Search: “papers”

arXiv:2605.25648v1 Announce Type: cross Abstract: This paper proposes StrTransformer, a source-wise structured Transformer framework for blind source recovery and branch-wise latent modeling. Instead

applicationsarxiv-cs-lg

26 May 2026

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Toward Enactive Artificial Intelligence

DGX agent

arXiv:2605.24238v1 Announce Type: new Abstract: In this paper, we advocate for incorporating enactive approaches to perception and cognition into artificial intelligence (AI). Enactive approaches view

agentsarxiv-cs-ai

26 May 2026

Towards Low-Gravity Planetary Exploration using Reinforcement Learning for Walking, Jumping, and In-flight Attitude Control

arXiv:2605.24643v1 Announce Type: new Abstract: This paper presents reinforcement learning (RL) policies for dynamic quadrupedal locomotion in planetary exploration scenarios. Building on a taskoptimi

safetyarxiv-cs-ro

26 May 2026

Zeroth-Order Nonconvex Nonsmooth Optimization with Heavy-Tailed Noise

arXiv:2605.24513v1 Announce Type: new Abstract: This paper considers the nonconvex nonsmooth problem in which the objective function is Lipschitz continuous. We focus on the stochastic setting where t

26 May 2026

A graph-based analysis of semantic types and coercion in contextualized word embeddings

arXiv:2605.23710v1 Announce Type: new Abstract: Semantic type mismatch between a noun and its context is central to coercion phenomena. This paper introduces a graph-based method to examine how lexica

Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads

arXiv:2605.23247v1 Announce Type: new Abstract: In this paper, we introduce the first machine learning framework for predicting optimal processing times in Single-Level Tree Network (SLTN) architectur

tutorialsarxiv-cs-lg

AI Evaluation Should Require Standardized Item-Level Data Releases

model-releasesarxiv-cs-ai

arXiv:2604.03244v2 Announce Type: replace Abstract: This position paper argues that standardized item-level benchmark data should become the default infrastructure for AI evaluation. Current evaluatio

Beyond Normal References: Discriminative Few-Shot Anomaly Detection

arXiv:2605.23231v1 Announce Type: new Abstract: This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomal

tutorialsarxiv-cs-cv

Diffusion Domain Expansion: Learning to Coordinate Pre-trained Diffusion Models

arXiv:2605.23275v1 Announce Type: new Abstract: In this paper, we propose Diffusion Domain Expansion (DDE), a method that efficiently extends pre-trained diffusion models to generate larger objects an

DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks

arXiv:2605.23056v1 Announce Type: cross Abstract: Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences. This paper p

Generator-Refiner-Examiner: A Tri-Module Data Augmentation Framework for 3D Human Avatar Learning from Monocular Videos

arXiv:2605.23555v1 Announce Type: new Abstract: This paper addresses the challenge of reconstructing photorealistic and animatable 3D human avatars from monocular videos. While existing methods rely o

researcharxiv-cs-cv

On the Koopman-Based Generalization Bounds for Multi-Task Deep Learning

arXiv:2512.19199v2 Announce Type: replace-cross Abstract: The paper establishes generalization bounds for multitask deep neural networks using operator-theoretic techniques. The authors propose a tigh

On the Provable Importance of Gradients for Language-Assisted Image Clustering

arXiv:2510.16335v4 Announce Type: replace Abstract: This paper investigates the recently emerged problem of Language-assisted Image Clustering (LaIC), where textual semantics are leveraged to improve

tutorialsarxiv-cs-cv

Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning

arXiv:2512.19184v2 Announce Type: replace-cross Abstract: This paper presents novel generalization bounds for vector-valued neural networks and deep kernel methods, focusing on multi-task learning thr

Order-Optimal Sequential 1-Bit Mean Estimation in General Tail Regimes

model-releasesarxiv-cs-lg

arXiv:2604.07796v2 Announce Type: replace-cross Abstract: In this paper, we study the problem of mean estimation under 1-bit communication constraints. We propose a novel adaptive mean estimator based

Applications

PLACE: Prompt Learning for Attributed Community Search in Large Graphs

arXiv:2507.05311v2 Announce Type: replace-cross Abstract: In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enli

applicationsarxiv-cs-ai

Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia

arXiv:2605.22836v1 Announce Type: cross Abstract: Rapid earthquake magnitude estimation is crucial for effective early warning systems that can save lives and reduce economic damage. In this paper, we

Robust Counterfactual Inference in Markov Decision Processes

arXiv:2502.13731v5 Announce Type: replace Abstract: This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a

SciNet: Evaluating AI Agents in Relation-Aware Scientific Literature Retrieval

arXiv:2601.03260v2 Announce Type: replace-cross Abstract: AI agents have seen widespread adoption in information retrieval for scientific research, giving rise to tools such as Deep Research. However,

Signal Temporal Logic Motion Planning via Graphs of Convex Sets

arXiv:2605.23240v1 Announce Type: new Abstract: This paper investigates continuous-time motion planning under Signal Temporal Logic (STL) specifications. The goal is to generate smooth robot trajector

researcharxiv-cs-ro

The Surprising Difficulty of Search in Model-Based Reinforcement Learning

model-releasesarxiv-cs-ai

arXiv:2601.21306v2 Announce Type: replace-cross Abstract: This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compoundin

U-CESE: Unified Clip-based Event Search Engine for AI Challenge HCMC 2025

arXiv:2605.23274v1 Announce Type: new Abstract: Retrieving events from large-scale video datasets is challenging due to complex temporal, spatial, and multimodal information. This paper presents U-CES

researcharxiv-cs-cv

Soft Bayesian Context Tree Models for Real-Valued Time Series

arXiv:2601.11079v2 Announce Type: replace Abstract: This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers

