Search: “pretraining”

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study

arXiv:2605.09622v1 Announce Type: cross Abstract: Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often st

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Research

AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models

DGX agent

arXiv:2603.10126v2 Announce Type: replace-cross Abstract: We propose a standalone autoregressive (AR) Action Expert that generates actions as a continuous causal sequence while conditioning on refresh

researcharxiv-cs-ai

12 May 2026

Applications

CASISR: Circular Arbitrary-Scale Image Super-Resolution

arXiv:2605.08173v1 Announce Type: new Abstract: The generalization performance (GP) of deep learning-based arbitrary-scale image super-resolution (ASISR) methods is subject to limited training dataset

applicationsarxiv-cs-cv

CAST: Channel-Aware Spatial Transfer Learning with Pseudo-Image Radar for Sign Language Recognition

arXiv:2605.08663v1 Announce Type: new Abstract: We propose CAST, a dual-stream architecture that utilizes channel-aware spatial transfer learning for isolated sign language recognition addressing the

Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels

arXiv:2603.05495v2 Announce Type: replace Abstract: To scale optimization and simulation, prior work has explored training machine-learning surrogates that map problem parameters to solutions inexpens

CLEF: EEG Foundation Model for Learning Clinical Semantics

arXiv:2605.10817v1 Announce Type: new Abstract: Clinical EEG interpretation requires reasoning over full EEG sessions and integrating signal patterns with clinical context. Existing EEG foundation mod

ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes

arXiv:2605.09688v1 Announce Type: new Abstract: Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target

tutorialsarxiv-cs-cv

Cosine-Gated Adam-Decay: Drop-In Staleness-Aware Outer Optimization for Decoupled DiLoCo

arXiv:2605.09126v1 Announce Type: new Abstract: Asynchronous DiLoCo systems may receive pseudo-gradients computed several outer rounds earlier, yet the standard Nesterov outer optimizer does not expli

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models

arXiv:2605.08960v1 Announce Type: cross Abstract: Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substa

DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition

arXiv:2605.09604v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar provides privacy-preserving sensing and is valuable for human action recognition (HAR). Existing mmWave point cloud datas

safetyarxiv-cs-cv

Developing a foundation model for high-resolution remote sensing data of the Netherlands

arXiv:2605.10184v1 Announce Type: cross Abstract: We develop a foundation model using 1.2m high resolution satellite images of the Netherlands. By combining a Convolutional Neural Network and a Vision

tutorialsarxiv-cs-ai

Applications

Digital Image Forgery Detection Using Transfer Learning

arXiv:2605.08167v1 Announce Type: cross Abstract: The increasing availability of advanced image editing tools has led to a significant rise in manipulated digital content, posing serious challenges fo

applicationsarxiv-cs-ai

Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series

arXiv:2604.05064v2 Announce Type: replace-cross Abstract: Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typ

Efficient Estimation of Kernel Surrogate Models for Task Attribution

arXiv:2602.03783v2 Announce Type: replace-cross Abstract: Modern AI agents such as large language models are trained on diverse tasks -- translation, code generation, mathematical reasoning, and text

tutorialsarxiv-cs-ai

Failing Forward: Adaptive Failure-Informed Learning for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2605.08434v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a promising paradigm for scalable robotic manipulation, yet their reliance on success-only behavioral clonin

Filtering Memorization from Parameter-Space in Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2605.10439v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing diffusion models, enabling users to inject new visual concepts or styles t

Flag Varieties: A Geometric Framework for Deep Network Alignment

arXiv:2605.09861v1 Announce Type: cross Abstract: Alignment, the tendency of adjacent weight matrices in deep networks to develop compatible subspace orientations, underlies gradient flow, Neural Coll

safetyarxiv-cs-ai

Follow the Mean: Reference-Guided Flow Matching

arXiv:2605.10302v1 Announce Type: new Abstract: Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits

GridProbe: Posterior-Probing for Adaptive Test-Time Compute in Long-Video VLMs

arXiv:2605.10762v1 Announce Type: cross Abstract: Long-video understanding in VLMs is bottlenecked by a single monolithic forward pass over thousands of frames at quadratic attention cost. A common mi

HyperTransport: Amortized Conditioning of T2I Generative Models

arXiv:2605.08254v1 Announce Type: cross Abstract: As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can

Improving Generative Adversarial Networks with Self-Distillation

arXiv:2605.08577v1 Announce Type: new Abstract: In modern GANs, maintaining an Exponential Moving Average (EMA) of the generator's weights is a standard practice, as such an averaged model consistentl

tutorialsarxiv-cs-cv

Improving Human Image Animation via Semantic Representation Alignment

arXiv:2605.10523v1 Announce Type: new Abstract: The field of image-to-video generation has made remarkable progress. However, challenges such as human limb twisting and facial distortion persist, espe

safetyarxiv-cs-cv

Inductive Entity Representations from Text via Link Prediction

arXiv:2010.03496v4 Announce Type: replace-cross Abstract: Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and

tutorialsarxiv-cs-ai

Kernel-Gradient Drifting Models

arXiv:2605.10727v1 Announce Type: new Abstract: We propose kernel-gradient drifting, a one-step generative modeling framework that replaces the fixed Euclidean displacement direction in drifting model

