Search: “papers”

Vision-Based Agile Landing on Turbulent Waters

arXiv:2605.23717v1 Announce Type: new Abstract: Autonomous landing of Unmanned Aerial Vehicles on maritime vessels is challenging due to the coupled motion of the vehicle and landing platform in open-

local-aiarxiv-cs-ro

Agents

X-TRACK: Physics-Aware xLSTM for Realistic Vehicle Trajectory Prediction

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DGX agent

arXiv:2511.00266v2 Announce Type: replace Abstract: Accurate trajectory prediction is crucial for safe and reliable autonomous driving systems, requiring models that capture long-term temporal depende

agentsarxiv-cs-lg

25 May 2026

XAttnMark: Learning Robust Audio Watermarking with Cross-Attention

model-releasesarxiv-cs-ai

arXiv:2502.04230v3 Announce Type: replace-cross Abstract: The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data

Hardware

XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms

arXiv:2605.23348v1 Announce Type: cross Abstract: AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up. Grid expansion comes with high capital

hardwarearxiv-cs-ai

ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling

arXiv:2601.21198v2 Announce Type: replace-cross Abstract: While Mixture-of-Experts (MoE) architectures substantially bolster the expressive power of large-language models, their prohibitive memory foo

local-aiarxiv-cs-ai

A Mechanistic Explanatory Strategy for XAI

arXiv:2411.01332v5 Announce Type: replace Abstract: Despite significant advancements in XAI, scholars note a persistent lack of solid conceptual foundations and integration with broader scientific dis

Applications

A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting

arXiv:2605.22390v1 Announce Type: new Abstract: Accurate wind power forecasting requires reliable uncertainty quantification, yet most existing methods report a single predictive uncertainty that conf

applicationsarxiv-cs-lg

Hardware

AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems

arXiv:2605.21560v1 Announce Type: new Abstract: Deploying neural networks on microcontroller units (MCUs) is critical for edge intelligence but remains challenging due to tight memory, storage, and co

hardwarearxiv-cs-lg

Applications

Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising

arXiv:2605.21556v1 Announce Type: new Abstract: Guaranteed display advertising is crucial for platform monetization, yet existing methods often operate under a single-slot assumption, limiting their a

applicationsarxiv-cs-lg

CacheClip: Accelerating RAG with Effective KV Cache Reuse

arXiv:2510.10129v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems suffer from severe time-to-first-token (TTFT) bottlenecks due to long input sequences. Existing KV cach

CCLab: Adversarial Testing of Learning- and Non-Learning-Based Congestion Controllers

arXiv:2605.21915v1 Announce Type: cross Abstract: Congestion controllers (CCs) are critical to network performance, and yet their robustness under adverse conditions remains insufficiently understood.

CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization

arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottleneck for traditional methods, which operate within ri

CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation

arXiv:2605.22774v1 Announce Type: new Abstract: Real-time cognitive load assessment is essential for adaptive human-computer interaction but remains challenging due to limited labeled data and poor cr

Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction

arXiv:2605.21499v1 Announce Type: cross Abstract: Grid-based neural networks such as convolutional autoencoders are widely used in dimension reduction-based surrogate models for computational fluid dy

Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning

arXiv:2605.22379v1 Announce Type: cross Abstract: With the advancement of science and technology, the importance of emotion research has become increasingly evident. Electroencephalography (EEG)-based

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary

arXiv:2510.03271v2 Announce Type: replace Abstract: Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in re

Departure from Regularity: Degree Heterogeneity and Eigengap as the Structural Drivers of ASE-LSE Latent Subspace Disagreement

arXiv:2605.22346v1 Announce Type: cross Abstract: Two of the most widely used methods for analysing graph data, Adjacency Spectral Embedding and Laplacian Spectral Embedding, often produce different r

Applications

Event-Aware Prompt Learning for Dynamic Graphs

arXiv:2510.11339v2 Announce Type: replace Abstract: Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph le

applicationsarxiv-cs-lg

Tutorials

Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education

arXiv:2605.21614v1 Announce Type: cross Abstract: Worked examples are step-by-step solutions to problems in a specific domain, offered to students to acquire domain-specific problem-solving skills. Th

tutorialsarxiv-cs-lg

FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation

arXiv:2505.20349v2 Announce Type: replace-cross Abstract: Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to

Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants

arXiv:2511.02043v4 Announce Type: replace Abstract: Attention is a fundamental building block of large language models (LLMs), so there have been many efforts to implement it efficiently. For example,

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs

arXiv:2605.21541v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate

How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability

arXiv:2605.21972v1 Announce Type: new Abstract: Unstructured magnitude pruning at high sparsity can reduce neural network accuracy to near-random performance, while labeled retraining may be unavailab

Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market

arXiv:2605.22387v1 Announce Type: new Abstract: Accurate electricity price forecasting (EPF) is essential for market participants to support operational planning and risk management, yet remains chall

Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation using Machine Learning Algorithms

arXiv:2605.22604v1 Announce Type: cross Abstract: The advent of cardless artificial intelligence (AI) banking heralds a paradigm shift in the financial landscape, offering users unprecedented security

