Search: “papers”

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

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

DGX agent

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

model-releasesarxiv-cs-cl

22 May 2026

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

researcharxiv-cs-cl

Tutorials

Gated DeltaNet-2 is almost exactly RWKV-7's DPLR recurrence, not acknowledging the elephant in the room 🙂

tutorialsjeremy-howard--x

Gated DeltaNet-2 is almost exactly RWKV-7's DPLR recurrence, not acknowledging the elephant in the room 🙂 Gated DeltaNet-2 is here. 🚀 🔥 New paper: Gated DeltaNet-2: Decoupling Erase and Write in Linea

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

Tutorials

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

researcharxiv-cs-ai

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

Agents

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

Agents

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

Tutorials

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

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.

researcharxiv-cs-cl

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

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

researcharxiv-cs-cl

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

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

Applications

Spectral bandits for smooth graph functions with applications in recommender systems

arXiv:2605.20552v1 Announce Type: cross Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs

applicationsarxiv-cs-lg

Local Ai

The General Theory of Localization Methods

arXiv:2605.20635v1 Announce Type: new Abstract: This paper proposes a general machine learning framework called the localization method, which is fundamentally built on two core concepts: localization

local-aiarxiv-cs-lg

Why Aggregate Accuracy is Inadequate for Evaluating Fairness in Law Enforcement Facial Recognition Systems

arXiv:2603.28675v2 Announce Type: replace Abstract: Facial recognition systems are increasingly deployed in law enforcement and security contexts, where algorithmic decisions can carry significant soc

safetyarxiv-cs-cv

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment

arXiv:2506.14148v2 Announce Type: replace-cross Abstract: This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair a

Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments

arXiv:2605.19202v1 Announce Type: cross Abstract: This paper addresses the problem of using a deep Reinforcement Learning (RL)-based low-level Quadrotor controller within an autonomous Quadrotor navig

safetyarxiv-cs-ai

Are Watermarked Images Editable? SafeMark for Watermark-Preserving Text-Guided Image Editing

arXiv:2605.19511v1 Announce Type: new Abstract: This paper investigates a fundamental yet underexplored question: can watermarked images remain editable without compromising watermark integrity? We pr

researcharxiv-cs-cv

AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration

model-releasesarxiv-cs-ai

arXiv:2605.20025v1 Announce Type: new Abstract: Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple per

Chunking German Legal Code

model-releasesarxiv-cs-ai

arXiv:2605.19806v1 Announce Type: cross Abstract: This paper investigates chunking strategies for retrieval-augmented generation on German statutory law, using the German Civil Code as a structured be

Compliant Explicit Reference Governor for Contact Friendly Robotic Manipulators

arXiv:2504.09188v2 Announce Type: replace Abstract: This paper introduces the Compliant Explicit Reference Governor (CERG), a modular reference management system that enables robots to interact physic

safetyarxiv-cs-ro

Convergence of Consensus-Based Particle Methods for Nonconvex Bi-Level Optimization

arXiv:2605.19667v1 Announce Type: cross Abstract: In this paper, we study a consensus-based optimization method for nonconvex bi-level optimization, where the objective is to minimize an upper-level f

Do Better Volatility Forecasts Lead to Better Portfolios? Evidence from Graph Neural Networks

arXiv:2605.19278v1 Announce Type: cross Abstract: This paper tests whether graph neural networks improve realized volatility forecasts and whether those forecasts improve portfolio performance. Using

Factor Augmented High-Dimensional SGD

arXiv:2605.19291v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is a fundamental optimization algorithm widely used in modern machine learning. In this paper, we propose Factor-Aug

From Llama to Cria: Scaling Down Neural Networks via Neuron-Level Spectral Structural Importance Evaluation

model-releasesarxiv-cs-cv

arXiv:2605.18860v1 Announce Type: cross Abstract: This paper proposes a neuron pruning framework based on neuron-level spectral structural importance evaluation. Given a trained neural network, we rec

Local Ai

Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation

arXiv:2605.19629v1 Announce Type: cross Abstract: In this paper, we establish Berry-Esseen-type bounds for federated linear stochastic approximation (LSA). Our results provide the first federated Gaus

local-aiarxiv-cs-lg

Hardware

HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics

arXiv:2605.19565v1 Announce Type: cross Abstract: This paper describes the first-ever open-source high-fidelity CFD dataset of a high-lift aircraft for the purpose of AI surrogate model development. T

hardwarearxiv-cs-lg

How Does Overparameterization Affect Machine Unlearning of Deep Neural Networks?

arXiv:2503.08633v2 Announce Type: replace Abstract: Machine unlearning is the task of updating a trained model to forget specific training data without retraining from scratch. In this paper, we inves

Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions

model-releasesarxiv-cs-lg

arXiv:2605.19562v1 Announce Type: cross Abstract: This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV)

Applications

LP-Eval: Rubric and Dataset for Measuring the Quality of Legal Proposition Generation

arXiv:2605.19815v1 Announce Type: cross Abstract: Legal proposition generation is central to legal reasoning and doctrinal scholarship, yet remain under-examined in Legal NLP. This paper investigates

applicationsarxiv-cs-ai

On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization

model-releasesarxiv-cs-lg

arXiv:2601.12238v4 Announce Type: replace-cross Abstract: In this paper, we provide a comprehensive theoretical analysis of Stochastic Gradient Descent (SGD) and its momentum variants (Polyak Heavy-Ba

Resilient Byzantine Agreement with Predictions

model-releasesarxiv-cs-ai

arXiv:2605.19452v1 Announce Type: cross Abstract: This paper studies the Byzantine Agreement problem where the nodes have access to a predictor that flags nodes for suspicion of faulty (Byzantine) beh

Return of Frustratingly Easy Unsupervised Video Domain Adaptation

arXiv:2605.19510v1 Announce Type: new Abstract: Unsupervised video domain adaptation (UVDA) is a practical but under-explored problem. In this paper, we propose a frustratingly easy UVDA method, calle

researcharxiv-cs-cv

Robustness and Regularization in Hierarchical Re-Basin

arXiv:2510.09174v3 Announce Type: replace Abstract: This paper takes a closer look at Git Re-Basin, an interesting new approach to merge trained models. We propose a hierarchical model merging scheme

Sparse Mixture-of-Experts Routing in Visual Diffusion Transformers:Diagnosis, Boundary Calibration and Evolutionary Roadmap from Routing Collapse to Selective Deadlock

arXiv:2605.19378v1 Announce Type: new Abstract: This paper systematically diagnoses the training failure modes of Token-Choice sparse Mixture-of-Experts (MoE) on video Diffusion Transformers. Starting

researcharxiv-cs-cv