Search: “papers”

How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs

arXiv:2606.08218v1 Announce Type: cross Abstract: Compositional priors describe the generic properties of layered functions in deep Bayesian models, where deep neural networks with random weights are

Human-Centered Benchmarking of Driver Monitoring Models

DGX agent

arXiv:2606.08123v1 Announce Type: cross Abstract: Vision-based driver monitoring systems are increasingly deployed in safety-critical intelligent transportation settings, yet they are almost always co

safetyarxiv-cs-ai

9 Jun 2026

Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control

arXiv:2606.08714v1 Announce Type: cross Abstract: Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under

iMaC: Translating Actions into Motion and Contact Images for Embodied World Models

arXiv:2606.09813v1 Announce Type: cross Abstract: Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventio

applicationsarxiv-cs-cv

Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses

arXiv:2606.08400v1 Announce Type: cross Abstract: Graduate-level research reading report assessment creates a substantial labor burden for educators. While large language models (LLMs) hold great pote

Improving User Experience with Personalized Review Ranking and Summarization

arXiv:2601.05261v2 Announce Type: replace-cross Abstract: Online consumer reviews are important decision-support resources in e-commerce, yet the increasing volume of reviews often creates information

InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs

arXiv:2606.08601v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely o

Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis

arXiv:2606.09558v1 Announce Type: cross Abstract: Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supe

Investigating the Histogram Loss in Regression

arXiv:2402.13425v3 Announce Type: replace-cross Abstract: It is becoming increasingly common in regression to train neural networks that model the entire distribution even if only the mean is required

IRAM-Omega-Q: A Computational Framework for Uncertainty Regulation in Adaptive Agents

arXiv:2603.16020v2 Announce Type: replace Abstract: Adaptive agents operating under uncertainty must do more than optimize task outputs: they must maintain a workable internal state under noise, pertu

Jas: AI-Paired Engineering as a Revival of N-Version Programming

arXiv:2606.07828v1 Announce Type: cross Abstract: I report a case study in AI-paired software engineering: five working ports of a vector illustration application across Rust, Swift, OCaml, Python, an

KITE: A Tri-Modal Transformer Integrating Text, Images, and Knowledge Graphs for Fake News Detection

model-releasesarxiv-cs-cv

arXiv:2606.07651v1 Announce Type: cross Abstract: Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manip

LAEI: Layered Autonomous Edge Intelligence Framework for Robust UAV Swarm Operations

arXiv:2606.09099v1 Announce Type: new Abstract: Autonomous UAV swarms require scalable coordination mechanisms that maintain mission performance under limited communication, environmental uncertainty,

safetyarxiv-cs-ro

Larch: Learned Query Optimization for Semantic Predicates

arXiv:2606.07923v1 Announce Type: cross Abstract: With the advent of Large Language Models (LLMs), many database systems introduced semantic operators that enabled analytical queries over unstructured

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences

arXiv:2606.07629v1 Announce Type: cross Abstract: Current approaches to aligning large language models (LLMs) aggregate diverse human preferences into a single reward signal, effectively optimizing fo

Latent Spatial Memory for Video World Models

arXiv:2606.09828v1 Announce Type: new Abstract: Video world models that maintain 3D spatial consistency across generated frames typically rely on explicit point cloud memory constructed in RGB space.

Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes

arXiv:2606.07908v1 Announce Type: new Abstract: Derivative-controlled networks based on ChainzRule (CR) combine cubic polynomial layers with a lightweight forward-mode per-layer Jacobian penalty (DREG

safetyarxiv-cs-lg

LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI

arXiv:2606.08131v1 Announce Type: cross Abstract: Conversational AI is increasingly used for advice, interpretation, reassurance, and decision support in contexts where users may be vulnerable, uncert

Learning a Semantic Calibration Network for Open-Vocabulary Semantic Segmentation

arXiv:2606.08001v1 Announce Type: new Abstract: Semantic image segmentation assigns a predefined category label to each pixel, has achieved significant progress lately. Open-Vocabulary Segmentation (O

Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.08170v1 Announce Type: new Abstract: With the evolution of large foundation models (LFMs), data-driven autonomous driving has made significant strides. However, existing paradigms still fac

LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss

arXiv:2606.07932v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become an efficient explicit representation for radiance field reconstruction and real-time novel view synthesis. Howev

Tutorials

Leveraging Morphology for Historical Script Metrological Analysis

arXiv:2606.09446v1 Announce Type: new Abstract: Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretab

tutorialsarxiv-cs-cv

Tutorials

LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry

arXiv:2606.08460v1 Announce Type: cross Abstract: Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.g., via kernel-

tutorialsarxiv-cs-lg

MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training

arXiv:2606.08788v1 Announce Type: new Abstract: Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By aligning

safetyarxiv-cs-cv

Measuring Poverty and Inequality with Reduced Data: A Machine Learning Approach Using Nigerian Household Data

arXiv:2606.07614v1 Announce Type: new Abstract: Reliable measurement of income and consumption is essential for monitoring poverty and inequality in low- and middle-income countries, yet full househol