Kernel-Based Safe Exploration in Deep Reinforcement Learning

arXiv:2605.22207v1 Announce Type: cross Abstract: Safety has been a major concern when deploying deep reinforcement learning algorithms in the real world. A promising direction that ensures that the l

Large-scale Score-based Variational Posterior Inference for Bayesian Deep Neural Networks

arXiv:2602.05873v2 Announce Type: replace Abstract: Bayesian (deep) neural networks (BNN) are often more attractive than the vanilla point-estimate deep learning in various aspects including uncertain

Long-term Fairness with Selective Labels

arXiv:2605.22291v1 Announce Type: new Abstract: Long-term fairness algorithms aim to satisfy fairness beyond static and short-term notions by accounting for the dynamics between decision-making polici

Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees

arXiv:2605.22756v1 Announce Type: new Abstract: Random forests are widely used in fields involving sensitive tabular data, but existing approaches to enforcing differential privacy (DP) typically degr

Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering

arXiv:2605.22410v1 Announce Type: new Abstract: Spectral clustering largely depends on the affinity graph, yet constructing a graph that preserves reliable local connectivity while adapting to heterog

Neural Acceleration for Graph Partitioning

arXiv:2605.21519v1 Announce Type: cross Abstract: Graph Partitioning is a critical problem in numerous scientific and engineering domains including social network analysis, VLSI design, and many more.

One LR Doesn't Fit All: Heavy-Tail Guided Layerwise Learning Rates for LLMs

arXiv:2605.22297v1 Announce Type: new Abstract: Learning rate configuration is a fundamental aspect of modern deep learning. The prevailing practice of applying a uniform learning rate across all laye

Optimization over the intersection of manifolds

arXiv:2605.22736v1 Announce Type: cross Abstract: Optimization over the intersection of two manifolds arises in a broad range of applications, but is hindered by the coupled geometry of the feasible r

Tutorials

Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning

arXiv:2605.21765v1 Announce Type: new Abstract: The practical adoption of sampling-based inference (SAI) in Bayesian neural networks (BNNs) remains limited, partly due to persistent misconceptions abo

tutorialsarxiv-cs-lg

Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices

arXiv:2605.22097v1 Announce Type: cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challen

[Re] FairDICE: A Fair Tradeoff in Multi-objective Offline RL

arXiv:2603.03454v2 Announce Type: replace Abstract: Offline Reinforcement Learning (RL) is an emerging field of RL in which policies are learned solely from demonstrations. Within offline RL, some env

Sparse Orthogonal Parameters Tuning for Continual Learning

arXiv:2411.02813v3 Announce Type: replace Abstract: Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catas

SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression

arXiv:2511.04838v2 Announce Type: replace Abstract: Molecular property regression struggles with cases in chemically relevant target ranges that are underrepresented in datasets. Standard average erro

Support-aware offline policy selection for advertising marketplaces

arXiv:2605.21736v1 Announce Type: cross Abstract: Logged advertising auctions make offline reserve-price evaluation attractive but risky. Replay tables can identify policies with large apparent yield

The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning

arXiv:2605.22800v1 Announce Type: new Abstract: Robustness, domain adaptation, photometric and occlusion invariance, compositional generalisation, temporal robustness, alignment safety, and classical

The Secretary Problem with a Stochastic Precursor

arXiv:2605.22653v1 Announce Type: cross Abstract: In learning-augmented online algorithms, predictions are usually valued for what they say: a value estimate, a solution, or an algorithmic recommendat

The Signal in the Noise: OOD Detection Through Goodness-of-Fit Testing in Factorised Latent Spaces

arXiv:2605.22496v1 Announce Type: new Abstract: Deep generative models offer a natural foundation for out-of-distribution (OOD) detection, yet prior work has shown that their assigned likelihoods are

TONIC: Token-Centric Semantic Communication for Task-Oriented Wireless Systems

arXiv:2605.21553v1 Announce Type: new Abstract: Tokens are becoming the basic units through which foundation models represent and process information for understanding and inference. However, traditio

Toward Understanding Adversarial Distillation: Why Robust Teachers Fail

arXiv:2605.21999v1 Announce Type: new Abstract: Adversarial Distillation aims to enhance student robustness by guiding the student with a robust teacher's soft labels within the min-max adversarial tr

Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models

arXiv:2605.21805v1 Announce Type: cross Abstract: State-space models (SSMs) are powerful probabilistic tools for modeling time-varying systems with latent dynamics. Inference in SSMs involves the esti

Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economy

arXiv:2603.04383v2 Announce Type: replace-cross Abstract: YouTube has evolved into a powerful platform where creators monetize their influence through affiliate marketing, raising concerns about trans

UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection

arXiv:2605.22621v1 Announce Type: cross Abstract: The detection of previously unseen network attacks remains a major challenge for intrusion detection systems. Although supervised learning methods oft

Hardware

WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving

arXiv:2512.09472v2 Announce Type: replace-cross Abstract: Deploying multiple models within shared GPU clusters is a key strategy to improve resource efficiency in large language model (LLM) serving. E

hardwarearxiv-cs-lg