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1 Introduction

https://arxiv.org/html/2511.06451v1

Problem statement and motivation. Thesis: risk-neutral pricing as a structured operator. From selective scan to a risk-neutral operator layer. Geometry in the loop: Q-Align and convex–monotone decoding. Spectral stability as a first-class

Maximizing utility in multi-agent environments by anticipating the

https://arxiv.org/html/2407.04889v1

behavior of other learners 1 Introduction Setting. Zero-sum games. General-sum games. 1.1 Preliminaries 1.2 Algorithms for the learner 1.3 Our results 1.3.1 Zero sum games Continuous-time games. Discrete-time games. 1.3.2 A lower bound

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https://arxiv.org/pdf/1811.12628

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Stochastic Games with Minimally Bounded Action Costs

https://arxiv.org/html/2407.18010v2

1 Introduction 2 Formulation 3 Existence of a Value and Saddle Point Equilibrium 4 Learning in Unknown Environments 4.1 Convergence using Linear Function Approximators 5 Intervention Budget Constraints 6 Discussion on Subcases 7 Related

Homogeneous Distributed Observers for Quasilinear Systems

https://arxiv.org/html/2407.05763v1

1 Introduction 2 Preliminaries 2.1 Graph Theory 2.2 Generalized Homogeneity 3 Problem Statement 4 Basic Idea of the Observer Design 5 Homogeneous Observer Design 5.1 Globally Homogeneous Distributed Observer 5.2 Locally Homogeneous

Mathematics

https://arxiv.org/list/math/new

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