Search: “research”

Emergent Self-Attention from Astrocyte-Gated Associative Memory Dynamics

arXiv:2604.25481v1 Announce Type: cross Abstract: We introduce a Hopfield-type associative memory in which effective connectivity is multiplicatively modulated by astrocytic gains evolving under an en

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Research

Enhancing SignSGD: Small-Batch Convergence Analysis and a Hybrid Switching Strategy

DGX agent

arXiv:2604.25550v1 Announce Type: new Abstract: SignSGD compresses each stochastic gradient coordinate to a single bit, offering substantial memory and communication savings, but its 1-bit quantizatio

researcharxiv-cs-lg

29 Apr 2026

Evaluation without Generation: Non-Generative Assessment of Harmful Model Specialization with Applications to CSAM

arXiv:2604.25119v1 Announce Type: new Abstract: Auditing the fine-tunes of open-weight generative models for harmful specialization has become a new governance challenge for model hosting platforms. T

Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation

arXiv:2602.13864v2 Announce Type: replace-cross Abstract: Learning in the presence of missing data can result in biased predictions and poor generalizability, among other difficulties, which data impu

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane

arXiv:2604.25885v1 Announce Type: cross Abstract: Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performa

Fast Geometric Embedding for Node Influence Maximization

arXiv:2506.07435v3 Announce Type: replace-cross Abstract: Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we

Fractionally Supervised Classification with Maxima Nominated Samples

arXiv:2604.25145v1 Announce Type: cross Abstract: Fractionally supervised classification (FSC) offers a flexible framework for combining labeled and unlabeled data in model-based classification, but e

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems

arXiv:2507.07847v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and redu

From Cursed to Competitive: Closing the ZO-FO Gap via Input-to-State Stability

arXiv:2604.25372v1 Announce Type: cross Abstract: While it is generally understood that zeroth-order (ZO) algorithms have an extra dependency on their number of iterations for any choice of parameters

From Syntax to Emotion: A Mechanistic Analysis of Emotion Inference in LLMs

arXiv:2604.25866v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in emotionally sensitive human-AI applications, yet little is known about how emotion recognition is

GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances

arXiv:2604.24766v1 Announce Type: new Abstract: With the rise of time-of-use and tiered electricity pricing, energy consumers are encouraged to adopt peak-shifting strategies by automatically controll

GeoSearch: Augmenting Worldwide Geolocalization with Web-Scale Reverse Image Search and Image Matching

arXiv:2604.25390v1 Announce Type: cross Abstract: Worldwide image geolocalization, which aims to predict the GPS coordinates of any image on Earth, remains challenging due to global visual diversity.

Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs

arXiv:2412.08835v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are almost universally built on a single primitive: the neighborhood. Regardless of architectural variations, message p

High-Precision Dichotomous Image Segmentation via Depth Integrity-Prior and Fine-Grained Patch Strategy

arXiv:2503.06100v5 Announce Type: replace Abstract: High-precision dichotomous image segmentation (DIS) is a task of extracting fine-grained objects from high-resolution images. Existing methods trade

IAM: Identity-Aware Human Motion and Shape Joint Generation

arXiv:2604.25164v1 Announce Type: new Abstract: Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. Howeve

Image Compression with Bubble-Aware Frame Rate Adaptation for Energy-Efficient Video Capsule Endoscopy

arXiv:2604.25464v1 Announce Type: new Abstract: Video Capsule Endoscopy (VCE) is a promising method for improving the medical examination of the small intestine in the gastrointestinal tract. A key ch

Immediate Derivatives Suffice for Online Recurrent Adaptation

arXiv:2603.28750v3 Announce Type: replace Abstract: For three decades online recurrent learning has been assumed to require propagating a Jacobian tensor through the network's dynamics at O(n^4) per s

Improving Diversity in Black-box Few-shot Knowledge Distillation

arXiv:2604.25795v1 Announce Type: new Abstract: Knowledge distillation (KD) is a well-known technique to effectively compress a large network (teacher) to a smaller network (student) with little sacri

Improving Sensing Coverage and Compliance of 3D-Printed Artificial Skins Through Multi-Modal Sensing and Soft Materials

arXiv:2604.25563v1 Announce Type: new Abstract: 3D-printed artificial skins are a scalable approach to whole-body tactile and proximity coverage, but prior implementations have been limited to unimoda

researcharxiv-cs-ro

Independent-Component-Based Encoding Models of Brain Activity During Story Comprehension

arXiv:2604.24942v1 Announce Type: new Abstract: Encoding models provide a powerful framework for linking continuous stimulus features to neural activity; however, traditional voxelwise approaches are

Instruction-Evidence Contrastive Dual-Stream Decoding for Grounded Vision-Language Reasoning

arXiv:2604.25809v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit strong performance in instruction following and open-ended vision-language reasoning, yet they frequently generate

Intrinsic Mutual Information as a Modulator for Preference Optimization

arXiv:2604.24804v1 Announce Type: cross Abstract: Offline preference optimization methods, such as Direct Preference Optimization (DPO), offer significant advantages in aligning Large Language Models

Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked?

arXiv:2507.15707v2 Announce Type: replace Abstract: Large Language Models (LLMs) have been evaluated using diverse question types, e.g., multiple-choice, true/false, and short/long answers. This study

Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model

arXiv:2604.25196v1 Announce Type: new Abstract: Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive

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

Language corpora for the Dutch medical domain

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

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

researcharxiv-cs-ro

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

researcharxiv-cs-ro

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