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Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients

arXiv:2605.01702v1 Announce Type: new Abstract: Theoretical studies show that for any differentiable function on a compact domain, there exists a neural network that approximates both the function val

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

DGX agent

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

safetyarxiv-cs-cv

5 May 2026

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

Focus and Dilution: The Multi-stage Learning Process of Attention

arXiv:2605.01199v1 Announce Type: new Abstract: Transformer-based models have achieved remarkable success across a wide range of domains, yet our understanding of their training dynamics remains limit

Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast

model-releasesarxiv-cs-cl

arXiv:2605.01373v1 Announce Type: new Abstract: The iterative denoising paradigm of Diffusion Large Language Models (DLMs) endows them with a distinct advantage in global context modeling. However, cu

FoR-Net: Learning to Focus on Hard Regions for Efficient Semantic Segmentation

arXiv:2605.02764v1 Announce Type: new Abstract: We present FoR-Net, a lightweight architecture for semantic segmentation that focuses on identifying and enhancing hard regions. Instead of relying on h

Forager: a lightweight testbed for continual learning with partial observability in RL

arXiv:2605.01131v1 Announce Type: new Abstract: In continual reinforcement learning (CRL), good performance requires never-ending learning, acting, and exploration in a big, partially observable world

tutorialsarxiv-cs-lg

Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding

arXiv:2605.00857v1 Announce Type: cross Abstract: Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled tar

Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims

arXiv:2605.02740v1 Announce Type: cross Abstract: Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative

safetyarxiv-cs-cl

Foundations of Riemannian Geometry for Riemannian Optimization: A Monograph with Detailed Derivations

arXiv:2605.02279v1 Announce Type: cross Abstract: Riemannian geometry provides the fundamental framework for optimization on nonlinear spaces such as matrix manifolds, which arise in machine learning,

tutorialsarxiv-cs-lg

Local Ai

FractalMamba++: Scaling Vision Mamba Across Resolutions via Hilbert Fractal Geometr

arXiv:2505.14062v3 Announce Type: replace Abstract: Vision Mamba offers linear complexity for long visual sequences, yet its performance depends critically on how a two-dimensional patch grid is seria

local-aiarxiv-cs-cv

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data

arXiv:2605.01367v1 Announce Type: cross Abstract: High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlate

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

safetyarxiv-cs-cv

From Cortical Synchronous Rhythm to Brain Inspired Learning Mechanism: An Oscillatory Spiking Neural Network with Time-Delayed Coordination

arXiv:2605.01656v1 Announce Type: cross Abstract: Human cognition emerges from coordinated spiking dynamics in distributed neural circuits, where information is encoded via both firing rates and preci

From Euler to Dormand-Prince: ODE Solvers for Flow Matching Generative Models

model-releasesarxiv-cs-lg

arXiv:2605.00836v1 Announce Type: new Abstract: Sampling from Flow Matching generative models requires solving an ordinary differential equation (ODE) whose computational cost is dominated by neural n

From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity

arXiv:2605.00939v1 Announce Type: new Abstract: Traditional hallucination detection fails on 'Stubborn Hallucinations' -- errors where LLMs are confidently wrong. We propose a geometric solution: Embe

From Fold to Function: Simulation-Driven Design of Origami Mechanisms

arXiv:2511.10580v3 Announce Type: replace Abstract: Origami-inspired mechanisms can transform flat sheets into functional three-dimensional dynamic structures that are lightweight, compact, and capabl

applicationsarxiv-cs-ro

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

applicationsarxiv-cs-lg

From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals

arXiv:2605.01616v1 Announce Type: new Abstract: Human behavior is difficult to observe continuously at scale, yet it leaves measurable traces in everyday device use. We test whether encrypted smartpho

From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments

arXiv:2605.02098v1 Announce Type: new Abstract: Large-scale 3D point clouds can consist of billions of points. Even after downsampling, these point clouds are too large for modern 3D neural networks.

researcharxiv-cs-cv

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

From Where Things Are to What They Are For: Benchmarking Spatial-Functional Intelligence in Multimodal LLMs

arXiv:2605.02130v1 Announce Type: new Abstract: Human-level agentic intelligence extends beyond low-level geometric perception, evolving from recognizing where things are to understanding what they ar

FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning

model-releasesarxiv-cs-cl

arXiv:2605.01495v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding responses in external knowledge during inference. However, conve

FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework

arXiv:2605.02789v1 Announce Type: cross Abstract: Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initializati

researcharxiv-cs-cl

Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation

arXiv:2605.00938v1 Announce Type: new Abstract: Accurate modeling of commuting flows is important for urban governance, traffic planning, and resource allocation. However, the combined influence of in

applicationsarxiv-cs-lg

Fuzzy Fingerprinting Encoder Pre-trained Language Models for Emotion Recognition in Conversations: Human Assessment and Validity Study

arXiv:2605.02665v1 Announce Type: new Abstract: In Emotion Recognition in Conversations (ERC), model decisions should align with nuanced human perception and ideally provide insights on the classifica

researcharxiv-cs-cl

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

model-releasesarxiv-cs-ai

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

Agents

GA-VisAgent: A Multi-Agent application for code generation and visualization in interactive learning

arXiv:2605.01299v1 Announce Type: new Abstract: Geometric Algebra (GA) presents challenges to learners due to its highly abstract mathematical structure and complex operational rules, as translating a

agentsarxiv-cs-lg

GameScope: A Multi-Attribute, Multi-Codec Benchmark Dataset for Gaming Video Quality Assessment

arXiv:2605.01272v1 Announce Type: new Abstract: The development of video game streaming has grown rapidly, with major platforms such as YouTube and Twitch using different codecs. To support quality as

GaMMA: Towards Joint Global-Temporal Music Understanding in Large Multimodal Models

model-releasesarxiv-cs-ai

arXiv:2605.00371v1 Announce Type: cross Abstract: In this paper, we propose GaMMA, a state-of-the-art (SoTA) large multimodal model (LMM) designed to achieve comprehensive musical content understandin

Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs

arXiv:2601.22709v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post-training quantization often causes significan

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

arXiv:2502.06719v3 Announce Type: replace-cross Abstract: In this paper, we establish the non-asymptotic validity of the multiplier bootstrap procedure for constructing the confidence sets using the S

GAZE: Grounded Agentic Zero-shot Evaluation with Viewer-Level Tools and Literature Retrieval on Rare Brain MRI

arXiv:2605.00876v1 Announce Type: cross Abstract: Vision-language models (VLMs) read an image and produce text in a single forward pass, whereas radiologists typically inspect an image several times a

GD-FPS: Growth-Driven Feedforward Parameter Selection for Efficient Fine-Tuning

arXiv:2510.27359v2 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key strategy for adapting large-scale pre-trained models to downstream tasks, but existing a

GEASS: Training-Free Caption Steering for Hallucination Mitigation in Vision-Language Models

arXiv:2605.01733v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at grounded reasoning but remain prone to object hallucination. Recent work treats self-generated captions as a unif

researcharxiv-cs-cv

Gen-Searcher: Reinforcing Agentic Search for Image Generation

arXiv:2603.28767v2 Announce Type: replace Abstract: Recent image generation models have shown strong capabilities in generating high-fidelity and photorealistic images. However, they are fundamentally

General Frameworks for Conditional Two-Sample Testing

arXiv:2410.16636v2 Announce Type: replace-cross Abstract: We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accou

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

Generalized Distributional Alignment Games for Unbiased Answer-Level Fine-Tuning

arXiv:2605.02435v1 Announce Type: new Abstract: The Distributional Alignment Game framework provides a powerful variational perspective on Answer-Level Fine-Tuning (ALFT). However, standard algorithms

Generative Interfaces for Language Models

arXiv:2508.19227v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through nat

researcharxiv-cs-cl

Generative Modeling with Orbit-Space Particle Flow Matching

arXiv:2605.02222v1 Announce Type: cross Abstract: We present Orbit-Space Geometric Probability Paths (OGPP), a particle-native flow-matching framework for generative modeling of particle systems. OGPP

tutorialsarxiv-cs-cv

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

model-releasesarxiv-cs-ai

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

GEODE: Angle-Adaptive OOD Detection with Universal Scorer Compatibility

arXiv:2605.01063v1 Announce Type: cross Abstract: Outlier Exposure (OE) is among the strongest training-based OOD detectors on standard benchmarks but exhibits scorer-dependent tradeoffs (e.g., strong

researcharxiv-cs-cv

Geometric and Spectral Alignment for Deep Neural Network I

arXiv:2605.02108v1 Announce Type: new Abstract: Deep residual architectures are modeled as products of near-identity Jacobians. This paper proves deterministic quotient-geometric estimates for singula

Geometric and Spectral Alignment for Deep Neural Network II

arXiv:2605.02111v1 Announce Type: new Abstract: This paper develops the angular and static-channel component of Geometric and Spectral Alignment for residual Jacobian chains. Starting from Cartan-coor

Geometry-Aware Scene Configurations for Novel View Synthesis

arXiv:2510.09880v2 Announce Type: replace Abstract: We propose scene-adaptive strategies to efficiently allocate representation capacity for generating immersive experiences of indoor environments fro

tutorialsarxiv-cs-cv

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