","cited_by_count":11,"area":null},{"id":"2509.22738","title":"Enabling Approximate Joint Sampling in Diffusion LMs","created":"2025-09-25","cited_by_count":10,"area":null},{"id":"2510.03680","title":"Rainbow Padding: Mitigating Early Termination in Instruction-Tuned Diffusion LLMs","created":"2025-10-04","cited_by_count":10,"area":null},{"id":"2510.11052","title":"Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States","created":"2025-10-13","cited_by_count":9,"area":null},{"id":"2602.11151","title":"Diffusion-Pretrained Dense and Contextual Embeddings","created":"2026-02-11","cited_by_count":9,"area":null},{"id":"2510.01274","title":"TraceDet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Models","created":"2025-09-30","cited_by_count":8,"area":null},{"id":"2603.10088","title":"ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping","created":"2026-03-10","cited_by_count":6,"area":null},{"id":"2604.18995","title":"$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction","created":"2026-04-21","cited_by_count":6,"area":null},{"id":"2606.01774","title":"FLARE: Diffusion for Hybrid Language Model","created":"2026-06-01","cited_by_count":6,"area":null},{"id":"2603.01776","title":"FreeAct: Freeing Activations for LLM Quantization","created":"2026-03-02","cited_by_count":4,"area":null},{"id":"2604.09450","title":"ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion","created":"2026-04-10","cited_by_count":4,"area":null},{"id":"2605.29716","title":"NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs","created":"2026-05-28","cited_by_count":4,"area":null},{"id":"2601.23278","title":"FOCUS: DLLMs Know How to Tame Their Compute Bound","created":"2026-01-30","cited_by_count":3,"area":null},{"id":"2602.11715","title":"DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels","created":"2026-02-12","cited_by_count":3,"area":null},{"id":"2603.18806","title":"dTRPO: Trajectory Reduction in Policy Optimization of Diffusion Large Language Models","created":"2026-03-19","cited_by_count":3,"area":null},{"id":"2605.08044","title":"Fast Byte Latent Transformer","created":"2026-05-08","cited_by_count":3,"area":null},{"id":"2510.09885","title":"Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs","created":"2025-10-10","cited_by_count":2,"area":null},{"id":"2512.07525","title":"Beyond Real: Imaginary Extension of Rotary Position Embeddings for Long-Context LLMs","created":"2025-12-08","cited_by_count":2,"area":null},{"id":"2601.06562","title":"Mosaic: Unlocking Long-Context Inference for Diffusion LLMs via Global Memory Planning and Dynamic Peak Taming","created":"2026-01-10","cited_by_count":2,"area":null},{"id":"2601.17868","title":"VidLaDA: Bidirectional Diffusion Large Language Models for Efficient Video Understanding","created":"2026-01-25","cited_by_count":2,"area":null},{"id":"2605.09291","title":"dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models","created":"2026-05-10","cited_by_count":2,"area":null},{"id":"2605.09536","title":"TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM","created":"2026-05-10","cited_by_count":2,"area":null},{"id":"2606.02955","title":"Fast-dLLM++: Fr\\'{e}chet Profile Decoding for Faster Diffusion LLM Inference","created":"2026-06-01","cited_by_count":2,"area":null},{"id":"2509.18164","title":"DSFT: Inspiring Diffusion Large Language Models to Comprehend Mathematical and Logical Patterns","created":"2025-09-17","cited_by_count":1,"area":null},{"id":"2601.04854","title":"Projected Autoregression: Autoregressive Language Generation in Continuous State Space","created":"2026-01-08","cited_by_count":1,"area":null},{"id":"2601.07347","title":"DiffER: Diffusion Entity-Relation Modeling for Reversal Curse in Diffusion Large Language Models","created":"2026-01-12","cited_by_count":1,"area":null},{"id":"2602.03496","title":"Lookahead Path Likelihood Optimization for Diffusion LLMs","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2602.16902","title":"LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs?","created":"2026-02-18","cited_by_count":1,"area":null},{"id":"2603.25711","title":"Seeing to Ground: Visual Attention for Hallucination-Resilient MDLLMs","created":"2026-03-26","cited_by_count":1,"area":null},{"id":"2604.26139","title":"HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models","created":"2026-04-28","cited_by_count":1,"area":null},{"id":"2605.16941","title":"Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers","created":"2026-05-16","cited_by_count":1,"area":null},{"id":"2605.25969","title":"Triplet-Block Diffusion RWKV","created":"2026-05-25","cited_by_count":1,"area":null},{"id":"2606.05728","title":"DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance","created":"2026-06-04","cited_by_count":1,"area":null},{"id":"2606.06547","title":"FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models","created":"2026-06-04","cited_by_count":1,"area":null},{"id":"2606.10820","title":"K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling","created":"2026-06-09","cited_by_count":1,"area":null},{"id":"2606.19349","title":"Where to Place the Query? Unveiling and Mitigating Positional Bias in In-Context Learning for Diffusion LLMs via Decoding Dynamics","created":"2026-04-26","cited_by_count":1,"area":null},{"id":"2607.13188","title":"Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes","created":"2026-07-14","cited_by_count":1,"area":null},{"id":"2609.20830","title":"Reviser: Writing and Editing Text with Autoregressive Cursor Actions","created":"2026-07-23","cited_by_count":1,"area":null},{"id":"2602.02178","title":"AR-MAP: Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models?","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2605.01373","title":"Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast","created":"2026-05-02","cited_by_count":0,"area":null},{"id":"2605.04215","title":"Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2605.11601","title":"DiffScore: Text Evaluation Beyond Autoregressive Likelihood","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2607.00208","title":"SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2608.04424","title":"Thinking with Anchors: Grounded and Efficient Document Reasoning","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.13681","title":"Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.26140","title":"Affix Cache for Diffusion Large Language Models","created":"2026-06-26","cited_by_count":0,"area":null},{"id":"2609.25131","title":"Entropy Can Flow, or It Can Guide. Be Entropy. LEDFlow: Introducing Entropy-guided Generation Order into Uniform Discrete Flow","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2609.31685","title":"What does FFN compression change downstream? Same-state causal restoration in diffusion language models","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.35860","title":"The Detectability Gap: Hidden Heterogeneity in Hallucination Detection Across Language Models","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2610.01739","title":"Fixed-point neural samplers on discrete spaces","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.06513","title":"SOL: Measuring Gaps between Text Distributions by Double Sliced Wasserstein Metrics","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.09092","title":"MaRK: Markov-adapted Recurrent Kernels for Dynamic Operator Conditioning in State Space Models","created":"2026-10-06","cited_by_count":0,"area":null},{"id":"2610.10990","title":"Omni-Diffusion-Distill: Few-Step Distillation of Unified Multimodal Diffusion Large Language Models","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.11284","title":"DynaTE: Accelerating Diffusion LLMs via Dynamic Token Execution","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.12545","title":"AutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.17948","title":"HaloBlock: Faster and Better Decoding for Diffusion Large Language Models","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}