Search: “research”

Beyond the Laplacian: Doubly Stochastic Matrices for Graph Neural Networks

arXiv:2604.15069v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) conventionally rely on standard Laplacian or adjacency matrices for structural message passing. In this work, we substitute

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Bidirectional Cross-Modal Prompting for Event-Frame Asymmetric Stereo

DGX agent

arXiv:2604.15312v1 Announce Type: new Abstract: Conventional frame-based cameras capture rich contextual information but suffer from limited temporal resolution and motion blur in dynamic scenes. Even

researcharxiv-cs-cv

17 Apr 2026

BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps

arXiv:2604.14421v1 Announce Type: new Abstract: Reliable odometry is essential for mobile robots as they increasingly enter more challenging environments, which often contain little information to con

researcharxiv-cs-ro

Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning

arXiv:2604.14974v1 Announce Type: new Abstract: You are a robot and you live in a Markov decision process (MDP) with a finite or an infinite number of transitions from state-action to next states. You

Boundary-Centric Active Learning for Temporal Action Segmentation

arXiv:2604.15173v1 Announce Type: new Abstract: Temporal action segmentation (TAS) demands dense temporal supervision, yet most of the annotation cost in untrimmed videos is spent identifying and refi

C2W-Tune: Cavity-to -Wall Transfer Learning for Thin Atrial Wall Segmentation in 3D Late Gadolinium-enhanced Magnetic Resonance

arXiv:2603.24992v2 Announce Type: replace Abstract: Accurate segmentation of the left atrial (LA) wall in 3D late gadolinium-enhanced MRI (LGE-MRI) is essential for wall thickness mapping and fibrosis

Certified and accurate computation of function space norms of deep neural networks

arXiv:2603.06431v2 Announce Type: replace-cross Abstract: Neural network methods for PDEs require reliable error control in function space norms. However, trained neural networks can typically only be

Challenges in Translating Technical Lectures: Insights from the NPTEL

arXiv:2602.08698v2 Announce Type: replace Abstract: This study examines the practical applications and methodological implications of Machine Translation in Indian Languages, specifically Bangla, Mala

Chaotic CNN for Limited Data Image Classification

arXiv:2604.14645v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) often exhibit poor generalisation in limited training data scenarios due to overfitting and insufficient feature di

CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning

arXiv:2604.14519v1 Announce Type: cross Abstract: Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a ne

CMTM: Cross-Modal Token Modulation for Unsupervised Video Object Segmentation

arXiv:2604.14630v1 Announce Type: new Abstract: Recent advances in unsupervised video object segmentation have highlighted the potential of two-stream architectures that integrate appearance and motio

Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images

arXiv:2604.14506v1 Announce Type: new Abstract: Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated dat

Cognitive Alpha Mining via LLM-Driven Code-Based Evolution

arXiv:2511.18850v2 Announce Type: replace Abstract: Discovering effective predictive signals, or 'alphas,' from financial data with high dimensionality and extremely low signal-to-noise ratio remains

Cognitive Offloading in Agile Teams: How Artificial Intelligence Reshapes Risk Assessment and Planning Quality

arXiv:2604.13814v1 Announce Type: cross Abstract: Recent advances in artificial intelligence (AI) have shown promise in automating key aspects of Agile project management, yet their impact on team cog

Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals

arXiv:2604.13567v1 Announce Type: cross Abstract: Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification tas

Compressing Sequences in the Latent Embedding Space: K-Token Merging for Large Language Models

arXiv:2604.15153v1 Announce Type: new Abstract: Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self-attention scales quadratically

Constraint-based Pre-training: From Structured Constraints to Scalable Model Initialization

arXiv:2604.14769v1 Announce Type: new Abstract: The pre-training and fine-tuning paradigm has become the dominant approach for model adaptation. However, conventional pre-training typically yields mod

Contextuality from Single-State Ontological Models: An Information-Theoretic Obstruction

arXiv:2602.16716v3 Announce Type: replace Abstract: Contextuality is a central feature of quantum theory, traditionally understood as the impossibility of reproducing quantum measurement statistics us

Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving

arXiv:2512.14098v3 Announce Type: replace Abstract: Any-to-Any models are an emerging class of multimodal models that accept combinations of text and multimodal data as input and generate them as outp

Cosine-Similarity Routing with Semantic Anchors for Interpretable Mixture-of-Experts Language Models

arXiv:2509.14255v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models improve efficiency through sparse activation, but their learned gating functions provide limited insight into routin

Counting Without Numbers and Finding Without Words

arXiv:2603.24470v2 Announce Type: replace-cross Abstract: Every year, 10 million pets enter shelters, separated from their families. Despite desperate searches by both guardians and lost animals, 70%

CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction

arXiv:2604.14532v1 Announce Type: new Abstract: Accurate prediction of future risk and disease progression in sepsis is clinically important for early warning and timely intervention in intensive care

CURaTE: Continual Unlearning in Real Time with Ensured Preservation of LLM Knowledge

arXiv:2604.14644v1 Announce Type: new Abstract: The inability to filter out in advance all potentially problematic data from the pre-training of large language models has given rise to the need for me

