{"total":22,"items":[{"citing_arxiv_id":"2605.13382","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"BlockVLA: Accelerating Autoregressive VLA via Block Diffusion Finetuning","primary_cat":"cs.RO","submitted_at":"2026-05-13T11:37:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"BlockVLA accelerates autoregressive VLA models by 3.3x using block diffusion finetuning, with faster training convergence and better early performance on long-horizon robotic tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13026","ref_index":3,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Understanding and Accelerating the Training of Masked Diffusion Language Models","primary_cat":"cs.LG","submitted_at":"2026-05-13T05:29:38+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Bell-shaped time sampling accelerates masked diffusion language model training by roughly 4x on LM1B by countering locality bias in language data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11726","ref_index":8,"ref_count":2,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models","primary_cat":"cs.LG","submitted_at":"2026-05-12T08:09:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Introduces Block-R1 benchmark, Block-R1-41K dataset, and a conflict score to handle domain-specific optimal block sizes in RL post-training of diffusion LLMs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11577","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"BitLM: Unlocking Multi-Token Language Generation with Bitwise Continuous Diffusion","primary_cat":"cs.CL","submitted_at":"2026-05-12T06:02:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BitLM replaces per-token softmax with bitwise continuous diffusion inside causal blocks to generate multiple tokens in parallel while preserving autoregressive structure.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10518","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Infinite Mask Diffusion for Few-Step Distillation","primary_cat":"cs.CL","submitted_at":"2026-05-11T13:07:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Infinite Mask Diffusion Models use stochastic infinite-state masks to overcome the factorization error lower bound in standard masked diffusion, achieving superior few-step performance on language tasks via distillation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10218","ref_index":62,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Relative Score Policy Optimization for Diffusion Language Models","primary_cat":"cs.CL","submitted_at":"2026-05-11T08:58:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"RSPO interprets reward advantages as targets for relative log-ratios in dLLMs, calibrating noisy estimates to stabilize RLVR training and achieve strong gains on planning tasks with competitive math reasoning performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10020","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation","primary_cat":"cs.LG","submitted_at":"2026-05-11T05:44:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TrajDLM applies block diffusion language models to discrete road-segment sequences with topology constraints to generate realistic trajectories up to 2.8 times faster than prior methods while supporting zero-shot transfer.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09536","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM","primary_cat":"cs.CL","submitted_at":"2026-05-10T13:38:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TAD improves the accuracy-parallelism trade-off in diffusion LLMs via temporal-aware self-distillation that applies hard labels to soon-to-be-decoded tokens and soft supervision to future tokens.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09397","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"BadDLM: Backdooring Diffusion Language Models with Diverse Targets","primary_cat":"cs.CR","submitted_at":"2026-05-10T07:50:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"BadDLM implants effective backdoors in diffusion language models across concept, attribute, alignment, and payload targets by exploiting denoising dynamics while preserving clean performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09291","ref_index":146,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models","primary_cat":"cs.LG","submitted_at":"2026-05-10T03:36:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"dFlowGRPO is a new rate-aware RL method for discrete flow models that outperforms prior GRPO approaches on image generation and matches continuous flow models while supporting broad probability paths.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10980","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection","primary_cat":"cs.LG","submitted_at":"2026-05-09T03:26:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"LEAP detects early-converging tokens in dLLMs via future context filtering and multi-sequence superposition, reducing average denoising steps by about 30% while maintaining accuracy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04647","ref_index":90,"ref_count":2,"confidence":0.9,"is_internal_anchor":true,"paper_title":"ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving","primary_cat":"cs.RO","submitted_at":"2026-05-06T08:52:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ReflectDrive-2 combines masked discrete diffusion with RL-aligned self-editing to generate and refine driving trajectories, reaching 91.0 PDMS on NAVSIM camera-only and 94.8 in best-of-6.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01373","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast","primary_cat":"cs.CL","submitted_at":"2026-05-02T10:46:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"FoCore uses self-contrast on early-converging high-density tokens to boost diffusion LLM quality on reasoning benchmarks while cutting decoding steps by over 2x.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26985","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Simple Self-Conditioning Adaptation for Masked Diffusion Models","primary_cat":"cs.LG","submitted_at":"2026-04-28T19:34:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SCMDM adapts trained masked diffusion models to condition denoising steps on their own prior clean predictions, cutting generative perplexity nearly in half on open-web text while improving discretized image, molecule, and genomic synthesis.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20796","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model","primary_cat":"cs.CV","submitted_at":"2026-04-22T17:20:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LLaDA2.0-Uni unifies multimodal understanding and generation inside one discrete diffusion large language model with a semantic tokenizer, MoE backbone, and diffusion decoder.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17068","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Stability-Weighted Decoding for Diffusion Language Models","primary_cat":"cs.CL","submitted_at":"2026-04-18T17:04:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Stability-Weighted Decoding improves diffusion LLM accuracy by modulating token scores with temporal stability from KL divergence between prediction steps.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15750","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference","primary_cat":"cs.LG","submitted_at":"2026-04-17T06:53:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DepCap accelerates diffusion LM inference up to 5.63x by using last-block influence for adaptive block boundaries and conflict-free token selection for parallel decoding, with negligible quality loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.09450","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion","primary_cat":"cs.LG","submitted_at":"2026-04-10T16:07:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ECHO is a one-step block diffusion VLM for chest X-ray reports that improves RaTE and SemScore by over 60% while delivering 8x faster inference than autoregressive baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.08302","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"DMax: Aggressive Parallel Decoding for dLLMs","primary_cat":"cs.LG","submitted_at":"2026-04-09T14:35:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DMax enables faster parallel decoding in diffusion language models by using on-policy training to recover from errors and soft embedding interpolations for iterative revision, boosting tokens per forward pass roughly 2-3x on benchmarks while preserving accuracy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06779","ref_index":7,"ref_count":2,"confidence":0.9,"is_internal_anchor":true,"paper_title":"VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion","primary_cat":"cs.AI","submitted_at":"2026-04-08T07:50:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VASR separates continuation and residual variance in reward-guided diffusion SMC, using optimal mass allocation and systematic resampling to achieve up to 26% better FID scores and faster runtimes than prior SMC and MCTS methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12522","ref_index":4,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Differences in Text Generated by Diffusion and Autoregressive Language Models","primary_cat":"cs.CL","submitted_at":"2026-04-04T17:30:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DLMs exhibit lower n-gram entropy, higher semantic coherence, and higher semantic diversity than ARMs, primarily due to bidirectional context and remasking decoding strategies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02560","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models","primary_cat":"cs.CL","submitted_at":"2026-04-02T22:21:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DEMASK adds a lightweight pairwise-dependency predictor to dLLMs and uses greedy selection to enable parallel unmasking whose total-variation error is provably bounded under sub-additivity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}