diffusion-models

topic25 events

papersTODAY 04:00 UTC

Diffusion Model Approach Targets Fuel-Optimal Spacecraft Trajectories

A new arXiv preprint proposes a diffusion-based multiple-shooting method for indirect optimal control, aimed at generating fuel-efficient spacecraft trajectories. The authors argue that prior diffusion-based control work has largely neglected optimality guarantees, which their approach seeks to address. The work sits at the intersection of generative modeling, robotics-style control, and aerospace trajectory design.

diffusion-models/robotics/control/research

papersTODAY 04:00 UTC

RAIN Model Extracts Semantic Watermarks in One Step

Researchers propose RAIN, a region-aware inversion network that recovers the initial noise used to embed semantic watermarks in diffusion models. The method aims to avoid the multi-step diffusion inversion that current Gaussian-Shading extraction typically requires, while leaving image quality largely intact. It is described in a cross-listed arXiv paper.

watermarking/diffusion-models/research/image-generation

papersTODAY 04:00 UTC

Diffusion Model Imputes Missing Values in Mixed Numerical and Categorical Data

A new arXiv paper introduces Impute-EM, a diffusion-based approach for filling in missing values in datasets that mix numerical, categorical, and binary variables. Most existing diffusion imputation methods convert discrete variables into continuous stand-ins, which the authors argue is a limitation the native mixed-state design avoids. The work targets heterogeneous data mining settings where such mixed variable types commonly appear together.

diffusion-models/data-imputation/missing-data/research

papersTODAY 04:00 UTC

ReCAST: Reward Credit Assignment Across Timesteps for Online Diffusion RL

A new arXiv paper introduces ReCAST, a method for assigning credit to individual timesteps when fine-tuning diffusion models with reinforcement learning from multiple reward signals. The work separates how much each reward should influence training, based on user preference, from how informative that reward actually is at a given point in the generation process. This distinction is used to drive online diffusion RL more effectively.

diffusion-models/reinforcement-learning/reward-modeling/arxiv

papersTODAY 04:00 UTC

Abstract-LoRA Method Targets U-Net Blocks for Single-Image Style Transfer

A new arXiv paper introduces Abstract-LoRA, a technique that adapts diffusion models for style transfer using only a single reference image. Rather than fine-tuning the whole network, the approach concentrates training on selected U-Net blocks, which the authors present as a way to bypass the data demands of existing multi-image style transfer pipelines. The work is a preprint and has not yet been peer reviewed.

diffusion-models/style-transfer/lora/research

papersTODAY 04:00 UTC

Sparse Matrix-Decomposition Init Method Targets Flow-Matching Fine-Tuning Costs

A new arXiv preprint proposes a restricted initialization scheme for flow-matching diffusion models based on sparse matrix decomposition, aimed at reducing the cost of adapting these models to downstream tasks. The work builds on the observation that fine-tuning flow-matching models is expensive, and that low-rank adaptation combined with timestep-aware choices may help. The abstract suggests the method constrains initialization at a principal timestep to improve training efficiency.

flow-matching/diffusion-models/fine-tuning/lora

papersTODAY 04:00 UTC

Diffusion Model Generates Patient-Specific Gait Trajectories for Exoskeletons

A new arXiv paper proposes using diffusion models to synthesize musculoskeletal gait trajectories tailored to individual patient parameters, a step toward better reference signals for wearable assistive devices. The authors frame this as a challenge for lower-limb exoskeletons and rehabilitation robotics, where control depends on trajectories matched to each user. The abstract is preliminary and reports no experimental results yet.

diffusion-models/robotics/rehabilitation/gait

papersTODAY 04:00 UTC

Backward SDE Approach Targets Physics Consistency in Diffusion Models

A new arXiv preprint proposes using backward stochastic differential equations to enforce physics and measurement consistency when score-based diffusion models are applied to inverse problems. The authors argue that existing methods, such as heuristic guidance, periodic projections, or task-specific conditional training, are less principled. The work positions the approach as a general way to keep pretrained diffusion priors aligned with physical constraints.

