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arXiv:2605.00087v1 Announce Type: cross Abstract: Many recent news reports have claimed that content generated by large language models (LLMs) is taking over the web. However, these claims are typical

tutorialsarxiv-cs-lg

Depth-Guided Privacy-Preserving Visual Localization Using 3D Sphere Clouds

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DGX agent

arXiv:2605.00562v1 Announce Type: new Abstract: The emergence of deep neural networks capable of revealing high-fidelity scene details from sparse 3D point clouds has raised significant privacy concer

local-aiarxiv-cs-cv

4 May 2026

Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling

arXiv:2510.00233v2 Announce Type: replace Abstract: Scientific machine learning has enabled the extraction of physical insights and data-driven modeling of high-dimensional spatiotemporal data, yet ac

DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion

arXiv:2504.18015v4 Announce Type: replace-cross Abstract: Face recognition poses serious privacy risks due to its reliance on sensitive and immutable biometric data. While modern systems mitigate priv

Applications

Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers

arXiv:2503.06740v2 Announce Type: replace Abstract: Gaussian Splatting has become a popular technique for various 3D Computer Vision tasks, including novel view synthesis, scene reconstruction, and dy

applicationsarxiv-cs-cv

Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance

arXiv:2507.18654v2 Announce Type: replace-cross Abstract: Diffusion models are powerful tools for sampling from high-dimensional distributions by progressively transforming pure noise into structured

Directed Social Regard: Surfacing Targeted Advocacy, Opposition, Aid, Harms, and Victimization in Online Media

arXiv:2605.00776v1 Announce Type: new Abstract: The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpful

researcharxiv-cs-cl

Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces

arXiv:2601.01082v5 Announce Type: replace Abstract: Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-spec

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers

arXiv:2405.13901v4 Announce Type: replace Abstract: Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initializ

Agents

Disentangled Control of Multi-Agent Systems

arXiv:2511.05900v3 Announce Type: replace-cross Abstract: This paper develops a general framework for multi-agent control synthesis, which applies to a wide range of problems with convergence guarante

agentsarxiv-cs-ro

Disentangled Safety Adapters Enable Efficient Guardrails and Flexible Inference-Time Alignment

arXiv:2506.00166v2 Announce Type: replace-cross Abstract: Existing paradigms for ensuring AI safety, such as guardrail models and alignment training, often compromise either inference efficiency or de

safetyarxiv-cs-cl

Distance-Aware Error for Spline Networks: A Bottom-Up Approach to Uncertainty

arXiv:2501.04757v2 Announce Type: replace-cross Abstract: We develop a new class of distance-aware error bounds that tightly characterize the approximation error of spline neural networks. Our bottom-

Distance metric learning for conditional anomaly detection

arXiv:2605.00490v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection f

Diversity in Large Language Models under Supervised Fine-Tuning

arXiv:2605.00195v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversit

DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets

arXiv:2605.00675v1 Announce Type: new Abstract: Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to comm

Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive

model-releasesarxiv-cs-ro

arXiv:2605.00066v1 Announce Type: new Abstract: Open-loop evaluation offers fast, reproducible assessment of autonomous driving planners, but its ability to predict real closed-loop driving performanc

Tutorials

Doubly robust identification of treatment effects from multiple environments

arXiv:2503.14459v2 Announce Type: replace-cross Abstract: Practical and ethical constraints often require the use of observational data for causal inference, particularly in medicine and social scienc

tutorialsarxiv-cs-lg

Hardware

DPU or GPU for Accelerating Neural Networks Inference -- Why not both? Split CNN Inference

arXiv:2605.00174v1 Announce Type: cross Abstract: Video and image streaming on edge devices requires low latency. To address this, Neural Networks (NNs) are widely used, and prior work mainly focuses

hardwarearxiv-cs-cv

Driving with A Thousand Faces: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving

arXiv:2602.18757v2 Announce Type: replace Abstract: Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single average driving style, neglecti

Dynamic-TD3: A Novel Algorithm for UAV Path Planning with Dynamic Obstacle Trajectory Prediction

arXiv:2605.00059v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) finds extensive application in autonomous drone navigation within complex, high-risk environments. However, its practi

safetyarxiv-cs-ro

Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation

arXiv:2410.04299v2 Announce Type: replace Abstract: Incorporating a priori physics knowledge into machine learning leads to more robust and interpretable algorithms. In this work, we combine deep lear

E^2DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation

arXiv:2605.00159v1 Announce Type: new Abstract: In reinforcement learning (RL) for robotic manipulation, the Decision Transformer (DT) has emerged as an effective framework for addressing long-horizon

local-aiarxiv-cs-ro

EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure

arXiv:2605.00733v1 Announce Type: cross Abstract: Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint

Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms

arXiv:2309.06577v5 Announce Type: replace Abstract: We present two algorithms to initialize layers of tensorized neural networks and general tensor network algorithms using partial computations of the

Tutorials

Efficient Mutation Testing of Quantum Machine Learning Models

arXiv:2605.00107v1 Announce Type: cross Abstract: Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer par

tutorialsarxiv-cs-lg

Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers

arXiv:2605.00296v1 Announce Type: new Abstract: Plant phenology-the study of recurrent life cycle events-is essential for understanding ecosystem dynamics and their responses to climate change impacts

EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement

arXiv:2605.00628v1 Announce Type: cross Abstract: Text-to-SQL enables non-expert users to query databases in natural language, yet real-world schemas often suffer from ambiguous, abbreviated, or incon

local-aiarxiv-cs-cl

Elimination Templates in Macaulay2

arXiv:2605.00278v1 Announce Type: cross Abstract: We introduce the package exttt{EliminationTemplates} for the Macaulay2 computer algebra system, which provides tools for constructing automatic solver

Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models

arXiv:2605.00321v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies often fail under distribution shift, suggesting that decisions may depend on spurious visual correlations rather t

researcharxiv-cs-ro

End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer

arXiv:2605.00503v1 Announce Type: new Abstract: Autoregressive image modeling relies on visual tokenizers to compress images into compact latent representations. We design an end-to-end training pipel

Escaping Mode Collapse in LLM Generation via Geometric Regulation

arXiv:2605.00435v1 Announce Type: new Abstract: Mode collapse is a persistent challenge in generative modeling and appears in autoregressive text generation as behaviors ranging from explicit looping

researcharxiv-cs-cl

Estimating LLM Grading Ability and Response Difficulty in Automatic Short Answer Grading via Item Response Theory

arXiv:2605.00238v1 Announce Type: new Abstract: Automated short answer grading (ASAG) with large language models (LLMs) is commonly evaluated with aggregate metrics such as macro-F1 and Cohen's kappa.

safetyarxiv-cs-cl

Applications

Evaluating Legal Reasoning Traces with Legal Issue Tree Rubrics

arXiv:2512.01020v2 Announce Type: replace-cross Abstract: Evaluating the quality of LLM-generated reasoning traces in expert domains (e.g., law) is essential for ensuring credibility and explainabilit

applicationsarxiv-cs-cl

Evaluating the Architectural Reasoning Capabilities of LLM Provers via the Obfuscated Natural Number Game

arXiv:2605.00677v1 Announce Type: new Abstract: While Large Language Models have achieved notable success on formal mathematics benchmarks such as MiniF2F, it remains unclear whether these results ste

Event-based Civil Infrastructure Visual Defect Detection: ev-CIVIL Dataset and Benchmark

arXiv:2504.05679v2 Announce Type: replace Abstract: Small unmanned aerial vehicle (UAV)-based visual inspections are a more efficient alternative to manual methods for examining civil structural defec

ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation

model-releasesarxiv-cs-cl

arXiv:2507.14201v3 Announce Type: replace-cross Abstract: We present ExCyTIn-Bench, the first benchmark to Evaluate an LLM agent X on the task of Cyber Threat Investigation through security questions

Exploring LLM biases to manipulate AI search overview

arXiv:2605.00012v1 Announce Type: cross Abstract: Modern large language models (LLMs) are used in many business applications in general, and specifically in web search systems and applications that ge

safetyarxiv-cs-cl

Exploring the Limits of End-to-End Feature-Affinity Propagation for Single-Point Supervised Infrared Small Target Detection

arXiv:2605.00722v1 Announce Type: new Abstract: Single-point supervised infrared small target detection (IRSTD) drastically reduces dense annotation costs. Current state-of-the-art (SOTA) methods achi

Exploring the System 1 Thinking Capability of Large Reasoning Models

model-releasesarxiv-cs-cl

arXiv:2504.10368v4 Announce Type: replace Abstract: This paper explores the system 1 thinking capability of Large Reasoning Models (LRMs), the intuitive ability to respond efficiently with minimal tok

Fair Dataset Distillation via Cross-Group Barycenter Alignment

arXiv:2605.00185v1 Announce Type: new Abstract: Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different dem

safetyarxiv-cs-lg

Fairness of Classifiers in the Presence of Constraints between Features

arXiv:2605.00592v1 Announce Type: new Abstract: In Machine Learning, an accepted definition of fairness of a decision taken by a classifier is that it should not depend on protected features, such as

safetyarxiv-cs-lg

Faithful Extreme Image Rescaling with Learnable Reversible Transformation and Semantic Priors

arXiv:2605.00605v1 Announce Type: new Abstract: Most recent extreme rescaling methods struggle to preserve semantically consistent structures and produce realistic details, due to the severely ill-pos

FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources

arXiv:2605.00011v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial resea

Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration

arXiv:2605.00578v1 Announce Type: new Abstract: Federated learning (FL) offers a promising framework for collaborative digital pathology by enabling model training across institutions. However, real-w

local-aiarxiv-cs-cv

Federated Learning with Hypergradient-based Online Update of Aggregation Weights

arXiv:2605.00458v1 Announce Type: new Abstract: Federated learning using mobile and Internet of Things devices requires not only the ability to handle heterogeneity of clients' data distributions but

Federated Weather Modeling on Sensor Data

arXiv:2605.00322v1 Announce Type: new Abstract: Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including gr

FedKPer: Tackling Generalization and Personalization in Medical Federated Learning via Knowledge Personalization

arXiv:2605.00698v1 Announce Type: cross Abstract: Federated learning (FL) holds great potential for medical applications. However, statistical heterogeneity across healthcare institutions poses a majo

local-aiarxiv-cs-lg

Feedback Lunch: Learned Feedback Codes for Secure Communications

arXiv:2510.16620v3 Announce Type: replace-cross Abstract: We consider reversely-degraded secure-communication channels, for which the secrecy capacity is zero if there is no channel feedback. Specific