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arXiv:2605.01107v1 Announce Type: new Abstract: Neural networks transform data through learned representations whose geometry affects separation, contraction, and generalization. Recent work studies t

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Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models

DGX agent

arXiv:2605.02715v1 Announce Type: cross Abstract: Self-supervised speech models (S3Ms) achieve strong downstream performance, yet their learned representations remain poorly understood under natural a

researcharxiv-cs-lg

5 May 2026

Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding

arXiv:2605.00894v1 Announce Type: new Abstract: Vision foundation models (VFMs), such as DINOv3, provide rich semantic representations that are promising for computational pathology. However, many cur

DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration

arXiv:2503.15984v3 Announce Type: replace Abstract: Modern image restoration and super-resolution methods utilize deep learning due to its superior performance compared to traditional algorithms. Howe

applicationsarxiv-cs-cv

Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis

arXiv:2605.01848v1 Announce Type: new Abstract: Synthesizing longitudinal medical images at controllable disease stages while preserving patient-specific anatomy is hindered by the entanglement of pat

DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation

arXiv:2411.14295v3 Announce Type: replace Abstract: Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video

Distributional Causal Mediation via Conditional Generative Modeling

arXiv:2605.01765v1 Announce Type: cross Abstract: Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional ch

Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation

arXiv:2605.01846v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs), where correct answers should ideally be uniformly distr

safetyarxiv-cs-cl

DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstruction

model-releasesarxiv-cs-cv

arXiv:2601.15416v2 Announce Type: replace Abstract: Sparse-view Cone-Beam Computed Tomography reconstruction from limited X-ray projections remains a challenging problem in medical imaging due to the

DynamicPO: Dynamic Preference Optimization for Recommendation

arXiv:2605.00327v1 Announce Type: cross Abstract: In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferen

researcharxiv-cs-ai

Dynamics Aware Quadrupedal Locomotion via Intrinsic Dynamics Head

arXiv:2605.01227v1 Announce Type: new Abstract: Quadrupedal locomotion plays a critical role in enabling agile, versatile movement across complex terrains. Understanding and estimating the underlying

Dynamics Distillation for Efficient and Transferable Control Learning

arXiv:2605.01516v1 Announce Type: new Abstract: Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, prope

Agents

DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving

arXiv:2603.19675v2 Announce Type: replace Abstract: Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically

agentsarxiv-cs-cv

Local Ai

DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation

arXiv:2605.02759v1 Announce Type: cross Abstract: Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their app

local-aiarxiv-cs-cv

E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory

arXiv:2601.21714v2 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requir

Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions

arXiv:2410.08491v2 Announce Type: replace Abstract: Automated vehicles promise to enhance transportation safety and efficiency. However, ensuring their reliability in real-world conditions remains cha

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment

arXiv:2605.00731v1 Announce Type: cross Abstract: While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi-domain heterogeneous graphs (MDHG

Enhanced LLM Reasoning by Optimizing Reward Functions with Search-Driven Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2605.02073v1 Announce Type: new Abstract: Mathematical reasoning is a key benchmark for large language models. Reinforcement learning is a standard post-training mechanism for improving the reas

Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization

model-releasesarxiv-cs-cl

arXiv:2605.02011v1 Announce Type: new Abstract: Automating the drafting of judgment documents is pivotal to judicial efficiency, yet it remains challenging due to the dual requirements of comprehensiv

Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters

arXiv:2605.02867v1 Announce Type: new Abstract: Despite significant advances in Reinforcement Learning (RL), model performance remains highly sensitive to algorithm and hyperparameter configurations,

Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins

arXiv:2510.04346v2 Announce Type: replace-cross Abstract: Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-s

researcharxiv-cs-lg

Evidence-Based Landing Site Selection and Vison-Based Landing for UAVs in Unstructured Environments

arXiv:2605.01432v1 Announce Type: new Abstract: Autonomous landing in cluttered or unstructured environments remains a safety-critical challenge for unmanned aerial vehicles (UAVs), particularly under

Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments

arXiv:2605.02165v1 Announce Type: new Abstract: Conventional federated learning assumes that greater learner participation improves training performance, by leveraging abundant, independently generate

Exploring Data-Free LoRA Transferability for Video Diffusion Models

arXiv:2605.01929v1 Announce Type: new Abstract: Video diffusion models leveraging step distillation or causal distillation have achieved remarkable performance. However, adapting existing LoRAs to the

ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction

arXiv:2605.02464v1 Announce Type: new Abstract: Single-image HDR reconstruction aims to recover high dynamic range radiance from a single low dynamic range (LDR) input, but remains highly ill-posed du

Extrapolation in Statistical Learning with Extreme Value Theory

arXiv:2605.01909v1 Announce Type: cross Abstract: Extreme value theory provides rigorous theory and statistical tools for extrapolation in machine learning, particularly in settings where traditional

researcharxiv-cs-lg

Hardware

Fast Log-Domain Sinkhorn Optimal Transport with Warp-Level GPU Reductions

arXiv:2605.00837v1 Announce Type: new Abstract: Entropic regularized optimal transport (OT) via the Sinkhorn algorithm has become a fundamental tool in machine learning, yet existing implementations e

hardwarearxiv-cs-lg

FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control

arXiv:2603.12612v2 Announce Type: replace Abstract: Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a fundamental challenge, as the ''curse of dimensio

Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction

arXiv:2605.01810v1 Announce Type: new Abstract: Gestational Diabetes Mellitus (GDM) is a high-prevalence pregnancy complication that requires accurate early risk stratification to reduce maternal and

FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback

model-releasesarxiv-cs-lg

arXiv:2605.01264v1 Announce Type: cross Abstract: Traditional approaches to test case generation often involve manual effort and incur significant computational overhead. Additionally, these approache

Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training

arXiv:2605.02374v1 Announce Type: cross Abstract: Machine-generated text (MGT) detection is critical for regulating online information ecosystems, yet existing detectors often underperform in few-shot

researcharxiv-cs-cl

Agents

FitText: Evolving Agent Tool Ecologies via Memetic Retrieval

arXiv:2605.02411v1 Announce Type: cross Abstract: A semantic gap separates how users describe tasks from how tools are documented. As API ecosystems scale to tens of thousands of endpoints, static ret

agentsarxiv-cs-lg

Five must-have guides to move agents into production with Gemini Enterprise Agent Platform

model-releasesgoogle-cloud-ai

Building AI agents that work well in a demo is one thing, but running them in production requires serious infrastructure. At Google Cloud Next '26, we introduced Gemini Enterprise Agent Platform to he

FLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multi-Task Distillation Network

arXiv:2605.02137v1 Announce Type: new Abstract: Accurate flood water mapping is critical for disaster management, yet current methods struggle to fully exploit the potential of spaceborne imagery. Opt

Agents

Flux4D: Flow-based Unsupervised 4D Reconstruction

arXiv:2512.03210v2 Announce Type: replace Abstract: Reconstructing large-scale dynamic scenes from visual observations is a fundamental challenge in computer vision, with critical implications for rob

agentsarxiv-cs-cv

From Concept to Capability: Evaluating 3D Gaussian Splatting for Synthetic Scene Editing in Autonomous Driving

arXiv:2605.01995v1 Announce Type: new Abstract: The perception of an Autonomous Driving System (ADS) critically depends on relevant, comprehensive, and diverse datasets to ensure its safety while oper

From Mice to Trains: Amortized Bayesian Inference on Graph Data

arXiv:2601.02241v5 Announce Type: replace-cross Abstract: Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics

From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Release for Offline-to-Online Reinforcement Learning

arXiv:2511.03828v2 Announce Type: replace Abstract: Offline-to-online reinforcement learning (O2O RL) faces a central challenge between retaining offline conservatism and adapting to online feedback u

G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge

arXiv:2509.24276v4 Announce Type: replace Abstract: Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented genera

Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models

arXiv:2605.00906v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) aims to categorize unlabelled instances from both known and unknown classes by transferring knowledge from labelled

applicationsarxiv-cs-cv

Tutorials

GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items

arXiv:2603.14259v2 Announce Type: replace-cross Abstract: Generative recommendation (GR) has shown strong potential for sequential recommendation in an end-to-end generation paradigm. However, existin

tutorialsarxiv-cs-ai

GeoContra: From Fluent GIS Code to Verifiable Spatial Analysis with Geography-Grounded Repair

arXiv:2605.00782v1 Announce Type: cross Abstract: Reliable spatial analysis in GIScience requires preserving coordinate semantics, topology, units, and geographic plausibility. Current LLM-based GIS s

Tutorials

GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models

arXiv:2605.01829v1 Announce Type: new Abstract: Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Stand

tutorialsarxiv-cs-cv

GETA-3DGS: Automatic Joint Structured Pruning and Quantization for 3D Gaussian Splatting

arXiv:2605.02086v1 Announce Type: new Abstract: 3D Gaussian splatting (3DGS) is a state-of-the-art representation for real-time photorealistic novel-view synthesis, yet a single high-fidelity scene ty

Tutorials

GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models

arXiv:2605.01256v1 Announce Type: new Abstract: A promising paradigm for adapting instruction-tuned language models is to learn task-specific updates on a pretrained base model and subsequently merge

tutorialsarxiv-cs-cl

Global-Local Feature Decoding with Adapter-Guided SAMv2 for Salient Object Detection

arXiv:2605.02616v1 Announce Type: new Abstract: Salient Object Detection (SOD) remains an essential yet underexplored task in the era of large-scale vision models. Although foundation models like SAM

Graph-Augmented Topological Internalization with Dual-Stream Classifiers for Medical Report Generation

arXiv:2605.02376v1 Announce Type: new Abstract: Automated medical report generation, MRG, holds substantial value for alleviating radiologist workload and enhancing diagnostic efficiency. However, mai

Local Ai

Graph Federated Unlearning for Privacy Preservation

arXiv:2605.02297v1 Announce Type: new Abstract: Graph federated learning (GFL) facilitates decentralized training on distributed graph data while keeping sensitive user information local, aligning wit

local-aiarxiv-cs-lg