Search: “research”

Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS

arXiv:2606.07575v1 Announce Type: cross Abstract: Regulatory stress testing frameworks, including the Comprehensive Capital Analysis and Review (CCAR) and the Internal Capital Adequacy Assessment Proc

Research

Foundation Inference Models for Ordinary Differential Equations

DGX agent

arXiv:2602.08733v2 Announce Type: replace Abstract: Ordinary differential equations (ODEs) are central to scientific modelling, but inferring their vector fields from noisy trajectories remains challe

researcharxiv-cs-lg

9 Jun 2026

Fourier Neural Operators with rank-1 lattice points and hyperbolic cross

arXiv:2606.08871v1 Announce Type: cross Abstract: The Fourier neural operator (FNO) is a neural network architecture that learns mappings between function spaces. Its efficient implementation is based

Frequency-based Constrained Sampling for Interval Patterns

arXiv:2606.09666v1 Announce Type: new Abstract: Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on

Frequency Decoupled Framework for Screen Content Image Super-Resolution

arXiv:2606.09029v1 Announce Type: new Abstract: Methods based on implicit neural representations have demonstrated superior performance in Screen Content Image Super-Resolution (SCISR) . However, they

Frequency-Scale Saliency for Spectral Descriptor Analysis in 3D Shape Retrieval

arXiv:2606.07791v1 Announce Type: cross Abstract: Classical spectral descriptors such as the Heat Kernel Signature and Wave Kernel Signature are widely used for non-rigid 3D shape retrieval, yet their

From USD Scenes to Knowledge Graphs: Zero-Shot Ontology Grounding with LLMs

arXiv:2606.09134v1 Announce Type: cross Abstract: Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but the key bottleneck, grounding scene objects to form

From Validator Selection to Portfolio Collection Optimization in Proof-of-Stake Blockchains

arXiv:2606.08282v1 Announce Type: new Abstract: We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for mai

Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning

arXiv:2602.02431v2 Announce Type: replace-cross Abstract: It is folklore that reusing training data more than once can improve the statistical efficiency of gradient-based learning. While this phenome

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking

arXiv:2603.27493v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs), characterized by their event-driven computation and low power consumption, have shown great potential for energy-eff

Function-Vector Heads Are Two Populations: Writers and Cancellers in In-Context Learning

arXiv:2606.07560v1 Announce Type: cross Abstract: Function-vector (FV) heads (Todd et al., 2024) are typically identified by the magnitude of their causal contribution to in-context rule tasks, under

Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation

arXiv:2606.08866v1 Announce Type: new Abstract: CNN-based semantic segmentation networks usually rely on context heads such as ASPP, PPM, or attention modules to enlarge the receptive field. These hea

Generation Properties of Stochastic Interpolation under Finite Training Set

arXiv:2509.21925v2 Announce Type: replace-cross Abstract: This paper investigates the theoretical behavior of generative models under finite training populations. Within the stochastic interpolation g

Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals

arXiv:2606.08360v1 Announce Type: cross Abstract: Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infec

GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks

arXiv:2606.08303v1 Announce Type: new Abstract: This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Succes

Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation

arXiv:2509.11485v3 Announce Type: replace-cross Abstract: Labyrinthine stripe patterns are common in many physical systems, yet their lack of long-range order makes quantitative characterization chall

Geometry-Aware Uncertainty Quantification via Conformal Prediction on Manifolds

arXiv:2602.16015v2 Announce Type: replace Abstract: Conformal prediction gives finite-sample coverage guarantees for regression, but most standard constructions are designed for Euclidean output space

Geometry-Driven Flow Analysis of Brain Sulcal Pattern

arXiv:2606.08404v1 Announce Type: new Abstract: Cortical folding reflects coordinated neurodevelopmental processes and is increasingly recognized as a sensitive marker of neurological disease. However

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series

arXiv:2606.07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic

Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling

arXiv:2606.09476v1 Announce Type: new Abstract: Hindsight relabeling usually turns achieved future states into exact goals, which can overconstrain offline robot learning when task success depends onl

researcharxiv-cs-ro

Graph-GRPO: Training Graph Flow Models with Reinforcement Learning

arXiv:2603.10395v2 Announce Type: replace Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, aka

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems

arXiv:2606.09432v1 Announce Type: new Abstract: Modeling interacting dynamical systems requires capturing spatial interactions alongside long-range temporal dependencies. Graph neural networks (GNNs)

GraspFoM: Towards Reconstruction-Driven Robotic Grasping with 3D Foundation Priors

arXiv:2606.08440v1 Announce Type: cross Abstract: Robotic grasping is a fundamental capability in robotic manipulation. Yet grasping remains challenging under partial observations. Reliable grasping d

