{"total":17,"rows":[{"id":"2605.10938","title":"ELF: Embedded Language Flows","created":"2026-05-11","cited_by_count":45,"area":"diffusion"},{"id":"2512.09106","title":"Learning Unmasking Policies for Diffusion Language Models","created":"2025-12-09","cited_by_count":30,"area":"diffusion"},{"id":"2602.10314","title":"Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training","created":"2026-02-10","cited_by_count":10,"area":"diffusion"},{"id":"2603.01367","title":"DUEL: Exact Likelihood for Masked Diffusion via Deterministic Unmasking","created":"2026-03-02","cited_by_count":9,"area":"diffusion"},{"id":"2606.26493","title":"Nemotron-Labs-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context","created":"2026-06-25","cited_by_count":4,"area":"diffusion"},{"id":"2602.05305","title":"FlashBlock: Attention Caching for Efficient Long-Context Block Diffusion","created":"2026-02-05","cited_by_count":3,"area":"diffusion"},{"id":"2603.20216","title":"Locally Coherent Parallel Decoding in Diffusion Language Models","created":"2026-03-03","cited_by_count":3,"area":"diffusion"},{"id":"2606.19475","title":"Diffusion Language Models: An Experimental Analysis","created":"2026-06-17","cited_by_count":2,"area":"diffusion"},{"id":"2603.20210","title":"CRoCoDiL: Continuous and Robust Conditioned Diffusion for Language","created":"2026-03-02","cited_by_count":1,"area":"diffusion"},{"id":"2604.02340","title":"Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models","created":"2026-02-04","cited_by_count":1,"area":"diffusion"},{"id":"2607.12829","title":"Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques","created":"2026-07-14","cited_by_count":1,"area":"diffusion"},{"id":"2602.01807","title":"Sentence Curve Language Models","created":"2026-02-02","cited_by_count":0,"area":"diffusion"},{"id":"2607.24841","title":"Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising","created":"2026-07-24","cited_by_count":0,"area":"diffusion"},{"id":"2610.00894","title":"Clock Diffusion: Efficient Semi-Autoregressive Continuous Diffusion Language Models","created":"2026-10-01","cited_by_count":0,"area":"diffusion"},{"id":"2610.12879","title":"One-Step Text Generation by Seq-Drifting","created":"2026-10-09","cited_by_count":0,"area":"diffusion"},{"id":"2610.14162","title":"Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation","created":"2026-10-09","cited_by_count":0,"area":"diffusion"},{"id":"2610.17590","title":"Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models","created":"2026-10-13","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}