Search: “research”

Design and Biomechanical Evaluation of a Lightweight Low-Complexity Soft Bilateral Ankle Exoskeleton

arXiv:2602.18569v2 Announce Type: replace Abstract: Many people could benefit from exoskeleton assistance during gait, for either medical or nonmedical purposes. But exoskeletons bring added mass and

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Design and Control of a Cable-Driven Switchable Actuator with Torque/Tension Dual Modes for Exoskeletons

DGX agent

arXiv:2609.23487v1 Announce Type: new Abstract: Existing wearable exoskeleton architectures are typically constrained by a single mechanical output modality, providing either joint torque around an an

researcharxiv-cs-ro

22 Sept 2026

Detecting Agitation Before Behavioral Escalation in Autistic Youth Through Multimodal Wearable Sensing

arXiv:2609.24791v1 Announce Type: cross Abstract: Challenging behaviors including aggression, self-injury, and property destruction are observed in 68% of autistic youth and pose risks to youth and ca

DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation

arXiv:2609.24976v1 Announce Type: cross Abstract: Dexterous manipulation depends on contact dynamics that are often only partially observable from vision. Recent World-Action Models (WAMs) couple pred

DFD-Lab: A Modular Audio-Visual Deepfake Detection Pipeline

arXiv:2609.23830v1 Announce Type: new Abstract: Comparing audio-visual deepfake detectors requires coordinating dataset adaptation, temporal input representation, model interfaces and experimental con

Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models

arXiv:2609.22161v1 Announce Type: cross Abstract: Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how t

DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detection

arXiv:2609.22136v1 Announce Type: new Abstract: Text anomaly detection, the task of identifying text instances that deviate from normal language patterns, is crucial for language-driven applications.

Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models

arXiv:2411.05005v2 Announce Type: replace Abstract: Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most exi

Discovering Physical Representation Languages

arXiv:2609.23381v1 Announce Type: new Abstract: Before a machine can discover a physical law, it must discover what its measurements are: which observations live on cells, which are intensive or exten

Discretization-independent operator learning for partial differential equations

arXiv:2507.07292v2 Announce Type: replace Abstract: We develop a new and general encode-approximate-reconstruct operator learning model that leverages learned neural representations of bases for input

Dissecting Hierarchical Reasoning Models: A Mechanistic Study

arXiv:2609.22197v1 Announce Type: new Abstract: We study Hierarchical Reasoning Model (HRM), a representative hierarchical Transformer-based latent reasoning model with many variants, on Sudoku, Maze,

Distributed Linear Solvers and Data Heterogeneity

arXiv:2304.10640v5 Announce Type: replace-cross Abstract: We consider the problem of solving a large-scale system of linear equations in a distributed/federated setting. The taskmaster solves the syst

Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations

arXiv:1812.09632v3 Announce Type: replace Abstract: We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributio

DPTM-DT: Dual-Pretrained Transformer Multitask Representation Learning for Drug-Target Prediction

arXiv:2609.22184v1 Announce Type: new Abstract: Drug-target relation prediction supports candidate screening, drug repositioning, and mechanism analysis. Existing models often use incomplete drug or p

Dropout Neural Network Training Viewed from a Percolation Perspective

arXiv:2512.13853v3 Announce Type: replace Abstract: In this work, we investigate the existence and effect of percolation in training deep Neural Networks (NNs) with dropout. Dropout methods are regula

DualWAM: Dual-System World Action Models for Asynchronous Global Planning and Local Refinement

arXiv:2609.24868v1 Announce Type: new Abstract: World Action Models (WAMs) jointly generate robot actions and predict future world states, transferring priors from video pretraining to robot control.