Latent Geometry Beyond Search: Amortizing Planning in World Models

arXiv:2605.08732v1 Announce Type: cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these space

LegalCiteBench: Evaluating Citation Reliability in Legal Language Models

arXiv:2605.10186v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into legal drafting and research workflows, where incorrect citations or fabricated precedent

LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training

arXiv:2509.20786v3 Announce Type: replace Abstract: Training deep neural networks with noise and data heterogeneity is a major challenge. We introduce Lightweight Learnable Adaptive Weighting (LiLAW),

safetyarxiv-cs-lg

Measuring and Decomposing Mode Separation via the Canonical Diffusion

arXiv:2605.08777v1 Announce Type: cross Abstract: Mode separation, namely how sharply a distribution fragments into barrier-separated clusters, is a fundamental geometric property of densities, diffic

Mela: Test-Time Memory Consolidation based on Transformation Hypothesis

arXiv:2605.10537v1 Announce Type: new Abstract: Memory consolidation, the process by which transient experiences are transformed into stable, structured representations, is a foundational organizing p

researcharxiv-cs-cl

ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality

arXiv:2605.09479v1 Announce Type: cross Abstract: We study full-reference image quality assessment from a machine-centric perspective, where images are evaluated by how well they preserve information

MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration

arXiv:2603.28254v2 Announce Type: replace Abstract: Orthogonalized-update optimizers such as Muon improve training of matrix-valued parameters, but existing extensions typically either rescale updates

Navigating LLM Valley: From AdamW to Memory-Efficient and Matrix-Based Optimizers

arXiv:2605.09176v1 Announce Type: cross Abstract: Training large language models requires optimization algorithms that are not only statistically effective, but also computationally and memory efficie

safetyarxiv-cs-ai

Optimizer-Induced Mode Connectivity: From AdamW to Muon

arXiv:2605.09991v1 Announce Type: new Abstract: Mode connectivity has been widely studied, yet the role of the optimizer remains underexplored. We revisit it through optimizer-induced implicit regular

OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control

arXiv:2605.08516v1 Announce Type: new Abstract: Transparent decision-making is essential for traffic signal control (TSC) systems to earn public trust. However, traditional reinforcement learning-base

Hardware

PermuQuant: Lowering Per-Group Quantization Error by Reordering Channels for Diffusion Models

arXiv:2605.09503v1 Announce Type: new Abstract: Large-scale visual generative models have achieved remarkable performance. However, their high computational and memory costs make deployment challengin

hardwarearxiv-cs-cv

Phoenix-VL 1.5 Medium Technical Report

arXiv:2605.10391v1 Announce Type: cross Abstract: We introduce Phoenix-VL 1.5 Medium, a 123B-parameter natively multimodal and multilingual foundation model, adapted to regional languages and the Sing

ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation

arXiv:2605.08774v1 Announce Type: cross Abstract: Long-horizon robotic manipulation requires dense feedback that reflects how a task advances through its procedural stages, not merely whether the fina

safetyarxiv-cs-lg

Radar-Guided Polynomial Fitting for Metric Depth Estimation

arXiv:2503.17182v4 Announce Type: replace Abstract: We propose POLAR, a novel radar-guided depth estimation method that introduces polynomial fitting to efficiently transform scaleless depth predictio

Reducing Annotation Burden for Femoral Cartilage Segmentation in Knee MRI via Cross-Sequence Transfer Learning

arXiv:2605.09067v1 Announce Type: new Abstract: Purpose: To develop and evaluate cross-sequence transfer learning for automatic femoral cartilage segmentation, testing bidirectional transfer between d

Reinforcement learning for inverse structural design and rapid laser cutting of kirigami prototypes

arXiv:2605.08098v1 Announce Type: new Abstract: Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult b

safetyarxiv-cs-lg

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data

arXiv:2602.12606v2 Announce Type: replace Abstract: Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relat

Restoration-Aligned Generative Flow Models for Blind Motion Deblurring

arXiv:2605.08854v1 Announce Type: new Abstract: Generative flow models offer powerful priors learned from large-scale natural images, but directly adapting them to restoration tasks such as motion deb

Revis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models

arXiv:2602.11824v2 Announce Type: replace Abstract: Despite the advanced capabilities of Large Vision-Language Models (LVLMs), they frequently suffer from object hallucination. One reason is that visu

Applications

SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation

arXiv:2605.09613v1 Announce Type: cross Abstract: Robotic deployment in real-world environments depends on rich, domain-specific action data as much as on strong model architecture. General-purpose ro

applicationsarxiv-cs-cv

SDG-MoE: Signed Debate Graph Mixture-of-Experts

arXiv:2605.08322v1 Announce Type: cross Abstract: Sparse MoE models achieve a good balance between capacity and compute by routing each token to a small subset of experts. However, in most MoE archite

Sink vs. diagonal patterns as mechanisms for attention switch and oversmoothing prevention

arXiv:2605.08453v1 Announce Type: cross Abstract: This paper studies the role of sinks and diagonal patterns as attention switch and anti-oversmoothing mechanisms. We analyze geometric conditions unde

safetyarxiv-cs-ai

SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs

arXiv:2605.09449v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) have made remarkable progress in visual understanding and language-based reasoning, yet they lack a pers

Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse

arXiv:2601.11042v2 Announce Type: replace-cross Abstract: Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for para