Search: “research”

DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift

arXiv:2605.11237v1 Announce Type: new Abstract: Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-

Deep Learning for Protein Complex Prediction and Design

DGX agent

arXiv:2605.11189v1 Announce Type: new Abstract: Accurately modeling and designing protein complex structures is a central problem in computational structural biology, with broad implications for under

researcharxiv-cs-lg

13 May 2026

Deep Probabilistic Unfolding for Quantized Compressive Sensing

arXiv:2605.11475v1 Announce Type: new Abstract: We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to

Demystifying When Pruning Works via Representation Hierarchies

arXiv:2603.24652v3 Announce Type: replace Abstract: Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. How

Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models

arXiv:2605.12138v1 Announce Type: cross Abstract: Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models drive

Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory

arXiv:2605.12394v1 Announce Type: new Abstract: Training Neural Networks (NNs) without overfitting is difficult; detecting that overfitting is difficult as well. We present a novel Random Matrix Theor

Diabetic Retinopathy Classification using Downscaling Algorithms and Deep Learning

arXiv:2605.11430v1 Announce Type: new Abstract: Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with clas

Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)

arXiv:2510.06719v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding them in external knowledge. However, its application i

DiffSegLung: Diffusion Radiomic Distillation for Unsupervised Lung Pathology Segmentation

arXiv:2605.11758v1 Announce Type: cross Abstract: Unsupervised segmentation of pulmonary pathologies in CT remains an open challenge due to the absence of annotated multi pathology cohorts and the fai

DIPSER: A Dataset for In-Person Student Engagement Recognition in the Wild

arXiv:2502.20209v3 Announce Type: replace Abstract: In this paper, a novel dataset is introduced, designed to assess student attention within in-person classroom settings. This dataset encompasses RGB

Dirichlet process mixtures of block g priors for model selection and prediction in linear models

arXiv:2411.00471v3 Announce Type: replace-cross Abstract: This paper introduces Dirichlet process mixtures of block g priors for model selection and prediction in linear models. These priors are exten

Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning

arXiv:2605.12122v1 Announce Type: cross Abstract: Unlearning specific concepts in text-to-image diffusion models has become increasingly important for preventing undesirable content generation. Among

DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick

arXiv:2509.26469v3 Announce Type: replace Abstract: Vector quantization is common in deep models, yet its hard assignments block gradients and hinder end-to-end training. We propose DiVeQ, which treat

Do Language Models Encode Knowledge of Linguistic Constraint Violations?

arXiv:2605.12055v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong linguistic performance, yet their internal mechanisms for producing these predictions remain unclear. We inv

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

arXiv:2510.04265v4 Announce Type: replace-cross Abstract: Pass@k is widely used to report the reasoning performance of LLMs, but it often produces unstable and potentially misleading rankings, especia

DP-{lambda}CGD: Efficient Noise Correlation for Differentially Private Model Training

arXiv:2601.22334v2 Announce Type: replace Abstract: Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential priva

DriftXpress: Faster Drifting Models via Projected RKHS Fields

arXiv:2605.12183v1 Announce Type: new Abstract: Drifting Models have emerged as a new paradigm for one-step generative modeling, achieving strong image quality without iterative inference. The premise

DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT

arXiv:2506.17501v3 Announce Type: replace-cross Abstract: Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience

EDGER: EDge-Guided with HEatmap Refinement for Generalizable Image Forgery Localization

arXiv:2605.12002v1 Announce Type: new Abstract: Text-guided inpainting has made image forgery increasingly realistic, challenging both SID and IFL. However, existing methods often struggle to point ou

Efficient Adjoint Matching for Fine-tuning Diffusion Models

arXiv:2605.11480v1 Announce Type: new Abstract: Reward fine-tuning has become a common approach for aligning pretrained diffusion and flow models with human preferences in text-to-image generation. Am

Efficient Bayesian Inference from Noisy Pairwise Comparisons

arXiv:2510.09333v2 Announce Type: replace-cross Abstract: Evaluating generative models is challenging because standard metrics often fail to reflect human preferences. Human evaluations are more relia

EgoForce: Forearm-Guided Camera-Space 3D Hand Pose from a Monocular Egocentric Camera

arXiv:2605.12498v1 Announce Type: new Abstract: Reconstructing the absolute 3D pose and shape of the hands from the user's viewpoint using a single head-mounted camera is crucial for practical egocent

Elastic Attention Cores for Scalable Vision Transformers

arXiv:2605.12491v1 Announce Type: new Abstract: Vision Transformers (ViTs) achieve strong data-driven scaling by leveraging all-to-all self-attention. However, this flexibility incurs a computational

Elicitation-Augmented Bayesian Optimization

arXiv:2605.12079v1 Announce Type: new Abstract: Human-in-the-loop Bayesian optimization (HITL BO) methods utilize human expertise to improve the sample-efficiency of BO. Most HITL BO methods assume th

EndoVGGT: GNN-Enhanced Depth Estimation for Surgical 3D Reconstruction

arXiv:2603.24577v2 Announce Type: replace Abstract: Accurate 3D reconstruction of deformable soft tissues is essential for surgical robotic perception. However, low-texture surfaces, specular highligh

Enriching and Controlling Global Semantics for Text Summarization

arXiv:2109.10616v2 Announce Type: replace Abstract: Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Ne

