Search: “papers”

Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data

arXiv:2604.08581v1 Announce Type: new Abstract: This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller for

local-aiarxiv-cs-lg

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Harnessing Weak Pair Uncertainty for Text-based Person Search

DGX agent

arXiv:2604.08877v1 Announce Type: new Abstract: In this paper, we study the text-based person search, which is to retrieve the person of interest via natural language description. Prevailing methods u

researcharxiv-cs-cv

13 Apr 2026

Hidden in Plain Sight: Visual-to-Symbolic Analytical Solution Inference from Field Visualizations

arXiv:2604.08863v1 Announce Type: new Abstract: Recovering analytical solutions of physical fields from visual observations is a fundamental yet underexplored capability for AI-assisted scientific rea

LLM4Delay: Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation

arXiv:2510.23636v3 Announce Type: replace-cross Abstract: Flight delay prediction has become a key focus in air traffic management (ATM), as delays reflect inefficiencies in the system. This paper pro

researcharxiv-cs-ai

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2)

model-releasesarxiv-cs-cv

arXiv:2604.09030v1 Announce Type: new Abstract: This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchm

Online Intention Prediction via Control-Informed Learning

model-releasesarxiv-cs-lg

arXiv:2604.09303v1 Announce Type: cross Abstract: This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is

Online Quantile Regression for Nonparametric Additive Models

arXiv:2604.08969v1 Announce Type: cross Abstract: This paper introduces a projected functional gradient descent algorithm (P-FGD) for training nonparametric additive quantile regression models in onli

Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints

arXiv:2604.08759v1 Announce Type: cross Abstract: This paper considers the problem of multi-bit generative watermarking for large language models under a worst-case false-alarm constraint. Prior work

researcharxiv-cs-cl

PRAGMA: Revolut Foundation Model

arXiv:2604.08649v1 Announce Type: cross Abstract: Modern financial systems generate vast quantities of transactional and event-level data that encode rich economic signals. This paper presents PRAGMA,

researcharxiv-cs-cl

Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments

arXiv:2604.09323v1 Announce Type: new Abstract: This paper presents a Robust Adaptive Backstepping Impedance Control (RABIC) strategy for robots operating in contact-rich and uncertain environments. T

researcharxiv-cs-ro

Sample Complexity of Composite Quantum Hypothesis Testing

arXiv:2601.08588v4 Announce Type: replace-cross Abstract: This paper investigates symmetric composite binary quantum hypothesis testing (QHT), where the goal is to determine which of two uncertainty s

Score-Driven Rating System for Sports

arXiv:2604.09143v1 Announce Type: new Abstract: This paper introduces a score-driven rating system, a generalization of the classical Elo rating system that employs the score, i.e. the gradient of the

safetyarxiv-cs-lg

Applications

Scrapyard AI

arXiv:2604.08803v1 Announce Type: cross Abstract: This paper considers AI model churn as an opportunity for frugal investigation of large AI models. It describes how the incessant push for ever more p

applicationsarxiv-cs-ai

Stochastic-Dimension Frozen Sampled Neural Network for High-Dimensional Gross-Pitaevskii Equations on Unbounded Domains

arXiv:2604.09361v1 Announce Type: new Abstract: In this paper, we propose a stochastic-dimension frozen sampled neural network (SD-FSNN) for solving a class of high-dimensional Gross-Pitaevskii equati

TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation

arXiv:2604.09439v1 Announce Type: cross Abstract: In this paper, we propose a sequential recommendation model that integrates Time-aware personalization, Multi-interest personalization, and Explanatio

safetyarxiv-cs-ai

Uncertainty Estimation for the Open-Set Text Classification systems

arXiv:2604.08560v1 Announce Type: cross Abstract: Accurate uncertainty estimation is essential for building robust and trustworthy recognition systems. In this paper, we consider the open-set text cla

Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining

arXiv:2504.12758v3 Announce Type: replace-cross Abstract: In this paper, we show that an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) wireless system with appropriate analog combining co

Applications

A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction

arXiv:2604.06207v1 Announce Type: cross Abstract: This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), ai

applicationsarxiv-cs-ai

Adversarial Evasion Attacks on Computer Vision using SHAP Values

arXiv:2601.10587v2 Announce Type: replace Abstract: The paper introduces a white-box attack on computer vision models using SHAP values. It demonstrates how adversarial evasion attacks can compromise

researcharxiv-cs-cv

Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment

arXiv:2604.06289v1 Announce Type: cross Abstract: In this paper, we analyze and improve the adversarial robustness of a convolutional neural network (CNN) that assists crystal-collimator alignment at

local-aiarxiv-cs-lg

AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models

arXiv:2405.16240v3 Announce Type: replace Abstract: In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the f

AtlasOCR: Building the First Open-Source Darija OCR Model with Vision Language Models

model-releasesarxiv-cs-cv

arXiv:2604.08070v1 Announce Type: new Abstract: Darija, the Moroccan Arabic dialect, is rich in visual content yet lacks specialized Optical Character Recognition (OCR) tools. This paper introduces At

Blockchain and AI: Securing Intelligent Networks for the Future

arXiv:2604.06323v2 Announce Type: cross Abstract: Blockchain and artificial intelligence (AI) are increasingly proposed together for securing intelligent networks, but the literature remains fragmente

agentsarxiv-cs-ai

CBM-Dual: A 65-nm Fully Connected Chaotic Boltzmann Machine Processor for Dual Function Simulated Annealing and Reservoir Computing

arXiv:2604.06808v1 Announce Type: cross Abstract: This paper presents CBM-Dual, the first silicon-proven digital chaotic dynamics processor (CDP) supporting both simulated annealing (SA) and reservoir

local-aiarxiv-cs-lg

Chatbot-Based Assessment of Code Understanding in Automated Programming Assessment Systems

arXiv:2604.07304v1 Announce Type: cross Abstract: Large Language Models (LLMs) challenge conventional automated programming assessment because students can now produce functionally correct code withou

local-aiarxiv-cs-ai

Controller Design for Structured State-space Models via Contraction Theory

arXiv:2604.07069v1 Announce Type: cross Abstract: This paper presents an indirect data-driven output feedback controller synthesis for nonlinear systems, leveraging Structured State-space Models (SSMs

