{"total":31,"items":[{"citing_arxiv_id":"2606.27094","ref_index":63,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Learning Climate Variability from Scarce Data with Diffusion Models: A Test Case for ENSO","primary_cat":"physics.ao-ph","submitted_at":"2026-06-25T14:31:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Diffusion models recover known ENSO variability structure from synthetic LIM data when given enough samples, but require pre-training on CMIP6 plus fine-tuning to match observations with the ~700 samples available in ERSSTv5.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10023","ref_index":112,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions","primary_cat":"astro-ph.CO","submitted_at":"2026-06-08T18:08:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00219","ref_index":185,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables","primary_cat":"astro-ph.CO","submitted_at":"2026-05-29T18:00:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"21cmEMUv3 emulates the cylindrical 21cm power spectrum via score-based diffusion and six other 21cmFAST observables via LSTM networks at sub-percent accuracy, then uses the emulator to infer a lower limit on soft-band X-ray luminosity from HERA data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27499","ref_index":58,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GenSBI: Generative Methods for Simulation-Based Inference in JAX","primary_cat":"cs.LG","submitted_at":"2026-05-26T17:59:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GenSBI delivers JAX-native implementations of generative SBI methods with transformer backbones and reports near-ideal calibration scores on standard benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22338","ref_index":33,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction","primary_cat":"cs.LG","submitted_at":"2026-05-21T11:24:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A generative solver separates data-driven prior learning from inference-time enforcement of conservation laws using martingale-regularized score matching and physics-informed sampling for stable field reconstruction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16520","ref_index":198,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing","primary_cat":"cs.LG","submitted_at":"2026-05-15T18:14:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15418","ref_index":152,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A General Differentiable Ray-Wave Framework for Hybrid Refractive-Diffractive System Modeling and Optimization","primary_cat":"physics.optics","submitted_at":"2026-05-14T21:04:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A plug-and-play differentiable model bridging ray and wave optics for hybrid systems that enables end-to-end optimization of planar and conformal diffractive elements.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11266","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"PG-3DGS: Optimizing 3D Gaussian Splatting to Satisfy Physics Objectives","primary_cat":"cs.CV","submitted_at":"2026-05-11T21:43:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PG-3DGS couples 3D Gaussian Splatting with differentiable physics so that optimized shapes satisfy both visual fidelity and physical objectives such as pouring and aerodynamic lift, with real-world 3D-printed validation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Salimans, Classifier-Free Diffusion Guidance, arXiv.org (2022).doi:10.48550/arxiv.2207.12598. 43 [28] J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, S. Ganguli, Deep Unsupervised Learning using Nonequilibrium Thermodynamics, arXiv:1503.03585 [cond-mat, q-bio, stat] (Nov. 2015).doi:10.48550/ arXiv.1503.03585. URLhttp://arxiv.org/abs/1503.03585 [29] Y. Song, S. Ermon, Generative Modeling by Estimating Gradients of the Data Distribution, arXiv:1907.05600 [cs, stat] (Oct. 2020). doi: 10.48550/arXiv.1907.05600. URLhttp://arxiv.org/abs/1907.05600 [30] F.-A. Croitoru, V. Hondru, R. T. Ionescu, M. Shah, Diffusion Models in Vision: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (9) (2023) 10850-10869, arXiv:2209."},{"citing_arxiv_id":"2605.06134","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Diffusion model for SU(N) gauge theories","primary_cat":"hep-lat","submitted_at":"2026-05-07T12:34:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Implicit score matching trains diffusion models that successfully sample SU(3) Wilson gauge configurations on lattices, with a Hamiltonian-dynamics corrector needed for strong coupling.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.02852","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Inferring Active Neural Circuits Using Diffusion Scores","primary_cat":"q-bio.NC","submitted_at":"2026-05-04T17:30:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SBTG recovers the Jacobian of the nonlinear transition map between brain states by multiplying cross-block scores from denoising models, enabling inference of lag-specific directed interactions in neural population data such as C. elegans calcium imaging.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00229","ref_index":155,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"A unified perspective on fine-tuning and sampling with diffusion and flow models","primary_cat":"stat.ML","submitted_at":"2026-04-30T21:06:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.24416","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Scaling Properties of Continuous Diffusion Spoken Language Models","primary_cat":"cs.CL","submitted_at":"2026-04-27T12:45:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Continuous diffusion spoken language models follow scaling laws for loss and phoneme divergence and generate emotive multi-speaker speech at 16B scale, though long-form coherence stays difficult.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10465","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Rethinking the Diffusion Model from a Langevin Perspective","primary_cat":"cs.LG","submitted_at":"2026-04-12T05:18:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Diffusion models are reorganized under a Langevin perspective that unifies ODE and SDE formulations and shows flow matching is equivalent to denoising under maximum likelihood.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06779","ref_index":2,"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":1,"top_context_role":"background","top_context_polarity":"background","context_text":"consistency models, and our method continues to rely on Tweedie's estimates, which can be noisy at 11 early timesteps. Additionally, we do not consider multi-objective alignment settings with competing reward functions. We leave these directions for future work. References [1] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models, 2020. URLhttps://arxiv.org/abs/2006.11239. [2] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution, 2020. URLhttps://arxiv.org/abs/1907.05600. [3] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023. URLhttps://arxiv."},{"citing_arxiv_id":"2604.02415","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Generative models on phase space","primary_cat":"hep-ph","submitted_at":"2026-04-02T18:00:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Generative diffusion and flow models are constructed to remain exactly on the Lorentz-invariant massless N-particle phase space manifold during sampling for particle physics applications.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"00482 [cs.LG]. [12] C.