diffusion/research/physics/generative-models

papersTODAY 04:00 UTC

Diffusion Models Applied to Spatiotemporal Influenza Forecasting

A new arXiv paper explores using generative diffusion models to forecast influenza incidence across space and time. The authors argue that existing mechanistic and statistical methods often fail to capture complex epidemic dynamics, and propose a generative approach as an alternative. The work targets public health planning, where better short-term forecasts can inform preparedness decisions.

diffusion-models/forecasting/public-health/research

papersTODAY 04:00 UTC

Review and Tutorial on Ergodic Control and Controlled Diffusion for Robot Learning

A new arXiv paper surveys how diffusion-based learning methods can be applied to robot learning, framing the problem through ergodic control and controlled diffusion. It is written as a combined review and tutorial, intended to give researchers a structured entry point to the underlying statistical machinery. The work is cross-listed in machine learning and focuses on deriving complex distributions from data for control tasks.

robotics/diffusion-models/control/tutorial

papersTODAY 04:00 UTC

Dynamic Semantic Steering Approach Targets Concept Erasure in Diffusion Models

A new arXiv paper proposes DSS, a method for removing unwanted concepts from text-to-image diffusion models. The approach steers the model's internal semantic representations dynamically rather than relying on static interventions, aiming to make erasure more robust against adversarial prompts. The work targets safety concerns such as generated NSFW material or copyrighted content.

diffusion-models/safety/concept-erasure/research

papersTODAY 04:00 UTC

TIDAL: Interleaved Diffusion and Action Loop for High-Frequency VLA Control

A new arXiv paper proposes TIDAL, a control scheme that alternates between diffusion-based planning and action execution to keep vision-language-action models running at high frequency. The authors argue that current VLA systems rely on a low-frequency batch-and-execute approach, and that the resulting mismatch between model inference speed and robot control rate creates gaps in which the agent cannot react. TIDAL aims to close that blind spot while retaining the semantic generalization of large VLA models.

robotics/vla/diffusion/inference-latency

papersTODAY 04:00 UTC

AdaFlash: Adaptive Speculative Decoding with On-Policy Distilled Diffusion Drafters

A new arXiv paper proposes AdaFlash, a speculative decoding method that uses diffusion-based draft models distilled on-policy to speed up large language model inference. The approach adapts the drafting process rather than relying on a fixed draft model, aiming to improve acceptance rates during verification by the target model. It builds on prior work in this line, including DFlash, and appears as a revised submission.

speculative-decoding/diffusion/inference/research

papersSEP 12 04:00 UTC

ScaleResfusion combines residual rectified flow with residual vector fields for image restoration

A new arXiv paper introduces ScaleResfusion, a method that uses a residual vector field to guide residual rectified flow for real-world image restoration. The approach targets recovering high-quality images from complex, unknown degradations, a setting where diffusion-based methods have improved perceptual quality but still face remaining obstacles. The abstract excerpt available does not detail the full experimental results.

image-restoration/diffusion/research/generative-models

papersSEP 12 04:00 UTC

Information-Theoretic Framework Unifies Generalization Bounds for VAEs and Diffusion Models

A new arXiv paper derives generalization guarantees for both variational autoencoders and diffusion models within a single information-theoretic framework. The analysis exploits the encoder-generator structure shared by the two model families, which earlier theoretical work had largely treated separately. The authors report bounds that clarify how the shared architecture affects performance on unseen data.

research/diffusion/generative-models/theory

papersSEP 12 04:00 UTC

Study Examines LoRA Rank Trade-offs for Diffusion Model Fine-Tuning

A new arXiv paper reports a controlled experiment on CIFAR-10 using a DDPM U-Net to measure how LoRA rank affects fine-tuning quality and compute cost. The authors tested ranks of 2, 4, 8, 16, and 32 under fixed optimization settings and evaluated results with PyTorch-FID in a reproducible setup. The work aims to give practitioners clearer guidance on choosing a rank that balances output quality against training expense.

