{"total":12,"items":[{"citing_arxiv_id":"2606.03393","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Flicker-DDPM: Accelerating Denoising Diffusion via 1/f Colored Noise Injection","primary_cat":"cs.LG","submitted_at":"2026-06-02T09:36:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Flicker-DDPM accelerates DDPM sampling by injecting 1/f colored noise matched to image spectra, achieving similar quality with 3.33 times fewer steps on CIFAR-10.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27919","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Frequency-Guided Action Diffusion via Sub-Frequency Manifold Traversal","primary_cat":"cs.RO","submitted_at":"2026-05-27T03:45:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FGO guides diffusion policy generation via expanding spectral bands on sub-frequency manifolds to improve action smoothness on 15 robotic manipulation tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.26032","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution","primary_cat":"cs.CV","submitted_at":"2026-05-25T17:01:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SKILD unifies unconditional image generation and continuous super-resolution in one diffusion model via scale-invariant k-space dynamics where the reverse process handles both tasks by varying only the starting timestep.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.24509","ref_index":41,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"{\\Phi}-Noise: Training-Free Temporal Video Conditioning via Phase-Based Noise Manipulation","primary_cat":"cs.CV","submitted_at":"2026-05-23T10:43:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Training-free motion conditioning for latent video diffusion by direct injection of low-frequency phase from a reference video into the diffusion noise.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18190","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network","primary_cat":"cs.LG","submitted_at":"2026-05-18T10:30:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Dual-Rate Diffusion interleaves sparse heavy context encoding with frequent light denoising to cut diffusion sampling cost by 2-4x on ImageNet while matching baseline quality and remaining compatible with distillation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13910","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Covariance-aware sampling for Diffusion Models","primary_cat":"stat.ML","submitted_at":"2026-05-13T07:46:06+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A covariance-aware extension of DDIM sampling for pixel-space diffusion models that uses Tweedie's formula and Fourier decomposition to model reverse-process covariance and improves sample quality at low NFE.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11506","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Principled Design of Diffusion-based Optimizers for Inverse Problems","primary_cat":"cs.CV","submitted_at":"2026-05-12T04:25:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Reparameterizations create invariances in diffusion inverse-problem solvers, enabling hyperparameter reuse and accelerated inference via the OptDiff optimization framework.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"∥∇f k∥2 ∥∇gk∥2 , r k = ∥∇f k∥2 ∥∇gk∥2 . Simultaneous descent (Fliege & Svaiter, 2000) requires ⟨∇f k, dk⟩>0and⟨∇g k, dk⟩>0. We compute both inner products in terms ofcosϑ k,r k, andλ k. First, ⟨∇f k, dk⟩=∥∇f k∥2 2 +λ k⟨∇f k,∇g k⟩(47) =∥∇f k∥2 2 +λ k∥∇f k∥2∥∇gk∥2 cosϑ k (48) =∥∇f k∥2 2 1 +λ k cosϑ k rk \u0001 .(49) Similarly, ⟨∇gk, dk⟩=⟨∇g k,∇f k⟩+λ k∥∇gk∥2 2 (50) =∥∇g k∥2 2 rk cosϑ k +λ k\u0001 .(51) Since both norm-squared factors are strictly positive, the signs of (49)-(51) are determined by 1 +λ k cosϑ k rk andr k cosϑ k +λ k. We now distinguish two geometric regimes. Case 1: cosϑ k ≥0 .The gradients form an acute angle. Since λk >0 by construction, and noting that cosϑ k ≥0 , rk >0, we obtain 1 +λ k cosϑ k"},{"citing_arxiv_id":"2605.07915","ref_index":68,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion","primary_cat":"cs.CV","submitted_at":"2026-05-08T15:52:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Prior-Aligned AutoEncoders shape latent manifolds with spatial coherence, local continuity, and global semantics to improve latent diffusion, achieving SOTA gFID 1.03 on ImageNet 256x256 with up to 13x faster convergence.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"Generative modelling with inverse heat dissipation, 2023. URLhttps://arxiv.org/abs/2206.13397. [67] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. [68] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs.arXiv preprint arXiv:2111.02114, 2021. [69] Noam Shazeer. Glu variants improve transformer.arXiv preprint arXiv:2002.05202, 2020."},{"citing_arxiv_id":"2507.16344","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Diff-ANO: Towards Fast High-Resolution Ultrasound Computed Tomography via Conditional Consistency Models and Adjoint Neural Operators","primary_cat":"math.NA","submitted_at":"2025-07-22T08:24:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Diff-ANO uses conditional consistency models and adjoint neural operator surrogates to enable fast, high-quality USCT reconstructions under sparse and partial views by replacing slow PDE solvers and enabling few-step sampling.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.16827","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Beyond Blur: A Fluid Perspective on Generative Diffusion Models","primary_cat":"cs.GR","submitted_at":"2025-06-20T08:31:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proposes an advection-diffusion PDE corruption process with stochastic velocity fields and Lattice Boltzmann solver for diffusion models, generalizing prior PDE methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.05472","ref_index":68,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation","primary_cat":"cs.CV","submitted_at":"2025-05-08T17:58:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Mogao presents a causal unified model with deep fusion, dual encoders, and interleaved position embeddings that achieves strong performance on multi-modal understanding, text-to-image generation, and coherent interleaved outputs including zero-shot editing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.03206","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models","primary_cat":"cs.LG","submitted_at":"2025-03-05T05:50:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Analytic solution of full-batch gradient flow for linear and convolutional denoisers in diffusion models yields a universal inverse-variance spectral law for learning times of eigenmodes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}