Memetic Capture: A Pluralistic Policy Framework for Governing AI-Driven Cultural Disempowerment

arXiv:2606.07802v1 Announce Type: cross Abstract: Culture is the most insidious vector of gradual human disempowerment by AI: unlike economic or political displacement, cultural displacement attacks t

Minimal Solvers for Full-DoF Motion Estimation from Asynchronous Differential SfM

arXiv:2606.09218v1 Announce Type: new Abstract: As a bio-inspired intelligent sensor, event cameras have introduced a new paradigm in the intelligent perception of spatiotemporal information and visua

applicationsarxiv-cs-cv

Agents

MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots

arXiv:2606.07813v1 Announce Type: cross Abstract: Navigation using a monocular camera is pivotal for autonomous operation on tiny aerial robots due to their perfect balance of versatility, cost and ac

agentsarxiv-cs-cv

MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models

model-releasesarxiv-cs-ai

arXiv:2606.07706v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated strong performance across multimodal tasks, yet their safety robustness remains an open challenge. Whi

Modeling Components and Connections in Cyber-Physical Systems

arXiv:2606.09645v1 Announce Type: new Abstract: Text based configuration files for cyber-physical systems show the hierarchy of component modules well but often hide the details of connections and int

researcharxiv-cs-ro

Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs

arXiv:2506.07853v5 Announce Type: replace Abstract: Representing the temporal evolution of legal norms is a critical challenge for automated processing. While foundational frameworks exist, they lack

MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding

arXiv:2410.21747v2 Announce Type: replace Abstract: Generating lifelike human motions from descriptive texts has experienced remarkable research focus in the recent years, propelled by the emerging re

Hardware

MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning

arXiv:2606.08039v1 Announce Type: new Abstract: Robotic simulators are a cornerstone of modern research in aerial robotics, serving both as a vehicle for the development of new control algorithms and

hardwarearxiv-cs-ro

Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers

arXiv:2601.12263v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) integrate visual and textual knowledge into unified representations that increasingly underpin modern retrieval

Muon Learns More Robust and Transferable Features than Adam

arXiv:2606.09658v1 Announce Type: cross Abstract: Muon has recently emerged as a state-of-the-art optimizer for pretraining Large Language Models (LLMs) and vision classifiers. Despite its efficiency

NGram-MoSE: Efficient Remote Sensing Super-Resolution via N-Gram Context and Mixture-of-Experts

model-releasesarxiv-cs-cv

arXiv:2606.08535v1 Announce Type: new Abstract: Remote sensing applications for environmental monitoring and disaster management are frequently constrained by a spatial--temporal trade-off: imagery wi

Nonparametric LLM Evaluation from Preference Data

arXiv:2601.21816v2 Announce Type: replace Abstract: Evaluating the performance of large language models (LLMs) from human preference data is crucial for obtaining LLM leaderboards. However, many exist

applicationsarxiv-cs-lg

OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation

arXiv:2606.08548v1 Announce Type: new Abstract: Recent progress in robot manipulation has been largely driven by learning from large-scale demonstrations. For humanoid robot loco-manipulation tasks, h

safetyarxiv-cs-ro

OmniTryOn: Video Try-On Anything at Once!

model-releasesarxiv-cs-cv

arXiv:2606.08514v1 Announce Type: new Abstract: Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restr

Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

model-releasesarxiv-cs-lg

arXiv:2606.08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this pa

Orthogonality and Dimensionality in Airline Cluster Analysis using PCA and Kernel PCA

arXiv:2606.08322v1 Announce Type: new Abstract: To characterize the US airline profit cycles from 1995 to 2020, the authors of Renold et al. (2023) combine k-means clustering, principal component anal

Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks

arXiv:2606.07583v1 Announce Type: cross Abstract: Self-healing smart grids can quickly adjust their network configuration during outages to minimize power disruptions. During an outage, several action

Overcoming the Limits of Finite Difference Method; Physics-Informed Neural Network for Noisy High-Dimensional Heat Diffusion

arXiv:2606.07982v1 Announce Type: new Abstract: High-dimensional transient heat diffusion under noisy boundary conditions exposes a fundamental limitation of classical numerical methods: accuracy degr

PAFO: Pareto Fairness Optimization for Personalized Reward Modeling

arXiv:2606.07988v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences. While personalized reward models a

Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models

arXiv:2602.16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedba

Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning

arXiv:2606.08458v1 Announce Type: new Abstract: Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy condition

applicationsarxiv-cs-ro

Phantom transitions in language model fine-tuning

model-releasesarxiv-cs-ai

arXiv:2606.07559v1 Announce Type: cross Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently. The cross-entropy loss decreases

Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks

arXiv:2606.08473v1 Announce Type: new Abstract: False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when

safetyarxiv-cs-lg