Search: “research”

DTEA: A Dual-Topology Elastic Actuator Enabling Real-Time Switching Between Series and Parallel Compliance

arXiv:2604.15865v1 Announce Type: new Abstract: Series and parallel elastic actuators offer complementary but mutually exclusive advantages, yet no existing actuator enables real-time transition betwe

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DVP-MVS++: Synergize Depth-Normal-Edge and Harmonized Visibility Prior for Multi-View Stereo

DGX agent

arXiv:2506.13215v2 Announce Type: replace Abstract: Recently, patch deformation-based methods have demonstrated significant effectiveness in multi-view stereo due to their incorporation of deformable

researcharxiv-cs-cv

20 Apr 2026

Early Detection of Acute Myeloid Leukemia (AML) Using YOLOv12 Deep Learning Model

arXiv:2604.16082v1 Announce Type: cross Abstract: Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a ch

EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence

arXiv:2509.14977v2 Announce Type: replace Abstract: Ultrasound imaging has become the preferred imaging modality for early cancer screening due to its advantages of non-ionizing radiation, low cost, a

Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan

arXiv:2604.16284v1 Announce Type: new Abstract: Atmospheric haze significantly degrades wildlife imagery, impeding computer vision applications critical for conservation, such as animal detection, tra

Environment-Adaptive Solid-State LiDAR-Inertial Odometry

arXiv:2604.15864v1 Announce Type: new Abstract: Solid-state LiDAR-inertial SLAM has attracted significant attention due to its advantages in speed and robustness. However, achieving accurate mapping i

Evaluating quality in synthetic data generation for large tabular health datasets

arXiv:2604.15961v1 Announce Type: new Abstract: There is no consensus in the field of synthetic data on concise metrics for quality evaluations or benchmarks on large health datasets, such as historic

EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond

arXiv:2411.18328v2 Announce Type: replace Abstract: Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with tradition

EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs

arXiv:2604.15787v1 Announce Type: cross Abstract: We introduce EVIL (extbf{EV}olving extbf{I}nterpretable algorithms with extbf{L}LMs), an approach that uses LLM-guided evolutionary search to discover

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data

arXiv:2604.15380v1 Announce Type: cross Abstract: We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-p

Factor Graph-Based Shape Estimation for Continuum Robots via Magnus Expansion

arXiv:2604.15619v1 Announce Type: new Abstract: Reconstructing the shape of continuum manipulators from sparse, noisy sensor data is a challenging task, owing to the infinite-dimensional nature of suc

Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation

arXiv:2505.21072v4 Announce Type: replace Abstract: Large Language Models (LLMs) enhanced with retrieval, an approach known as Retrieval-Augmented Generation (RAG), have achieved strong performance in

Faster LLM Inference via Sequential Monte Carlo

arXiv:2604.15672v1 Announce Type: cross Abstract: Speculative decoding (SD) accelerates language model inference by drafting tokens from a cheap proposal model and verifying them against an expensive

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use

arXiv:2604.15646v1 Announce Type: new Abstract: Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet wr

FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale

arXiv:2604.16265v1 Announce Type: new Abstract: Existing multi-hazard susceptibility mapping (MHSM) studies often rely on spatially uniform models, treat hazards independently, and provide limited rep

GenHSI: Controllable Generation of Human-Scene Interaction Videos

arXiv:2506.19840v2 Announce Type: replace Abstract: Large-scale pre-trained video diffusion models have exhibited remarkable capabilities in diverse video generation. However, existing solutions face

Hallucination as Trajectory Commitment: Causal Evidence for Asymmetric Attractor Dynamics in Transformer Generation

arXiv:2604.15400v1 Announce Type: cross Abstract: We present causal evidence that hallucination in autoregressive language models is an early trajectory commitment governed by asymmetric attractor dyn

Harmonizing Multi-Objective LLM Unlearning via Unified Domain Representation and Bidirectional Logit Distillation

arXiv:2604.15482v1 Announce Type: cross Abstract: Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning dema

How Hypocritical Is Your LLM judge? Listener-Speaker Asymmetries in the Pragmatic Competence of Large Language Models

arXiv:2604.15873v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly studied as repositories of linguistic knowledge. In this line of work, models are commonly evaluated both

HYPERHEURIST: A Simulated Annealing-Based Control Framework for LLM-Driven Code Generation in Optimized Hardware Design

arXiv:2604.15642v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promising progress for generating Register Transfer Level (RTL) hardware designs, largely because they can rap

Impact of Nonlinear Power Amplifier on Massive MIMO: Machine Learning Prediction Under Realistic Radio Channel

arXiv:2604.15977v1 Announce Type: new Abstract: M-MIMO is one of the crucial technologies for increasing spectral and energy efficiency of wireless networks. Most of the current works assume that M-MI

Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies

arXiv:2604.15607v1 Announce Type: cross Abstract: AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and join

Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration

arXiv:2512.07173v4 Announce Type: replace Abstract: We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate the dynamic n

LACE: Lattice Attention for Cross-thread Exploration

arXiv:2604.15529v1 Announce Type: new Abstract: Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not inter

Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners

arXiv:2601.02996v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit

Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts

arXiv:2604.15581v1 Announce Type: cross Abstract: Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key compone

Learning to Reason with Insight for Informal Theorem Proving

arXiv:2604.16278v1 Announce Type: new Abstract: Although most of the automated theorem-proving approaches depend on formal proof systems, informal theorem proving can align better with large language

Learning Uncertainty from Sequential Internal Dispersion in Large Language Models

arXiv:2604.15741v1 Announce Type: cross Abstract: Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model in

Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks

arXiv:2604.15392v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on challenging partial differe

LLaMo: Scaling Pretrained Language Models for Unified Motion Understanding and Generation with Continuous Autoregressive Tokens

arXiv:2602.12370v2 Announce Type: replace Abstract: Recent progress in large models has led to significant advances in unified multimodal generation and understanding. However, the development of mode

LLM Reasoning Is Latent, Not the Chain of Thought

arXiv:2604.15726v1 Announce Type: new Abstract: This position paper argues that large language model (LLM) reasoning should be studied as latent-state trajectory formation rather than as faithful surf

Losses that Cook: Topological Optimal Transport for Structured Recipe Generation

arXiv:2601.02531v2 Announce Type: replace-cross Abstract: Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural

LP^{2}DH: A Locality-Preserving Pixel-Difference Hashing Framework for Dynamic Texture Recognition

arXiv:2604.15707v1 Announce Type: new Abstract: Spatiotemporal Local Binary Pattern (STLBP) is a widely used dynamic texture descriptor, but it suffers from extremely high dimensionality. To tackle th

Machine learning approaches to uncover the neural mechanisms of motivated behaviour: from ADHD to individual differences in effort and reward sensitivity

arXiv:2604.15363v1 Announce Type: cross Abstract: Motivated behaviour relies on the brain's capacity to evaluate effort and reward. Dysregulation within these processes contributes to a spectrum of co

Majority Voting for Code Generation

arXiv:2604.15618v1 Announce Type: new Abstract: We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies

Make Tracking Easy: Neural Motion Retargeting for Humanoid Whole-body Control

arXiv:2603.22201v2 Announce Type: replace Abstract: Humanoid robots require diverse motor skills to integrate into complex environments, but bridging the kinematic and dynamic embodiment gap from huma

Mapping High-Performance Regions in Battery Scheduling across Data Uncertainty, Battery Design, and Planning Horizons

arXiv:2604.15360v1 Announce Type: new Abstract: This study presents a triadic analysis of energy storage operation under multi-stage model predictive control, investigating the interplay between data

Mechanisms of Prompt-Induced Hallucination in Vision-Language Models

arXiv:2601.05201v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) are highly capable, yet often hallucinate by favoring textual prompts over visual evidence. We study this

Mitigating hallucinations and omissions in LLMs for invertible problems: An application to hardware logic design automation

arXiv:2512.03053v2 Announce Type: replace-cross Abstract: We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain

MMAudioSep: Taming Video-to-Audio Generative Model Towards Video/Text-Queried Sound Separation

arXiv:2510.09065v2 Announce Type: replace-cross Abstract: We introduce MMAudioSep, a generative model for video/text-queried sound separation that is founded on a pretrained video-to-audio model. By l

Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models

arXiv:2507.20058v3 Announce Type: replace-cross Abstract: Predicting Parkinson's Disease (PD) progression is crucial for personalized treatment, and voice biomarkers offer a promising non-invasive met

Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortion Detection

arXiv:2509.17292v3 Announce Type: replace-cross Abstract: Cognitive distortions have been closely linked to mental health disorders, yet their automatic detection remains challenging due to contextual

Neural Continuous-Time Markov Chain: Discrete Diffusion via Decoupled Jump Timing and Direction

arXiv:2604.15694v1 Announce Type: new Abstract: Discrete diffusion models based on continuous-time Markov chains (CTMCs) have shown strong performance on language and discrete data generation, yet exi

Neural Gabor Splatting: Enhanced Gaussian Splatting with Neural Gabor for High-frequency Surface Reconstruction

arXiv:2604.15941v1 Announce Type: new Abstract: Recent years have witnessed the rapid emergence of 3D Gaussian splatting (3DGS) as a powerful approach for 3D reconstruction and novel view synthesis. I

Neuro-Symbolic ODE Discovery with Latent Grammar Flow

arXiv:2604.16232v1 Announce Type: cross Abstract: Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and

NeuroLip: An Event-driven Spatiotemporal Learning Framework for Cross-Scene Lip-Motion-based Visual Speaker Recognition

arXiv:2604.15718v1 Announce Type: cross Abstract: Visual speaker recognition based on lip motion offers a silent, hands-free, and behavior-driven biometric solution that remains effective even when ac

Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models

arXiv:2510.21783v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated powerful performance in generating high-quality images. A typical example is text-to-image generator like S

One-Shot Generative Flows: Existence and Obstructions

arXiv:2604.15439v1 Announce Type: cross Abstract: We study dynamic measure transport for generative modelling in the setting of a stochastic process X_ullet whose marginals interpolate between a sourc