Dark & Stormy: Modeling Humor in Sentences from the Bulwer-Lytton Fiction Contest

arXiv:2510.24538v2 Announce Type: replace Abstract: Textual humor is enormously diverse and computational studies need to account for this range, including intentionally bad humor. In this paper, we c

Data Synthesis Improves 3D Myotube Instance Segmentation

arXiv:2604.14720v1 Announce Type: new Abstract: Myotubes are multinucleated muscle fibers serving as key model systems for studying muscle physiology, disease mechanisms, and drug responses. Mechanist

Decoupling Scores and Text: The Politeness Principle in Peer Review

arXiv:2604.14162v1 Announce Type: new Abstract: Authors often struggle to interpret peer review feedback, deriving false hope from polite comments or feeling confused by specific low scores. To invest

Deep Learning for Subspace Regression

arXiv:2509.23249v4 Announce Type: replace Abstract: It is often possible to perform reduced order modelling by specifying linear subspace which accurately captures the dynamics of the system. This app

DeepPrune: Parallel Scaling without Inter-trace Redundancy

arXiv:2510.08483v2 Announce Type: replace Abstract: Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-o

Dense Neural Networks are not Universal Approximators

arXiv:2602.07618v3 Announce Type: replace Abstract: We investigate the approximation capabilities of dense neural networks. While universal approximation theorems establish that sufficiently large arc

Design and Validation of a Low-Cost Smartphone Based Fluorescence Detection Platform Compared with Conventional Microplate Readers

arXiv:2604.14527v1 Announce Type: new Abstract: A low cost fluorescence-based optical system is developed for detecting the presence of certain microorganisms and molecules within a diluted sample. A

DEX-Mouse: A Low-cost Portable and Universal Interface with Force Feedback for Data Collection of Dexterous Robotic Hands

arXiv:2604.15013v1 Announce Type: new Abstract: Data-driven dexterous hand manipulation requires large-scale, physically consistent demonstration data. Simulation and video-based methods suffer from s

researcharxiv-cs-ro

Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations

arXiv:2604.15302v1 Announce Type: cross Abstract: LLM-as-judge frameworks are increasingly used for automatic NLG evaluation, yet their per-instance reliability remains poorly understood. We present a

DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models

arXiv:2604.15016v1 Announce Type: new Abstract: EEG foundation models (FMs) achieve strong cross-subject and cross-task generalization but impose substantial computational and memory costs that hinder

Domain-Adaptive Model Merging Across Disconnected Modes

arXiv:2603.05957v2 Announce Type: replace-cross Abstract: Learning across domains is challenging when data cannot be centralized due to privacy or heterogeneity, which limits the ability to train a si

Doubly Outlier-Robust Online Infinite Hidden Markov Model

arXiv:2604.14322v1 Announce Type: cross Abstract: We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is missp

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling

arXiv:2509.03472v2 Announce Type: replace Abstract: Differentially-Private SGD (DP-SGD) and its adaptive variant DP-Adam are powerful techniques to protect user privacy when using sensitive data to tr

DySCO: Dynamic Attention-Scaling Decoding for Long-Context Language Models

arXiv:2602.22175v2 Announce Type: replace Abstract: Understanding and reasoning over long contexts is a crucial capability for language models (LMs). Although recent models support increasingly long c

Edge-preserving noise for diffusion models

arXiv:2410.01540v4 Announce Type: replace Abstract: Classical diffusion models typically rely on isotropic Gaussian noise, treating all regions uniformly and overlooking structural information importa

Efficient Search of Implantable Adaptive Cells for Medical Image Segmentation

arXiv:2604.14849v1 Announce Type: new Abstract: Purpose: Adaptive skip modules can improve medical image segmentation, but searching for them is computationally costly. Implantable Adaptive Cells (IAC

ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving

arXiv:2604.14626v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have become the dominant architecture for large-scale language models, yet on-premises serving remains fundamentally mem

Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks

arXiv:2601.03448v2 Announce Type: replace Abstract: Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning o

Estimating the Diameter at Breast Height of Trees in a Forest from RGB

arXiv:2505.03093v3 Announce Type: replace Abstract: Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon a

Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting

arXiv:2602.12389v3 Announce Type: replace-cross Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot a

Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoring

arXiv:2604.14809v1 Announce Type: cross Abstract: We study a classification problem with three key challenges: pervasive informative missingness, the integration of partial prior expert knowledge into

Explain the Flag: Contextualizing Hate Speech Beyond Censorship

arXiv:2604.14970v1 Announce Type: new Abstract: Hate, derogatory, and offensive speech remains a persistent challenge in online platforms and public discourse. While automated detection systems are wi

Exploring the flavor structure of leptons via diffusion models

arXiv:2503.21432v2 Announce Type: replace-cross Abstract: We propose a method to explore the flavor structure of leptons using diffusion models, which are known as one of generative artificial intelli

Expressivity of Transformers: A Tropical Geometry Perspective

arXiv:2604.14727v1 Announce Type: new Abstract: To quantify the geometric expressivity of transformers, we introduce a tropical geometry framework to characterize their exact spatial partitioning capa

Fabricator or dynamic translator?

arXiv:2604.15165v1 Announce Type: new Abstract: LLMs are proving to be adept at machine translation although due to their generative nature they may at times overgenerate in various ways. These overge