Gravity-guided Contact Dynamics Estimation from 3D Human Motions

arXiv:2606.08133v1 Announce Type: new Abstract: Ground contact forces acting on the human body, are crucial for biomechanics studies or sport performance analysis. Prior methods rely on force plates o

HACK++: Towards More Effective Head-Aware Key-Value Compression for Efficient Visual Autoregressive Modeling

arXiv:2606.08302v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality generation with substantially fewer decoding steps. How

Harmonia: End-to-End RAG Serving Optimization

arXiv:2505.07833v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipe

Hiding in Plain Floats: Steganographic Carriers for Indirect Prompt and Content Injection

arXiv:2606.08403v1 Announce Type: cross Abstract: Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views. We study a reproducible LLM01-

How Context Shapes Truth: Geometric Transformations of Statement-level Truth Representations in LLMs

arXiv:2601.06599v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often encode whether a statement is true as a vector in their residual stream activations. These vectors, also kn

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

How Many Counterfactuals Does It Take? Probing VLM Hallucinations Through Circuits and Causal Effects

arXiv:2606.08777v1 Announce Type: cross Abstract: Visual Language Models (VLMs) are known to produce hallucinated predictions that are not grounded in visual evidence, yet existing approaches lack a p

How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Models

arXiv:2606.08164v1 Announce Type: new Abstract: MRI preprocessing defines the input distribution seen by brain MRI foundation models, yet it is usually treated as routine data cleaning rather than a m

How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing

arXiv:2603.13259v2 Announce Type: replace-cross Abstract: When a decoder-only transformer is forced to process matched correct and incorrect single-token continuations of a factual query, the two path

Hummus: A Dataset of Humorous Multimodal Metaphor Use

arXiv:2504.02983v3 Announce Type: replace-cross Abstract: Metaphor and humor share a lot of common ground, and metaphor is one of the most common humorous mechanisms. This study focuses on the humorou

Hyperflux: Pruning Reveals Importance

arXiv:2504.05349v4 Announce Type: replace-cross Abstract: Network pruning is used to reduce inference latency and power consumption in large neural networks. However, most methods focus on empirical r

'I understand your perspective': LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory

arXiv:2606.08076v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains

I Was Scrolling and Then I Saw a Pregnant Strawberry

arXiv:2606.09589v1 Announce Type: cross Abstract: AI minidramas (also known as fruit dramas) are short, algorithmically distributed generative AI video series featuring anthropomorphized characters th

IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal

arXiv:2606.09347v1 Announce Type: new Abstract: Synthetic aperture radar (SAR)-assisted optical cloud removal aims to recover surface information obscured by clouds in optical remote sensing images by

Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence

arXiv:2606.07914v1 Announce Type: cross Abstract: We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent component

Illumination-Invariant Anomaly Detection for Sub-Canopy UAV Multispectral Point Clouds

arXiv:2606.09111v1 Announce Type: new Abstract: Unmanned Aerial Vehicle (UAV) multispectral point clouds (MPC) provide high-dimensional spatial-spectral data for sub-canopy target detection; however,

IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval

arXiv:2606.08144v1 Announce Type: new Abstract: Composed Video Retrieval (CVR) is designed to retrieve a target video that matches a reference video modified by a modification text. While existing met

Implicit Causal Graph Construction in Text via Chain Discovery

arXiv:2606.07525v1 Announce Type: cross Abstract: Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by

Improved Analysis of the Accelerated Noisy Power Method with Applications to Decentralized PCA

arXiv:2602.03682v2 Announce Type: replace-cross Abstract: We analyze the Accelerated Noisy Power Method, an algorithm for Principal Component Analysis in the setting where only inexact matrix-vector p

Improved Convergence Analysis of Topology Dependence in Decentralized SGD

arXiv:2606.09154v1 Announce Type: new Abstract: Decentralized SGD is a fundamental algorithm in decentralized learning, although the influence of an underlying network topology on its convergence beha

Improving Multimodal Reasoning via Worst Dimension Optimization

arXiv:2606.07801v1 Announce Type: new Abstract: Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the c

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

In-Context Learning for Latent Space Bayesian Optimization

arXiv:2606.09664v1 Announce Type: new Abstract: Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects

Inference-Time Conformal Reasoning with Valid Factuality Control for Large Language Models

arXiv:2606.08831v1 Announce Type: new Abstract: Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node corre

Inferring hidden forcing in a biological oscillator using Kolmogorov-Arnold networks

arXiv:2606.08479v1 Announce Type: new Abstract: Inferring the forces that drive a dynamical system from partial observations is a fundamental challenge across physics, particularly when distinct under