Efficient Concertormer for Image Deblurring and Beyond

arXiv:2404.06135v4 Announce Type: replace Abstract: The Transformer architecture has achieved remarkable success in natural language processing and high-level vision tasks over the past few years. How

EmbeddGAN: A Novel GAN Framework Using an Embedding Network and Gini Distance Correlation

arXiv:2609.22508v1 Announce Type: new Abstract: Generative Adversarial Networks (GANs) have demonstrated strong performance in generating high-quality synthetic data. However, they are limited by no f

Enhancing Shrimp Disease Detection via Deep Learning and Data Refinement for Resilient Aquaculture

arXiv:2609.23397v1 Announce Type: new Abstract: Shrimp diseases continue to cause devastating losses in the aquaculture industry, driving a critical need for robust, automated detection. This work con

Enhancing Transformer Representations of Symbolic ODE Expressions

arXiv:2609.24746v1 Announce Type: new Abstract: Existing approaches to solving differential equations, such as symbolic regression, physics informed neural networks, and neural operators, typically fo

Error-Supervised Synthetic Learner Writing for Automated Essay Scoring

arXiv:2609.23573v1 Announce Type: new Abstract: Synthetic essays can help reduce dependence on human-written data in Automated Essay Scoring (AES). However, they often lack realistic errors, limiting

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining

arXiv:2505.19893v2 Announce Type: replace-cross Abstract: Large language model pretraining is compute-intensive, yet many tokens contribute marginally to learning, resulting in inefficiency. We introd

Estimation and Control of Tensegrity Manipulator Kinematics based on Strut Inclination Angles

arXiv:2609.24535v1 Announce Type: new Abstract: Unlike conventional rigid-link robots defined by discrete joints, continuum robots pose a fundamental challenge for expressing their complex continuous

Event Signature Transfer: Model-Agnostic Forecast Scenario Construction from Historical Events

arXiv:2609.23074v1 Announce Type: cross Abstract: Forecasters often know an event is imminent but not the shape, size, or timing of its effect. We introduce Event Signature Transfer (EST), a training-

Exact Quotients of Fresnel-Kummer Surfaces and Certified Biaxial Refraction

arXiv:2609.23249v1 Announce Type: new Abstract: The Fresnel wave surface governs the propagation of light in a transparent biaxial crystal. It is a special Kummer quartic, and we identify it exactly.

Exactness at Inference: A Representational Criterion for Out-of-Distribution Generalization

arXiv:2609.24942v1 Announce Type: new Abstract: A model generalizes outside its training distribution only when it computes a representation structurally equivalent to the generating mechanism, not an

Experimental Evaluation of a Low-Power Ultra-Wideband Receiver for Spectrum Sensing

arXiv:2609.22134v1 Announce Type: cross Abstract: Walsh-sequence-based receiver architectures offer an alternative approach for the reception and reconstruction of multiple simultaneous RF signals ove

Expert-Play Contouring Control: Faster-than-Demonstration Planning from Slow Expert and Fast Play

arXiv:2609.22798v1 Announce Type: new Abstract: Expert demonstrations often specify what a robot should do, but not how fast it can do it. Imitation Learning (IL) inherits demonstration timing, while

Exponential Family Synthetic Controls

arXiv:2609.23970v1 Announce Type: cross Abstract: We develop exponential family synthetic controls (EFSC), a distributional version of synthetic controls for a panel of datasets. Each cell of the pane

Fast Graph Laplacian Estimation using Effective Resistance

arXiv:2609.23668v1 Announce Type: cross Abstract: Inferring network topology from noisy node observations is a central problem in graph signal processing. In this paper, we consider Laplacian-constrai

Feasibility Distance Fields for Heterogeneous Constraints in Robot Configuration Space

arXiv:2609.24632v1 Announce Type: new Abstract: Robot manipulators are monitored by constraint-specific indicators whose units and gradient scales are not comparable, so they do not provide a common m

Feature Suppression and Differential Privacy for Residential Traffic Classification: A Two-Home Federated Study

arXiv:2609.23521v1 Announce Type: new Abstract: Residential traffic classification supports service management, but learning across homes must account for heterogeneous traffic and privacy constraints

Finite Topological Space Filtrations: A Topological Framework for Data Analysis

arXiv:2512.23348v3 Announce Type: replace-cross Abstract: We introduce a data-analysis framework based on filtrations of finite topological spaces. Starting from a finite metric data set, we construct