lora/diffusion-models/fine-tuning/benchmarks

papersSEP 12 04:00 UTC

Model-Aware Diffusion Schedules Derived via Optimal Transport

A new arXiv paper argues that the schedules controlling how signal and noise are mixed along diffusion and flow-matching paths can be optimized by minimizing a kinetic action borrowed from optimal transport theory. The authors show that making these schedules depend on the specific model, rather than using fixed hand-tuned coefficients, improves generation quality. The work offers a theoretical framing for why certain noise schedules perform better than others.

diffusion/optimal-transport/generative-models/arxiv

papersSEP 11 04:00 UTC

Paper Proposes Theoretical Framework for Memorization in Diffusion Models

A new arXiv preprint develops a theoretical account of why diffusion models sometimes reproduce training data verbatim rather than generating novel samples. The authors propose smoothing the score function as a way to reduce this memorization effect and improve generalization. The work is presented as an explanation of the phenomenon rather than a new model release.

diffusion-models/memorization/theory/research

papersSEP 11 04:00 UTC

Prediction-Loss Alignment for Sampler-Robust Flow Matching Training

The paper looks at a widely used training recipe for diffusion and flow-matching models, where the network predicts a clean sample that is then converted into a velocity for the loss. That conversion amplifies errors near the endpoints of the noise schedule, making training unstable and tied to a particular sampler. The authors propose aligning the prediction objective with the loss objective so that training stays robust regardless of which sampler is used at inference.

flow-matching/diffusion/training/arxiv

papersSEP 11 04:00 UTC

Study Argues Continuous Diffusion Can Scale Competitively for Language Modeling

A new arXiv paper revisits Plaid, a likelihood-based continuous diffusion model for text, to test the assumption that continuous diffusion scales worse than discrete alternatives. The authors report that with the right design, continuous diffusion can match discrete diffusion at scale. The work is a replacement version of a cross-listed machine learning preprint.

diffusion/language-models/scaling/research

papersSEP 11 04:00 UTC

Adaptive Diffusion Freezing proposed against membership inference attacks

A new arXiv preprint introduces a technique called Adaptive Diffusion Freezing that aims to make diffusion models more resistant to membership inference attacks, which try to determine whether a specific sample was part of the training data. The method adapts how parts of the model are frozen during training to limit the privacy leakage that standard diffusion training can expose.

privacy/diffusion-models/membership-inference/research

papersSEP 10 04:00 UTC

Causal Abstraction Method Reduces Cost of Fairness Auditing in Diffusion Models

A new arXiv paper proposes an auditing instrument built on causal abstraction to assess fairness in text-to-image diffusion models. The approach aims to avoid the heavy computation normally required when generating many images across different sampling configurations. It targets making comprehensive fairness evaluations more practical for these models.

fairness/diffusion-models/auditing/research

papersSEP 10 04:00 UTC

Researchers propose manifold-aligned generative transport for low-dimensional data structures

A machine learning preprint on arXiv introduces a generative transport method aimed at datasets that concentrate near a low-dimensional structure embedded in a high-dimensional space. The approach seeks to limit probability mass leaking away from the data-supporting manifold while staying computationally practical, in contrast to the iterative sampling used by diffusion models. The paper was updated as a v2 cross-list replacement.

generative-models/diffusion/manifolds/research

papersSEP 10 04:00 UTC

Conditional diffusion model produces high-resolution temperature maps from sparse station data

Researchers have introduced a conditional diffusion framework that refines coarse ERA5 reanalysis fields into fine-scale air temperature estimates by incorporating sparse ground-station measurements. The approach is designed to capture terrain and land-surface contrasts that coarse products miss, with the goal of improving local heatwave hazard assessment in areas with limited sensor coverage.

diffusion-models/climate/downscaling/research

papersSEP 10 04:00 UTC

FlowCPO paper unifies online RL and offline preference alignment for flow and diffusion models

A new arXiv preprint presents FlowCPO, a framework that interprets preference alignment for flow and diffusion models through a single divergence-based lens. The work connects online reinforcement learning techniques with offline preference optimization, approaches that had previously been studied in isolation. The authors also examine limitations of existing forward-process alignment methods within this unified view.

flow-models/diffusion/preference-alignment/reinforcement-learning