-H. Lai, Y. Song, D. Kim, Y. Mitsufuji, and S. Ermon, The principles of diffusion models, (2025), arXiv:2510.21890 [cs.LG]. [13] M. Leigh, D. Sengupta, G. Qu' etant, J. A. Raine, K. Zoch, and T. Golling, PC-JeDi: Diffusion for particle cloud generation in high energy physics, SciPost Phys.16, 018 (2024), arXiv:2303.05376 [hep-ph]. [14] V. Mikuni, B. Nachman, and M. Pettee, Fast point cloud generation with diffusion models in high energy physics, Phys. Rev. D108, 036025 (2023), arXiv:2304.01266 [hep-ph]. [15] A. Butter, N. Huetsch, S. P. Schweitzer, T. Plehn, P. Sorrenson, and J. Spinner, Jet Diffusion versus JetGPT - Modern Networks for the LHC, SciPost Phys.Core8, 026 (2023), arXiv:2305."},{"citing_arxiv_id":"2604.08580","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control","primary_cat":"math.OC","submitted_at":"2026-03-28T13:01:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Adjoint matching objectives derived from the Stochastic Maximum Principle have critical points satisfying HJB stationarity conditions for SOC problems with control-dependent drift and diffusion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.22564","ref_index":54,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data","primary_cat":"cs.LG","submitted_at":"2026-03-23T20:49:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MIOFlow 2.0 learns stochastic cellular trajectories from transcriptomics data via neural SDEs, unbalanced optimal transport for growth, and a joint latent space unifying gene expression with spatial features.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.13419","ref_index":55,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Diffusion Models Memorize in Training -- and Generalize in Inference","primary_cat":"cs.LG","submitted_at":"2026-03-12T21:02:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Diffusion models overfit denoising loss at intermediate noise but generalize in inference as model error smooths the flow field and sampling paths avoid memorized noisy training data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.23748","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios","primary_cat":"cs.LG","submitted_at":"2025-12-26T18:18:25+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.11077","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"A probabilistic framework for crystal structure denoising, phase classification, and order parameters","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-12-11T19:46:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A unified probabilistic model uses per-atom logits over crystal prototypes to denoise atomic configurations, classify phases, and derive order parameters from a single differentiable scalar field.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.26258","ref_index":52,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules","primary_cat":"physics.ao-ph","submitted_at":"2025-09-30T13:46:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EnScale emulates high-resolution regional climate model outputs from global circulation models for multiple variables using a two-step generative process with sparse local stochastic layers and energy score optimization, including a temporally consistent variant.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.20430","ref_index":220,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"pop-cosmos: Star formation over 12 Gyr from generative modelling of a deep infrared-selected galaxy catalogue","primary_cat":"astro-ph.GA","submitted_at":"2025-09-24T18:00:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A score-based diffusion generative model on deep infrared galaxy photometry yields a star formation rate density peaking at z=1.3 and shows distinct non-parametric star formation histories plus AGN activity peaking during the quenching transition of massive galaxies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.21432","ref_index":39,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Exploring the flavor structure of leptons via diffusion models","primary_cat":"hep-ph","submitted_at":"2025-03-27T12:17:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Applies diffusion models to generate 10,000 neutrino mass matrices consistent with oscillation parameters in a seesaw model, revealing non-trivial distributions in CP phases and 0νββ effective mass.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.05909","ref_index":52,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Towards a Universal Foundation Model for Protein Dynamics: A Multi-Chain Tree-Structured 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performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2501.16839","ref_index":42,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans","primary_cat":"cs.LG","submitted_at":"2025-01-28T10:28:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A mathematical review of flow matching techniques for generative models, showing characterizations via couplings, kernels, and processes, with application to inverse problems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2410.19956","ref_index":54,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Gravitational-Wave 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model to generate new ones from text, enabling fast multi-representation 3D asset creation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2105.05233","ref_index":59,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Diffusion Models Beat GANs on Image Synthesis","primary_cat":"cs.LG","submitted_at":"2021-05-11T17:50:24+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Diffusion models with architecture improvements and classifier guidance achieve superior FID scores to GANs on unconditional and conditional ImageNet image synthesis.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"2 Conditional Sampling for DDIM The above derivation for conditional sampling is only valid for the stochastic diffusion sampling process, and cannot be applied to deterministic sampling methods like DDIM [57]. To this end, we use a score-based conditioning trick adapted from Song et al. [60], which leverages the connection between diffusion models and score matching [ 59]. In particular, if we have a model ϵθ(xt) that predicts the noise added to a sample, then this can be used to derive a score function: ∇xt logpθ(xt) = − 1√1 − ¯αt ϵθ(xt) (11) 7 Figure 3: Samples from an unconditional diffusion model with classifier guidance to condition on the class \"Pembroke Welsh corgi\". Using classifier scale 1.0 (left; FID: 33."},{"citing_arxiv_id":"2010.02502","ref_index":20,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Denoising Diffusion Implicit Models","primary_cat":"cs.LG","submitted_at":"2020-10-06T06:15:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"DDIMs construct non-Markovian diffusion processes that share DDPM training objectives but allow much faster reverse sampling, demonstrated empirically at 10-50x wall-clock speedup.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1910.03771","ref_index":137,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","primary_cat":"cs.CL","submitted_at":"2019-10-09T03:23:22+00:00","verdict":"ACCEPT","verdict_confidence":"HIGH","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Hugging Face releases an open-source Python library that supplies a unified API and pretrained weights for major Transformer architectures used in natural language processing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}