research

Contextualized Visual Personalization in Vision-Language Models

arXiv:2602.03454v3 Announce Type: replace Abstract: Despite recent progress in vision-language models (VLMs), existing approaches often fail to generate personalized responses based on the user's spec

Convergence of Consensus-Based Particle Methods for Nonconvex Bi-Level Optimization

DGX agent

arXiv:2605.19667v1 Announce Type: cross Abstract: In this paper, we study a consensus-based optimization method for nonconvex bi-level optimization, where the objective is to minimize an upper-level f

researcharxiv-cs-lg

20 May 2026

Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation

arXiv:2102.11840v2 Announce Type: replace Abstract: In recent years, artificial neural networks have developed into a powerful tool for addressing a multitude of problems for which classical solution

CosFly: Plan in the Matrix, Fly in the World

arXiv:2605.19120v1 Announce Type: new Abstract: We present CosFly, a box-structured planning and multimodal simulation pipeline for aerial tracking, together with CosFly-Track, a large-scale UAV datas

researcharxiv-cs-ro

Counterfactual Likelihood Tests for Indirect Influence in Private Reasoning Channels

arXiv:2605.19092v1 Announce Type: cross Abstract: Reasoning systems increasingly separate intermediate computation into private and public channels, creating evaluation cases that look similar in tran

CounterFlow: A Two-Phase Inference-Time Sampling for Counterfactual Video Foley Generation

arXiv:2605.18916v1 Announce Type: cross Abstract: We investigate Counterfactual Video Foley Generation, which aims to adopt a sound-source identity that contradicts the visual evidence while remaining

CPC-VAR:Continual Personalized and Compositional Generation in Visual Autoregressive Models

arXiv:2605.19750v1 Announce Type: new Abstract: Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation. Despite their strong generative capabili

Cross-Paradigm Knowledge Distillation: A Comprehensive Study of Bidirectional Transfer Between Random Forests and Deep Neural Networks for Big Data Applications

arXiv:2605.19299v1 Announce Type: new Abstract: The exponential growth of big data has intensified the need for efficient and interpretable machine learning models that can handle diverse data charact

Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers

arXiv:2605.18897v1 Announce Type: cross Abstract: Intracranial EEG (iEEG) provides high-fidelity neural recordings essential for clinical and brain-computer interface applications, but acquiring these

Cubit: Token Mixer with Kernel Ridge Regression

arXiv:2605.06501v2 Announce Type: replace-cross Abstract: Since its introduction in 2017, the Transformer has become one of the most widely adopted architectures in modern deep learning. Despite exten

D-Convexity: A Unified Differentiable Convex Shape Prior via Quasi-Concavity for Data-driven Image Segmentation

arXiv:2605.19210v1 Announce Type: new Abstract: Convexity is a fundamental geometric prior that underlies many natural and man-made structures, yet remains challenging to impose effectively in end-to-

D-PACE: Dynamic Position-Aware Cross-Entropy for Parallel Speculative Drafting

arXiv:2605.18810v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by having a small drafter propose tokens that a larger target model verifies in parallel. Recent diffus

Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning

arXiv:2605.18892v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative learning of computer vision models, where privacy and regulatory constraints prevent centralizing data a

Deep Learning-Assisted Improved Differential Fault Attacks on Lightweight Stream Ciphers

arXiv:2603.29382v2 Announce Type: replace-cross Abstract: Lightweight cryptographic primitives are widely deployed in resource-constrained environments, particularly in Internet of Things (IoT) device

Deep Neural Network for Musical Instrument Recognition using MFCCs

arXiv:2105.00933v3 Announce Type: replace-cross Abstract: The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the

Deep Tech to Space: Space Data Centers and AI Revolution at the Edge

arXiv:2605.19892v1 Announce Type: cross Abstract: Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresponding su

Delta Attention Residuals

arXiv:2605.18855v1 Announce Type: cross Abstract: Attention Residuals replace standard additive residual connections with learned softmax attention over previous layer outputs, enabling selective cros

Density-Ratio Losses for Post-Hoc Learning to Defer

arXiv:2605.19557v1 Announce Type: cross Abstract: We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation

arXiv:2602.00545v2 Announce Type: replace Abstract: The eigenvalue distribution of the Hessian matrix plays a crucial role in understanding the optimization landscape of deep neural networks. Prior wo

DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift

arXiv:2605.19231v1 Announce Type: new Abstract: We introduce DeRegiME -- Deep Regime Mixture of Experts -- a direct multi-horizon probabilistic forecaster that separates latent uncertainty regimes fro

Difficulty-Controllable Cloze Question Distractor Generation

arXiv:2511.01526v2 Announce Type: replace Abstract: Multiple-choice cloze questions are commonly used to assess linguistic proficiency and comprehension. However, generating high-quality distractors r

Digital Voices of Survival: From Social Media Disclosures to Support Provisions for Domestic Violence Victims

arXiv:2509.12288v2 Announce Type: replace-cross Abstract: Domestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relatio

Distribution Matching Distillation without Fake Score Network

arXiv:2605.19256v1 Announce Type: new Abstract: Distribution Matching Distillation (DMD) provides an effective distribution-level correction for few-step generation, while relying on an auxiliary fake

Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis

arXiv:2602.03139v2 Announce Type: replace Abstract: Distribution matching distillation (DMD) facilitates few-step image generation by aligning a distilled student with a reference multi-step teacher.