Forecasting Intrathecal Tracer Enhancement from Pre-Contrast Brain MRI: Direct Regression versus Flow Matching

arXiv:2609.22377v1 Announce Type: cross Abstract: Intrathecal contrast-enhanced MRI tracks how a cerebrospinal-fluid tracer spreads through the brain, but requires repeated scans over 24--48,h. Foreca

From Articles to Publishers: Aggregating Language Model Predictions for News Source Reliability Inference

arXiv:2609.24219v1 Announce Type: new Abstract: Traditionally, the reliability of news publishers is assessed by expert organisations that evaluate editorial practices, transparency and factual standa

From Latent Biomarkers to Clinical Rules: Embedding-Guided Rule Mining and Attribution-Based Translation for Interpretable Tabular Learning

arXiv:2609.22155v1 Announce Type: new Abstract: Clinical decision support tools are most useful when accurate predictions are accompanied by understandable explanations. Rule-based models provide tran

From Noise to Signal: When Outliers Seed New Topics

arXiv:2603.18358v2 Announce Type: replace Abstract: Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals of emerging topics. We introduce

From Outliers to Topics in Language Models: Anticipating Trends in News Corpora

arXiv:2509.22030v2 Announce Type: replace Abstract: This paper examines how outliers, often dismissed as noise in topic modeling, can act as weak signals of emerging topics in dynamic news corpora. Us

From Regional to Global: Transfer Learning for Atmospheric Transport Emulators

arXiv:2609.23838v1 Announce Type: new Abstract: Greenhouse gas emissions estimates can be derived using inverse methods by combining atmospheric concentration observations with chemical transport mode

From Tables to Quantified Statements: Evaluating LLM Inference Generation through Executable Verification

arXiv:2609.23966v1 Announce Type: new Abstract: LLMs can generate fluent descriptions from tables, but their outputs may remain logically unsupported by the structured data. We introduce STAT-TO-TEXT,

GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation

arXiv:2609.24981v1 Announce Type: new Abstract: We present a compact geometry-native latent space as a shared foundation for perception and generation. Visual generators can produce photorealistic fra

GAPS: Generative Active Pseudo-view Selection for Sparse-View 3D Gaussian Splatting

arXiv:2609.23436v1 Announce Type: new Abstract: Novel view synthesis from sparse observations is severely under-constrained. Although 3D Gaussian Splatting (3DGS) enables real-time rendering, it produ

GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model

arXiv:2609.23761v1 Announce Type: new Abstract: Internal voids are ubiquitous defects in manufactured structures, yet their characterization remains challenging because their geometry is hidden and ca

GeoBalance: Geometry-Aware Monitoring and Reconstruction with Asymmetric Optimization for Balanced Multimodal Learning

arXiv:2609.23533v1 Announce Type: new Abstract: Multimodal classifiers can converge to modality-dominant solutions in which one modality dominates the joint prediction, suppressing the learning of oth

GLR-MM: Graph-Based Global-Local Reconstruction for Robust Multimodal Chest X-ray and EHR Representation Learning under Missing Modalities

arXiv:2609.23876v1 Announce Type: new Abstract: Clinical multimodal models must often predict before all chest X-ray (CXR) and electronic health record (EHR) inputs are available. Existing approaches

GNN-based Path-aware multi-view Circuit Learning for Technology Mapping

arXiv:2601.14286v2 Announce Type: replace-cross Abstract: Traditional technology mapping suffers from systemic inaccuracies in delay estimation due to its reliance on abstract, technology-agnostic del

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers

arXiv:2506.11784v2 Announce Type: replace Abstract: Vision Transformers (ViTs) are essential in computer vision but are computationally intensive, too. Model quantization, particularly to low bit-widt

GrapeSplat: Geometry-Grounded Reconstruction via Amalgamated Pose-Free Encoding for Feed-Forward 3D Gaussian Splatting

arXiv:2609.23182v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting now reconstructs renderable scenes from unposed, uncalibrated images. Yet, most models supervise only photometric con