{"total":12,"items":[{"citing_arxiv_id":"2606.27732","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation","primary_cat":"cs.IR","submitted_at":"2026-06-26T05:26:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"R2LM combines causal attention with a reverse Mamba SSM sidecar to supply right-side context in dLLMs, claiming 2.4x-12.9x throughput gains over bidirectional dLLMs and 1.9x-2.9x over AR baselines while matching or exceeding quality.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.30876","ref_index":62,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"dMoE: dLLMs with Learnable Block Experts","primary_cat":"cs.CL","submitted_at":"2026-05-29T06:03:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"dMoE aggregates token expert distributions to block level in dLLMs, cutting unique experts from 69.5 to 14.6, memory by 76-80%, and latency by 1.14-1.66x while retaining 99.11% performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14607","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ViMU: Benchmarking Video Metaphorical Understanding","primary_cat":"cs.CV","submitted_at":"2026-05-14T09:23:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"ViMU is the first benchmark for evaluating video models on metaphorical and subtextual understanding using hint-free questions grounded in multimodal evidence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11882","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment","primary_cat":"cs.AI","submitted_at":"2026-05-12T09:56:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FATE lets LLM agents self-evolve safer behaviors by generating and filtering repairs from their own failure trajectories using verifiers and Pareto optimization.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[44] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models.arXiv preprint arXiv:2210.03629, 2022. [45] Bo Yin, Qi Li, Runpeng Yu, and Xinchao Wang. Refinement provenance inference: Detecting llm-refined training prompts from model behavior.arXiv preprint arXiv:2601.01966, 2026. [46] Runpeng Yu, Qi Li, and Xinchao Wang. Discrete diffusion in large language and multimodal models: A survey.arXiv preprint arXiv:2506.13759, 2025. [47] Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. Star: Bootstrapping reasoning with reasoning.Advances in Neural Information Processing Systems, 35:15476-15488, 2022. 12 [48] Haiquan Zhao, Chenhan Yuan, Fei Huang, Xiaomeng Hu, Yichang Zhang, An Yang, Bowen Yu,"},{"citing_arxiv_id":"2605.09981","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation","primary_cat":"q-bio.BM","submitted_at":"2026-05-11T04:49:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Gilchrist, Johannes Söding, and Martin Steinegger. Fast and accurate protein structure search with foldseek. Nature biotechnology, 42(2):243-246, 2024. [28] Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces.Advances in neural information processing systems, 34:17981-17993, 2021. [29] Runpeng Yu, Qi Li, and Xinchao Wang. Discrete diffusion in large language and multimodal models: A survey, 2025.URL https://arxiv. org/abs/2506.13759. [30] Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al. Robust deep learning-based protein"},{"citing_arxiv_id":"2604.26985","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"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":7.0,"formal_verification":"none","one_line_summary":"SCMDM is a post-training self-conditioning adaptation for masked diffusion models that reduces generative perplexity by nearly 50% on OWT and improves performance on images, molecules, and genomics.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19349","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Where to Place the Query? Unveiling and Mitigating Positional Bias in In-Context Learning for Diffusion LLMs via Decoding Dynamics","primary_cat":"cs.CL","submitted_at":"2026-04-26T20:22:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Query position is a first-order variable in dLLM ICL whose variance matches semantic quality impact; mitigated via Average Confidence metric and training-free Auto-ICL routing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15750","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"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":"2604.08302","ref_index":95,"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":"[93] Qiuhua Yi, Xiangfan Chen, Chenwei Zhang, Zehai Zhou, Linan Zhu, and Xiangjie Kong. Diffusion models in text generation: a survey.PeerJ Computer Science, 10:e1905, 2024. [94] Zebin You, Shen Nie, Xiaolu Zhang, Jun Hu, Jun Zhou, Zhiwu Lu, Ji-Rong Wen, and Chongxuan Li. Llada-v: Large language diffusion models with visual instruction tuning.arXiv preprint arXiv:2505.16933, 2025. [95] Runpeng Yu, Qi Li, and Xinchao Wang. Discrete diffusion in large language and multimodal models: A survey.arXiv preprint arXiv:2506.13759, 2025. [96] Runpeng Yu, Xinyin Ma, and Xinchao Wang. Dimple: Discrete diffusion multimodal large language model with parallel decoding.arXiv preprint arXiv:2505.16990, 2025. [97] Lunbin Zeng, Jingfeng Yao, Bencheng Liao, Hongyuan Tao, Wenyu Liu, and Xinggang Wang."},{"citing_arxiv_id":"2603.25661","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance","primary_cat":"cs.RO","submitted_at":"2026-03-26T17:14:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Parameter differences from two training runs on a small task set are treated as auxiliary capability vectors that are merged into a pretrained VLA model, yielding auxiliary-task gains at the cost of ordinary supervised finetuning plus a simple regularization term.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.20706","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"NPU Design for Diffusion Language Model Inference","primary_cat":"cs.AR","submitted_at":"2026-01-28T15:37:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Introduces the first NPU accelerator for diffusion language models with dLLM-specific ISA, hardware execution model, BAOS KV quantization, and 7nm RTL synthesis.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.15745","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LLaDA2.0: Scaling Up Diffusion Language Models to 100B","primary_cat":"cs.LG","submitted_at":"2025-12-10T09:26:18+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LLaDA2.0 scales discrete diffusion language models to 100B parameters via systematic conversion from autoregressive models using a 3-phase WSD training scheme and releases open-source 16B and 100B MoE variants.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}