Do Better Volatility Forecasts Lead to Better Portfolios? Evidence from Graph Neural Networks

arXiv:2605.19278v1 Announce Type: cross Abstract: This paper tests whether graph neural networks improve realized volatility forecasts and whether those forecasts improve portfolio performance. Using

Does Your Wildfire Prediction Model Actually Work, or Just Score Well?

arXiv:2605.18911v1 Announce Type: cross Abstract: Wildfire prediction is important for early warning and resource allocation, yet existing Earth foundation models (Earth FMs) are pretrained for genera

Dr.LLM: Dynamic Layer Routing in LLMs

arXiv:2510.12773v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and i

ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation

arXiv:2602.04279v2 Announce Type: replace Abstract: Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) rem

Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers

arXiv:2601.15014v2 Announce Type: replace-cross Abstract: We study in-context learning for nonparametric regression with alpha-Holder smooth regression functions, for some alpha>0. We prove that, with

Efficient Elicitation of Collective Disagreements

arXiv:2605.19521v1 Announce Type: new Abstract: We analyze the structure of the disagreement among a population of voters over a set of alternatives. Surveys typically ask either for pairwise comparis

Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention

arXiv:2605.19726v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) enable globally coherent, bidirectional, and controllable text generation, offering advantages over traditional autoreg

Efficient Pre-Training with Token Superposition

arXiv:2605.06546v2 Announce Type: replace Abstract: Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, requiring complex and invasive modifications in ord

EmbGen: Teaching with Reassembled Corpora

arXiv:2605.19394v1 Announce Type: cross Abstract: Adapting small instruction-tuned models to specialized domains often relies on supervised fine-tuning (SFT) on curated instruction-response examples,

EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection

arXiv:2605.19630v1 Announce Type: new Abstract: With every advancement in generative AI models, forensics is under increasing pressure. The constant emergence of new generation techniques makes it imp

EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors

arXiv:2604.02784v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) excel at multimodal tasks, but they remain vulnerable to hallucinations that are factually incorrect or unground

EpiDiffVO: Geometry-Aware Epipolar Diffusion for Robust Visual Odometry

arXiv:2605.19556v1 Announce Type: new Abstract: Estimating relative pose from image pairs fundamentally requires only a minimal subset of geometrically consistent correspondences. However, most learni

Euclidean Embedding of Data Using Local Distances

arXiv:2605.19243v1 Announce Type: cross Abstract: We study the problem of recovering a globally consistent Euclidean embedding of data, given only a local distance graph and propose a method that opti

ExECG: An Explainable AI Framework for ECG models

arXiv:2605.19258v1 Announce Type: cross Abstract: Deep learning has enabled ECG diagnostic models with strong performance in tasks such as arrhythmia classification and abnormality detection. However,

Eyes on VLM: Benchmarking Gaze Following and Social Gaze Prediction in Vision Language Models

arXiv:2605.19859v1 Announce Type: new Abstract: Vision-language models (VLMs) have rapidly evolved into general-purpose multimodal reasoners with strong zero-shot generalization. In this context, VLMs

Factor Augmented High-Dimensional SGD

arXiv:2605.19291v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is a fundamental optimization algorithm widely used in modern machine learning. In this paper, we propose Factor-Aug

Fast 4D Mesh Generation by Spatio-Temporal Attention Chains

arXiv:2605.19786v1 Announce Type: new Abstract: 4D mesh generation has recently emerged as a powerful paradigm for recovering dynamic 3D structure from videos, but existing methods remain slow, comput

Fast Tensorization of Neural Networks via Slice-wise Feature Distillation

arXiv:2605.19842v1 Announce Type: new Abstract: We propose a scalable tensorization framework for neural network compression based on slice-wise feature distillation. Unlike conventional tensor decomp

Features have life history. And we should care

arXiv:2605.18789v1 Announce Type: cross Abstract: Features in language models have life history: they emerge, persist, and die during training, yet the importance of that history remains largely unexp

FedBiCross: A Bi-Level Optimization Framework to Tackle Non-IID Challenges in Data-Free One-Shot Federated Learning on Medical Data

arXiv:2601.01901v2 Announce Type: replace Abstract: Data-free knowledge distillation-based one-shot federated learning (OSFL) trains a model in a single communication round without sharing raw data, m

Federated Learning with Nonvacuous Generalisation Bounds

arXiv:2310.11203v2 Announce Type: replace Abstract: We introduce a novel strategy to train randomised predictors in federated learning, where each node of the network aims at preserving its privacy by

FGSVQA: Frequency-Guided Short-form Video Quality Assessment

arXiv:2605.20016v1 Announce Type: cross Abstract: Short-form video poses new challenges to the quality assessment of user-generated content (UGC) due to its complex generation pipeline, rapid content

Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG

arXiv:2605.19224v1 Announce Type: new Abstract: Neuroscientists have recently turned to intracranial brain recording methods, like electrocorticography (ECoG), for human experiments because of the fin

Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates

arXiv:2605.20005v1 Announce Type: new Abstract: Fine-tuning large language models on new data improves task performance but degrades capabilities learned during pretraining, a phenomenon known as cata