Density-Driven Optimal Control: Convergence Guarantees for Stochastic LTI Multi-Agent Systems

arXiv:2604.08495v1 Announce Type: cross Abstract: This paper addresses the decentralized non-uniform area coverage problem for multi-agent systems, a critical task in missions with high spatial priori

agentsarxiv-cs-ro

Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach

arXiv:2604.07133v1 Announce Type: cross Abstract: This paper focuses on energy savings in downlink operation of cell-free massive MIMO (CF mMIMO) networks under dynamic traffic conditions. We propose

agentsarxiv-cs-ai

Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems

arXiv:2604.06914v1 Announce Type: new Abstract: In this paper, we study a vehicle-to-infrastructure (V2I) system where distributed base stations (BSs) acting as road-side units (RSUs) collect multimod

safetyarxiv-cs-lg

Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach

arXiv:2604.06838v1 Announce Type: new Abstract: In this paper, we propose using Learning from Answer Sets to approximate black-box models, such as Neural Networks (NN), in the specific case of learnin

local-aiarxiv-cs-ai

Applications

Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning

arXiv:2410.08334v2 Announce Type: replace-cross Abstract: In this paper, we build a reinforcement learning framework to study how children compose numbers using base-ten blocks. Studying numerical cog

applicationsarxiv-cs-ai

FMI@SU ToxHabits: Evaluating LLMs Performance on Toxic Habit Extraction in Spanish Clinical Texts

arXiv:2604.06403v1 Announce Type: cross Abstract: The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHab

Formalizing building-up constructions of self-dual codes through isotropic lines in Lean

arXiv:2604.08485v1 Announce Type: cross Abstract: The purpose of this paper is two-fold. First we show that Kim's building-up construction of binary self-dual codes is equivalent to Chinburg-Zhang's H

researcharxiv-cs-cl

Gaussian Approximation for Asynchronous Q-learning

arXiv:2604.07323v1 Announce Type: cross Abstract: In this paper, we derive rates of convergence in the high-dimensional central limit theorem for Polyak-Ruppert averaged iterates generated by the asyn

GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA

arXiv:2510.23868v4 Announce Type: replace Abstract: This paper proposes extit{Group-relative Implicit Fine-Tuning (GIFT)}, a reinforcement learning framework for aligning large language models (LLMs

safetyarxiv-cs-lg

How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence

arXiv:2603.18203v3 Announce Type: replace Abstract: Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel config

safetyarxiv-cs-cl

Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation

arXiv:2405.03420v2 Announce Type: cross Abstract: This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using gradient-based Ne

researcharxiv-cs-ai

Incorporating Social Awareness into Control of Unknown Multi-Agent Systems: A Real-Time Spatiotemporal Tubes Approach

arXiv:2510.25597v2 Announce Type: replace-cross Abstract: This paper presents a decentralized control framework that incorporates social awareness into multi-agent systems with unknown dynamics to ach

safetyarxiv-cs-ro

Iteratively Learning Muscle Memory for Legged Robots to Master Adaptive and High Precision Locomotion

arXiv:2507.13662v2 Announce Type: replace Abstract: This paper presents a scalable and adaptive control framework for legged robots that integrates Iterative Learning Control (ILC) with a biologically

researcharxiv-cs-ro

Learning Debt and Cost-Sensitive Bayesian Retraining: A Forecasting Operations Framework

model-releasesarxiv-cs-lg

arXiv:2604.06438v1 Announce Type: cross Abstract: Forecasters often choose retraining schedules by convention rather than by an explicit decision rule. This paper gives that decision a posterior-space

Logics-Parsing-Omni Technical Report

arXiv:2603.09677v3 Announce Type: replace Abstract: Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the O

MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping

arXiv:2604.08364v1 Announce Type: new Abstract: In this paper, we introduce MegaStyle, a novel and scalable data curation pipeline that constructs an intra-style consistent, inter-style diverse and hi

researcharxiv-cs-cv

Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem

arXiv:2507.09503v2 Announce Type: replace-cross Abstract: This paper proposes a neural stochastic optimization method for efficiently solving the two-stage stochastic unit commitment (2S-SUC) problem

NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results

model-releasesarxiv-cs-cv

arXiv:2604.06945v2 Announce Type: replace Abstract: This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recoverin

On the Uphill Battle of Image frequency Analysis

arXiv:2604.07563v1 Announce Type: new Abstract: This work is a follow up on the newly proposed clustering algorithm called The Inverse Square Mean Shift Algorithm. In this paper a special case of algo

researcharxiv-cs-cv

PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC

arXiv:2604.08036v1 Announce Type: cross Abstract: This paper addresses the problem of training a reinforcement learning (RL) policy under partial observability by exploiting a privileged, anytime-feas

safetyarxiv-cs-ro

PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents

arXiv:2512.14735v2 Announce Type: replace-cross Abstract: This paper proposes PyFi, a novel framework for pyramid-like financial image understanding that enables vision language models (VLMs) to reaso

agentsarxiv-cs-ai

Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study

arXiv:2604.07672v1 Announce Type: new Abstract: This paper presents an empirical study of reset-free reinforcement learning (RL) for real-world agile driving, in which a physical 1/10-scale vehicle le

safetyarxiv-cs-ro