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Did you mean if cam mojeek site en wikipedia org site lemmy would site:arxiv.org?

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1 Introduction

https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release) Synthetic risk–neutral generator

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https://arxiv.org/pdf/1811.12628
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Homogeneous Distributed Observers for Quasilinear Systems

https://arxiv.org/html/2407.05763v1
A system is said to be finite-time stable if it is Lyapunov stable and its states reach zeros at a finite instant of time (which may depend on the initial state of the system)

Mathematics

https://arxiv.org/list/math/new
up to 2000 entries per page: fewer | more | all New submissions (showing 219 of 219 entries) [1] arXiv:2608

Computational Geometry

https://arxiv.org/list/cs.CG/new
more | all New submissions (showing 1 of 1 entries) [1] arXiv:2608

Hardware Architecture

https://arxiv.org/list/cs.AR/new
more | all New submissions (showing 4 of 4 entries) [1] arXiv:2608

Machine Learning Dec 2022

https://arxiv.org/list/cs.LG/2022-12
2001-2004 Showing up to 50 entries per page: fewer | more | all [1] arXiv:2212

Mathematical Finance Apr 2021

https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3

Systems and Control

https://arxiv.org/list/cs.SY/new
more | all New submissions (showing 8 of 8 entries) [1] arXiv:2608

Information Theory

https://arxiv.org/list/cs.IT/new
all New submissions (showing 11 of 11 entries) [1] arXiv:2608

Mitigating Distribution Shift in Stock Price Data via Return-Volatility

https://arxiv.org/html/2508.20108v2
Normalization for Accurate Prediction 1 Introduction 2 Related Works 2.1 Geometric Brownian Motion 2.2 Deep Learning-Based Stock Price Prediction 2.3 Methods for Addressing Distribution Shift 3 Proposed Method 3.1 Overview 3.2 Return

Microstructural Foundations of Rough Noise1footnote

https://arxiv.org/html/2607.29442v2
1footnoteFootnotefootnotesFootnotes1footnote 1We are grateful to Carsten Chong for fruitful discussions on rough noise. We benefitted from comments by and discussions with Kim Christensen, Mikko Pakkanen, Bent Jesper Christensen, and Mikkel

Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related

https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit!

I INTRODUCTION

https://arxiv.org/html/2407.10811v1
II RELATED WORK II-A Traditional methods II-B RL methods. III PRELIMINARIES IV METHOD IV-A RL Setup IV-B Network design IV-C Training IV-D Theoretical Analysis V EXPERIMENT V-A Towards Real-world Dataset and Simulation V-B Benchmark

A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for

https://arxiv.org/html/2508.20101v1
Volatility in Financial Networks 1 Introduction 2 Heterogeneous Spatiotemporal GARCH model 2.1 Model Assumptions 2.2 Prediction at Unobserved Locations 2.3 Estimation of Parameter Surface 2.4 Properties of the Estimator 3 Monte Carlo

Raising Rivals’ Costs on Hybrid Platforms: The Complementarity of Fees

https://arxiv.org/html/2608.02800v1
and Self-Preferencing Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML?

Multi-Currency AMMs for Decentralized FOREX Markets: Feasibility

https://arxiv.org/html/2607.26405v1
& Optimal Design Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report

Untitled Document

https://arxiv.org/html/2607.28294v1
Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

https://arxiv.org/html/2407.20441v1
I Introduction II System Model and Problem Formulation III Main result IV Proof of the main result V Simulations VI Conclusion and Future Work Finite-Time Analysis of Asynchronous Multi-Agent TD Learning Nicolò Dal Fabbro, Arman Adibi

Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
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1 Introduction

https://arxiv.org/html/2407.15077v2
2 Problem Definition 3 Method 3.1 Monotonic Improvement of B2MAPO scheme 3.2 B2MAPO Framework 3.2.1 Batch Sequence Generation Layer 3.2.2 Batch by Batch Policy Optimization Layer 4 DAG-based B2MAPO Algorithm 4.1 Hierarchical DAG

Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially

https://arxiv.org/html/2407.04974v1
Observable Environments I Introduction II State of the art II-A Settings II-B State estimation in model-based learning II-C State estimation in model-free RL II-C1 Neural Network-based Approaches II-C2 Non-neural-based Approach II-D

Computation and Language

https://arxiv.org/list/cs.CL/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computation and Language New submissions Cross-lists Replacements See recent

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
A core calculus gives an effect-confinement lemma and six safety properties

Very Large-Scale Multi-Agent Simulation in AgentScope

https://arxiv.org/html/2407.17789v2
org perpetual non-exclusive license arXiv:2407

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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2508.11152
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https://arxiv.org/pdf/2608.02609
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