23 May 2026

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets

model-releasesarxiv-cs-lg

arXiv:2605.22529v1 Announce Type: new Abstract: This paper investigates a unexplored yet impactful vulnerability in AI explainability used in intrusion detection (IDS): multicollinearity-induced insta

23 May 2026

Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development

arXiv:2605.20456v1 Announce Type: cross Abstract: Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capa

agentsarxiv-cs-ai

Audience Engagement with Arabic Women's Social Empowerment and Wellbeing: A Decadal Corpus

arXiv:2605.22204v1 Announce Type: new Abstract: This paper presents the Arabic Women and Society Corpus, a ten year collection of 252,487 public Arabic Facebook posts related to women's empowerment an

Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning

arXiv:2401.00139v3 Announce Type: replace-cross Abstract: This paper introduces a causal attribution model to enhance the interpretability of large language models (LLMs) and improve their causal reas

Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems

arXiv:2605.21723v1 Announce Type: new Abstract: This paper studies heterogeneous multi-team collaboration through dynamic robot allocation, where robots are treated as transferable resources. Leveragi

safetyarxiv-cs-ro

MagicFuse: Single Image Fusion for Visual and Semantic Reinforcement

arXiv:2602.01760v2 Announce Type: replace Abstract: This paper focuses on a highly practical scenario: how to continue benefiting from the advantages of multi-modal image fusion under harsh conditions

tutorialsarxiv-cs-cv

Non-Contact Vibration-Based Damage Detection of Civil Structures Using a Cost-Effective Autonomous UAV

arXiv:2605.21914v1 Announce Type: new Abstract: This paper presents a non-contact approach for vibration-based structural damage detection using an autonomous and customized cost-effective unmanned ae

safetyarxiv-cs-ro

On the Complexity of Entailment for Cumulative Propositional Dependence Logics

arXiv:2605.21113v1 Announce Type: cross Abstract: This paper establishes and proves complexity results for entailment for cumulative propositional dependence logic and for cumulative propositional log

Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance

arXiv:2605.22202v1 Announce Type: new Abstract: In this paper, we show that high-performing embedding models organize their embedding spaces in a consistent way. We evaluate 25 contemporary embedding

When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey

arXiv:2602.06995v2 Announce Type: replace-cross Abstract: This paper surveys the state-of-the-art in the nexus of SLAM and Wireless Communications, attributing the bidirectional impact of each with a

agentsarxiv-cs-cv

A Semantic and Occlusion-Aware GM-PHD Filter

arXiv:2605.20666v1 Announce Type: new Abstract: This paper proposes a new birth model including semantic information derived from deep learning to create an occlusion-aware Gaussian Mixture Probabilit

agentsarxiv-cs-ro

ACL-Verbatim: hallucination-free question answering for research

arXiv:2605.21102v1 Announce Type: new Abstract: Academic researchers need efficient and reliable methods for collecting high-quality information from trusted sources, but modern tools for AI-assisted

Adaptive Probe-based Steering for Robust LLM Jailbreaking

arXiv:2605.20286v1 Announce Type: cross Abstract: Recent work has demonstrated the potential of contrastive steering for jailbreaking Large Language Models (LLMs). However, existing methods rely on li

tutorialsarxiv-cs-lg

AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes

arXiv:1908.05972v3 Announce Type: replace Abstract: This paper significantly improves on, and finishes to validate, an approach proposed in previous research in which safety outcomes were predicted fr

safetyarxiv-cs-lg

Beyond Numerical Features: CNN-Driven Algorithm Selection via Contour Plots for Continuous Black-Box Optimization

arXiv:2605.20797v1 Announce Type: new Abstract: The present paper introduces a new representation-driven approach to per-instance algorithm selection, applied to black-box optimization, for automatica

Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies

arXiv:2502.17518v2 Announce Type: replace Abstract: This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging clas

Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment

model-releasesarxiv-cs-lg

arXiv:2605.21217v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA

Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities

arXiv:2605.21369v1 Announce Type: new Abstract: This paper describes the fifth edition of the Shared Task on Multilingual Coreference Resolution, held in conjunction with the CODI-CRAC 2026 workshop.

Fully Actuated Manifold Constraint Based Output Feedback Control for Input-Constrained Uncertain Nonlinear Systems

arXiv:2605.21439v1 Announce Type: cross Abstract: This paper presents a low-complexity, model-free, output-feedback controller for a class of unknown time-varying nonlinear systems with unknown input

researcharxiv-cs-ro

Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum

model-releasesarxiv-cs-ro

arXiv:2605.21133v1 Announce Type: new Abstract: In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this task poses two key challenges: 1) S

JobArabi: An Arabic Corpus and Analysis of Job Announcements from Social Media

arXiv:2605.20960v1 Announce Type: new Abstract: This paper introduces JobArabi, a large-scale corpus of Arabic job announcements collected from social media between January 2024 and October 2025. The

LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series

arXiv:2605.20449v1 Announce Type: new Abstract: Can language-pretrained transformers become effective time-series forecasters, and why? In this paper, we show that cross-modal transfer arises because

safetyarxiv-cs-lg

Metaphors in Literary Post-Editing: Opening Pandora's Box?

arXiv:2605.21178v1 Announce Type: new Abstract: This paper investigates how post-editors of literary texts react and respond to the way metaphors have been translated by Neu ral Machine Translation (N

Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces

arXiv:2509.22963v3 Announce Type: replace Abstract: Reinforcement learning (RL) struggles to scale to large, combinatorial action spaces common in many real-world problems. This paper introduces a nov

safetyarxiv-cs-lg

Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning

arXiv:2605.20300v1 Announce Type: new Abstract: In this paper, we propose a robust subspace-constrained quadratic model (SCQM) for learning low-dimensional structure from high-dimensional data. Buildi