EqOD: Symmetry-Informed Stability Selection for PDE Identification

arXiv:2605.11524v1 Announce Type: new Abstract: Data-driven identification of partial differential equations (PDEs) relies on sparse regression over a candidate library of differential operators, wher

Error whitening: Why Gauss-Newton outperforms Newton

arXiv:2605.11316v1 Announce Type: new Abstract: The Gauss-Newton matrix is widely viewed as a positive semidefinite approximation of the Hessian, yet mounting empirical evidence shows that Gauss-Newto

Evolutionary Task Discovery: Advancing Reasoning Frontiers via Skill Composition and Complexity Scaling

arXiv:2605.11666v1 Announce Type: new Abstract: The reasoning frontier of Large Language Models (LLMs) has advanced significantly through modern post-training paradigms (e.g., Reinforcement Learning f

Exploring Token-Space Manipulation in Latent Audio Tokenizers

arXiv:2605.11192v1 Announce Type: cross Abstract: Neural audio codecs provide compact discrete representations for speech generation and manipulation. However, most codecs organize tokens as frame-lev

Express Your Doubts -- Probabilistic World Modeling Should not be Based on Token logprobs

arXiv:2505.02072v2 Announce Type: replace Abstract: Language modeling has shifted in recent years from a distribution over strings to prediction models with textual inputs and outputs for general-purp

Feasible Force Set Shaping for a Payload-Carrying Platform Consisting of Tiltable Multiple UAVs Connected Via Passive Hinge Joints

arXiv:2503.00341v2 Announce Type: replace Abstract: This paper presents a method for shaping the feasible force set of a payload-carrying platform composed of multiple Unmanned Aerial Vehicles (UAVs)

researcharxiv-cs-ro

Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention

arXiv:2605.11752v1 Announce Type: new Abstract: Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods ass

Finite Sentence-Interface Control for Learning Bounded-Fan-Out Linear MCFGs under Fixed Monoid Typing

arXiv:2605.11644v1 Announce Type: cross Abstract: We study positive-data learning of bounded-fan-out linear multiple context-free grammars under a fixed explicit finite monoid homomorphism (h). The ma

FIS-DiT: Breaking the Few-Step Video Inference Barrier via Training-Free Frame Interleaved Sparsity

arXiv:2605.11869v1 Announce Type: new Abstract: While the overall inference latency of Video Diffusion Transformers (DiTs) can be substantially reduced through model distillation, per-step inference l

From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review

arXiv:2605.12303v1 Announce Type: cross Abstract: High-quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI-assisted

From raw data to neutrino candidates: a neural-network pipeline for Baikal-GVD

arXiv:2605.11176v1 Announce Type: cross Abstract: We present a neural-network-based data processing pipeline for Baikal-GVD, designed to improve event reconstruction quality and accelerate neutrino ca

Fully AI-Generated Image Detection: Definition, Recent Advances and Challenges

arXiv:2502.19716v2 Announce Type: replace Abstract: Recent advances in visual generative models have enabled the creation of highly realistic, fully AI-generated images without relying on real source

FuTCR: Future-Targeted Contrast and Repulsion for Continual Panoptic Segmentation

arXiv:2605.12451v1 Announce Type: new Abstract: Continual Panoptic Segmentation (CPS) requires methods that can quickly adapt to new categories over time. The nature of this dense prediction task mean

G^2TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models

arXiv:2605.12309v1 Announce Type: new Abstract: The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. I

GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization

arXiv:2605.12431v1 Announce Type: new Abstract: Conventional gait de-identification methods often encounter an inherent trade-off: they either provide insufficient identity suppression or introduce sp

GATA2Floor: Graph attention for floor counting in street-view facades

arXiv:2605.11863v1 Announce Type: new Abstract: Automated analysis of building facades from street-level imagery has great potential for urban analytics, energy assessment, and emergency planning. How

GeoQuery: Geometry-Query Diffusion for Sparse-View Reconstruction

arXiv:2605.12399v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent paradigm for 3D reconstruction and novel view synthesis. However, it remains vulnerable to sever

Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters

arXiv:2605.11838v1 Announce Type: new Abstract: Gradient clipping is a standard safeguard for training neural networks under noisy, heavy-tailed stochastic gradients; yet, most clipping rules treat al

h-control: Training-Free Camera Control via Block-Conditional Gibbs Refinement

arXiv:2605.11871v1 Announce Type: new Abstract: Training-free camera control for pretrained flow-matching video generators is a partial-observation inverse problem: a depth-warped guidance video suppl

H2G: Hierarchy-Aware Hyperbolic Grouping for 3D Scenes

arXiv:2605.11967v1 Announce Type: new Abstract: Hierarchical 3D grouping aims to recover scene groups across multiple granularities, from fine object parts to complete objects, without relying on sema

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series

arXiv:2605.11130v1 Announce Type: new Abstract: Critical events in multivariate time series, from turbine failures to cardiac arrhythmias, demand accurate prediction, yet labeled data is scarce becaus

High-arity Sample Compression

arXiv:2605.12465v1 Announce Type: new Abstract: Recently, a series of works have started studying variations of concepts from learning theory for product spaces, which can be collected under the name