Search: “research”

Korean aegyo speech shows systematic F1 increase to signal childlike qualities

arXiv:2604.25133v1 Announce Type: new Abstract: Korean aegyo is a socially recognized childlike speaking style used predominantly in romantic interactions among adults. This study examined vowel space

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Language corpora for the Dutch medical domain

DGX agent

arXiv:2604.25374v1 Announce Type: new Abstract: extbf{Background:} Dutch medical corpora are scarce, limiting NLP development. extbf{Methods:} We translated English datasets, identified medical text i

researcharxiv-cs-cl

29 Apr 2026

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators

arXiv:2503.06778v3 Announce Type: replace Abstract: Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annot

Learning biophysical models of gene regulation with probability flow matching

arXiv:2604.25062v1 Announce Type: cross Abstract: Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcription

Learning Illumination Control in Diffusion Models

arXiv:2604.24877v1 Announce Type: new Abstract: Controlling illumination in images is essential for photography and visual content creation. While closed-source models have demonstrated impressive ill

Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics

arXiv:2604.25244v1 Announce Type: cross Abstract: Protein dynamics underlie many biological functions, yet remain difficult to characterize due to the high computational cost of molecular dynamics sim

Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference

arXiv:2604.24911v1 Announce Type: new Abstract: Machine Learning is becoming more prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predicti

Less Is More: Fast and Accurate Reasoning with Cross-Head Unified Sparse Attention

arXiv:2508.07101v2 Announce Type: replace Abstract: Large reasoning models achieve strong performance through test-time scaling, but this incurs substantial computational overhead due to long decoding

Limited Linguistic Diversity in Embodied AI Datasets

arXiv:2601.03136v2 Announce Type: replace Abstract: Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate

Logic of Fuzzy Paths

arXiv:2604.24907v1 Announce Type: cross Abstract: We introduce a new family of temporal logics intended for specifications in motion planning (MP). It builds upon the signal temporal logic (STL), whic

Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures

arXiv:2604.25817v1 Announce Type: new Abstract: Magnification shift is a major obstacle to robust histopathology classification, because models trained on one imaging scale often generalize poorly to

Making AI-Assisted Grant Evaluation Auditable without Exposing the Model

arXiv:2604.25200v1 Announce Type: cross Abstract: Public agencies are beginning to consider large language models (LLMs) as decision-support tools for grant evaluation. This creates a practical govern

Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling

arXiv:2604.25578v1 Announce Type: new Abstract: We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only

Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile

arXiv:2604.25765v1 Announce Type: new Abstract: The quality of training data is critical to the performance of machine learning models. In this paper, the Error Sensitivity Profile (ESP) is proposed.

Measuring the stability and plasticity of recommender systems

arXiv:2508.03941v3 Announce Type: replace-cross Abstract: The typical offline protocol to evaluate recommendation algorithms is to collect a dataset of user-item interactions and then use a part of th

Metric, inertially aligned monocular state estimation via kinetodynamic priors

arXiv:2511.20496v3 Announce Type: replace Abstract: Accurate state estimation for flexible robotic systems poses significant challenges, particularly for platforms with dynamically deforming structure

MGTEVAL: An Interactive Platform for Systemtic Evaluation of Machine-Generated Text Detectors

arXiv:2604.25152v1 Announce Type: cross Abstract: We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detectio

Modeling Human-Like Color Naming Behavior in Context

arXiv:2604.25674v1 Announce Type: new Abstract: Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both le

Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing

arXiv:2604.24880v1 Announce Type: cross Abstract: This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic

MTPano: Multi-Task Panoramic Scene Understanding via Label-Free Integration of Dense Prediction Priors

arXiv:2602.05330v2 Announce Type: replace Abstract: Comprehensive panoramic scene understanding is critical for immersive applications, yet it remains challenging due to the scarcity of high-resolutio

Multi-layer Cross-Attention is Provably Optimal for Multi-modal In-context Learning

arXiv:2602.04872v2 Announce Type: replace-cross Abstract: Recent progress has rapidly advanced our understanding of the mechanisms underlying in-context learning in modern attention-based neural netwo

Multimodal Contextualized Support for Enhancing Video Retrieval System

arXiv:2412.07584v2 Announce Type: replace Abstract: Current video retrieval systems, especially those used in competitions, primarily focus on querying individual keyframes or images rather than encod

Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation

arXiv:2604.25819v1 Announce Type: new Abstract: In this work, we propose Mutual Forcing, a framework for fast autoregressive audio-video generation with long-horizon audio-video synchronization. Our a

Named Entity Recognition of Historical Texts via Large Language Model

arXiv:2508.18090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. On

Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models at the Nishimori Temperature

arXiv:2508.18717v3 Announce Type: replace-cross Abstract: Modern multi-class image classification uses high-dimensional CNN features that incur large memory and computational costs and obscure the dat

