{"total":16,"items":[{"citing_arxiv_id":"2605.22586","ref_index":22,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models","primary_cat":"cs.LG","submitted_at":"2026-05-21T14:59:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":1.0,"formal_verification":"none","one_line_summary":"A tutorial that unifies diffusion probabilistic models, score-based generative modeling, and SDE methods by deriving forward and reverse dynamics from a shared Gaussian noising process.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22507","ref_index":53,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Generative Modeling by Value-Driven Transport","primary_cat":"cs.LG","submitted_at":"2026-05-21T13:57:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A control-theoretic linear program yields value-driven transport policies for generative modeling with straight paths and simulation-free training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19119","ref_index":82,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization","primary_cat":"cs.NE","submitted_at":"2026-05-18T21:11:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12573","ref_index":11,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration","primary_cat":"cs.CV","submitted_at":"2026-05-12T11:38:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LAMP adds a lagged temporal correction derived from second-order discretization to diffusion posterior samplers, yielding consistent gains over DiffPIR and DDRM on imaging tasks via a bias-variance trade-off.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10916","ref_index":18,"ref_count":2,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Confidence-Guided Diffusion Augmentation for Enhanced Bangla Compound Character Recognition","primary_cat":"cs.CV","submitted_at":"2026-05-11T17:51:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A confidence-guided diffusion framework generates synthetic Bangla compound characters that, when filtered and added to training data, raise classifier accuracy to 89.2% on the AIBangla dataset.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09319","ref_index":9,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection","primary_cat":"cs.CV","submitted_at":"2026-05-10T04:32:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PGID restores watermark detection in diffusion models by using progressive inversion-denoising cycles to correct latents displaced by removal or forgery attacks.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"backward through diffusion inversion to retrieve an estimate of the initial noise. This recovered noise is subsequently decoded for watermark detection. Even though different semantic watermark schemes have different ways of injecting and decoding the embedded signals, they all share the same dependence on an accurate inversion function for initial noise retrieval, which is typically DDIM [9]. Exploiting this dependency, Müller et al. (2025)[ 8] have demonstrated the vulnerability of this watermarking paradigm to black-box imprint forgery and removal attacks. Consequently, attackers can either falsely attribute illicit content to service ∗Corresponding author. Preprint. arXiv:2605.09319v1 [cs.CV] 10 May 2026 Current methods: Removal-"},{"citing_arxiv_id":"2605.06376","ref_index":48,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Continuous-Time Distribution Matching for Few-Step Diffusion Distillation","primary_cat":"cs.CV","submitted_at":"2026-05-07T14:56:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"CDM migrates distribution matching distillation to continuous time via dynamic random-length schedules and active off-trajectory latent alignment, yielding competitive few-step image fidelity on SD3 and Longcat-Image.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06355","ref_index":31,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Order-Agnostic Autoregressive Modelling with Missing 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