{"total":15,"items":[{"citing_arxiv_id":"2607.05722","ref_index":63,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding","primary_cat":"cs.CL","submitted_at":"2026-07-07T01:09:54+00:00","verdict":"ACCEPT","verdict_confidence":"HIGH","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Joint AR–diffusion training yields one tri-mode LM that switches AR, diffusion, and self-speculation, beating open AR/diffusion models on accuracy and tokens-per-forward.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.16847","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens","primary_cat":"cs.CL","submitted_at":"2026-06-15T15:23:47+00:00","verdict":"CONDITIONAL","verdict_confidence":"HIGH","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ASRD decouples trusted anchor tokens from uncertain candidates via temporal consistency and steers revocable dLLM decoding in embedding space, improving accuracy and throughput without training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09159","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Unified Energy for Invariant and Independent Decoding in Diffusion Language Models","primary_cat":"cs.CL","submitted_at":"2026-06-08T07:50:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The paper introduces Uni-E, a unified energy for DLMs that accounts for model capacity, dependency and invariance, can be computed exactly, and corrects distribution shifts from dependency and invariance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.08411","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding","primary_cat":"cs.CL","submitted_at":"2026-06-07T02:24:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"AsyncLane decouples refinement from advancement in DLM decoding via lane forking at delimiters plus efficiency optimizations, yielding up to 3x throughput gains on math and code benchmarks without retraining.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02955","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast-dLLM++: Fr\\'{e}chet Profile Decoding for Faster Diffusion LLM Inference","primary_cat":"cs.CL","submitted_at":"2026-06-01T23:18:59+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Fast-dLLM++ generalizes Fast-dLLM decoding to heterogeneous confidence profiles via Fréchet profile selection, delivering up to 37% throughput gains on GSM8K, MATH, HumanEval, and MBPP with LLaDA-8B.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.30753","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation","primary_cat":"cs.CL","submitted_at":"2026-05-29T02:29:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces TSPD with a trajectory-feature controller and training-free CE to reduce denoising steps in dLLMs while aiming to preserve quality.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.26106","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Looped Diffusion Language Models","primary_cat":"cs.LG","submitted_at":"2026-05-25T17:58:24+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LoopMDM loops early-middle layers in masked diffusion models to match same-size MDM performance with up to 3.3x fewer training FLOPs and outperform on reasoning tasks by up to 8.5 points on GSM8K.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25820","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models","primary_cat":"cs.LG","submitted_at":"2026-05-25T13:16:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VRCD prioritizes visually complementary positions during parallel decoding in dMLLMs by measuring attention overlap with the new Visual Redundancy Index, yielding accuracy gains over confidence-based baselines on M^3CoT and MMBench.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19470","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Drifting Objectives for Refining Discrete Diffusion Language Models","primary_cat":"cs.CL","submitted_at":"2026-05-19T07:22:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TokenDrift refines discrete diffusion language models by applying anti-symmetric drifting to soft-token features during training, yielding large reductions in generation perplexity at low NFEs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16941","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers","primary_cat":"cs.CL","submitted_at":"2026-05-16T11:27:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Diffusion LLMs can act as their own efficiency teachers by using revokable parallel decoding to identify reliable token orders and then distilling those orders into the model parameters for faster inference.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13999","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Support Before Frequency in Discrete Diffusion","primary_cat":"cs.LG","submitted_at":"2026-05-13T18:08:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Discrete diffusion models learn data support before frequencies because the exact reverse process decomposes edits into a dominant validity scale and a finer probability coefficient.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.16514","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation","primary_cat":"cs.CV","submitted_at":"2026-04-15T09:17:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Progressive supervised block merging plus stage-wise intra-diffusion distillation converts Qwen3-VL into a large-block dVLM with claimed SOTA open-dVLM scores and up to 3× decoding speedup on ≤4.4M data.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[7] Shansan Gong, Shivam Agarwal, Yizhe Zhang, Jiacheng Ye, Lin Zheng, Mukai Li, Chenxin An, Peilin Zhao, Wei Bi, Jiawei Han, et al. Scaling diffusion language mod- els via adaptation from autoregressive models.arXiv preprint arXiv:2410.17891, 2024. [8] Haoyu He, Katrin Renz, Yong Cao, and Andreas Geiger. Mdpo: Overcoming the training-inference divide of masked diffusion language models.arXiv preprint arXiv:2508.13148, 2025. [9] Daniel Israel, Guy Van den Broeck, and Aditya Grover. Accelerating diffusion llms via adaptive parallel decoding.arXiv preprint arXiv:2506.00413, 2025. [10] Yatai Ji, Teng Wang, Yuying Ge, Zhiheng Liu, Sidi Yang, Ying Shan, and Ping Luo. From denoising to refining: A corrective framework for vision-language diffusion model.arXiv preprint arXiv:2510."},{"citing_arxiv_id":"2604.08302","ref_index":37,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DMax: Aggressive Parallel Decoding for dLLMs","primary_cat":"cs.LG","submitted_at":"2026-04-09T14:35:42+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DMax uses On-Policy Uniform Training and Soft Parallel Decoding to enable aggressive parallelism in dLLMs, raising TPF on GSM8K from 2.04 to 5.47 and on MBPP from 2.71 to 5.86 while preserving accuracy.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"∗Correspoding Author Preprint. Under review. arXiv:2604.08302v2 [cs.LG] 20 Apr 2026 Despite this promise, the practical decoding parallelism of existing dLLMs [58, 109, 92, 17, 10, 90] remains limited, as their performance drops sharply under aggressive parallel decoding. Some prior work has attempted to improve this trade-off through improved decoding [37, 81, 41, 27, 8, 32, 34, 89] or distillation strategies [16, 62, 101, 38]. Nevertheless, these methods do not address the fundamental bottleneck underlying parallel decoding in current dLLM paradigm: error accumulation. In current mask-based dLLMs, decoding is a binary, one-way mask-to-token process. Once a masked position is decoded into a token, that token is fixed and propagated as context to subsequent decoding"},{"citing_arxiv_id":"2512.14067","ref_index":43,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed","primary_cat":"cs.CL","submitted_at":"2025-12-16T04:12:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Efficient-DLM converts AR models to dLMs via block-wise causal attention and position-dependent masking, yielding higher accuracy and 2.7-4.5x throughput than Dream 7B and Qwen3 4B.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.19982","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Diffusion Language Models Know the Answer Before Decoding","primary_cat":"cs.CL","submitted_at":"2025-08-27T15:40:25+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DLMs show early answer convergence allowing Prophet to cut decoding steps by up to 3.4x on LLaDA-8B and Dream-7B while keeping output quality.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}