Novel 3D Binary Indexed Tree for Volume Computation of 3D Reconstructed Models from Volumetric Data

arXiv:2412.10441v2 Announce Type: replace-cross Abstract: In the burgeoning field of medical imaging, precise computation of 3D volume holds a significant importance for subsequent qualitative analysi

Null Measurability at the Symmetrization Interface in VC Learning

arXiv:2604.25028v1 Announce Type: new Abstract: Recent work revisiting measurability in the fundamental theorem of statistical learning imposes Borel measurability of ghost-gap suprema. We show that,

On quantitative Laplace-type convergence results for some exponential probability measures, with two applications

arXiv:2110.12922v2 Announce Type: replace-cross Abstract: Laplace-type results characterize the limit of sequence of measures (pi_arepsilon)_{arepsilon >0} with density w.r.t the Lebesgue measure (d p

Online combinatorial optimization with stochastic decision sets and adversarial losses

arXiv:2604.25269v1 Announce Type: new Abstract: Most work on sequential learning assumes a fixed set of actions that are available all the time. However, in practice, actions can consist of picking su

Online learning with Erdos-Renyi side-observation graphs

arXiv:2604.25271v1 Announce Type: cross Abstract: We consider adversarial multi-armed bandit problems where the learner is allowed to observe losses of a number of arms beside the arm that it actually

Optimal UGV-UAV Cooperative Partitioning and Inspection of Shortest Paths

arXiv:2604.25284v1 Announce Type: new Abstract: We study cooperative shortest path planning for an unmanned ground vehicle (UGV) assisted by an unmanned aerial vehicle (UAV) in environments with unkno

Optimization-Free Topological Sort for Causal Discovery via the Schur Complement of Score Jacobians

arXiv:2604.25295v1 Announce Type: new Abstract: Continuous causal discovery typically couples representation learning with structural optimization via non-convex acyclicity penalties, which subjects s

PhyloSDF: Phylogenetically-Conditioned Neural Generation of 3D Skull Morphology via Residual Flow Matching

arXiv:2604.25371v1 Announce Type: cross Abstract: Generating novel, biologically plausible three-dimensional morphological structures is a fundamental challenge in computational evolutionary biology,

PINNs in More General Geometry

arXiv:2604.25020v1 Announce Type: cross Abstract: Neural architectures trained with losses inspired by differential conditions are the basis for PINN models. Since many constructions in differential g

PLMGH: What Matters in PLM-GNN Hybrids for Code Classification and Vulnerability Detection

arXiv:2604.25599v1 Announce Type: cross Abstract: Code understanding models increasingly rely on pretrained language models (PLMs) and graph neural networks (GNNs), which capture complementary semanti

Power Foam: Unifying Real-Time Differentiable Ray Tracing and Rasterization

arXiv:2604.24994v1 Announce Type: cross Abstract: We introduce a differentiable 3D representation that unifies the ray tracing capabilities of foam-based ray tracing with the efficiency of modern rast

Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models

arXiv:2604.25642v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual-textual understanding, yet their reliability is critically undermined b

Principled Detection of Hallucinations in Large Language Models via Multiple Testing

arXiv:2508.18473v3 Announce Type: replace Abstract: While Large Language Models (LLMs) have emerged as powerful foundational models to solve a variety of tasks, they have also been shown to be prone t

Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models

arXiv:2604.25859v1 Announce Type: new Abstract: World action models jointly predict future video and action during training, raising an open question about what role the future-prediction branch actua

QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention

arXiv:2604.25306v1 Announce Type: new Abstract: FlashAttention improves efficiency through tiling, but its online softmax still relies on floating-point arithmetic for numerical stability, making full

Quantum Dynamics via Score Matching on Bohmian Trajectories

arXiv:2604.25137v1 Announce Type: cross Abstract: We solve the time-dependent Schrodinger equation by learning the score function, the gradient of the log-probability density, on Bohmian trajectories.

Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis

arXiv:2604.25310v1 Announce Type: new Abstract: This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from ex

Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning

arXiv:2604.24938v1 Announce Type: cross Abstract: Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work has focused on importance criteria

Revisiting the Past: Data Unlearning with Model State History

arXiv:2506.20941v3 Announce Type: replace Abstract: Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, o

Robust Graph Matching through Semantic Relationship Generation for SLAM

arXiv:2604.25404v1 Announce Type: new Abstract: Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed

SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation

arXiv:2604.25315v1 Announce Type: new Abstract: Gradient-based saliency methods are widely used to interpret deep neural networks, yet they often produce noisy and unstable explanations that poorly al

Science is how we’ve cured diseases and how we just sent four humans around the Moon. It’s a big reason why we’re the greatest country on Ea…

Science is how we’ve cured diseases and how we just sent four humans around the Moon. It’s a big reason why we’re the greatest country on Earth, but firing the National Science Board shows just how mu

researchyann-lecun--x

Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation

arXiv:2512.01510v3 Announce Type: replace Abstract: We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation, where we train a network on one domain