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https://arxiv.org/src/2006.02759
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https://arxiv.org/list/cs.DS/new
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https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3
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https://arxiv.org/html/2608.02942v1
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arXiv:2608
https://arxiv.org/html/2607.24970v2
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https://arxiv.org/html/2608.02851v1
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arXiv:2608
https://arxiv.org/pdf/2006.02602
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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
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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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
https://arxiv.org/html/2608.02909v1
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[1] arXiv:2608
https://arxiv.org/pdf/2005.13905
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/html/2407.08254v2
1 Introduction
2 Related Work
3 Problem Formulation
3.1 Multi-agent planning with attrition
4 Attritable MCTS with Regret Minimization
4.1 Overview of the A-MCTS algorithm
4.2 Regret Matching For Cooperative Coordination
4.3 Analysis and
https://arxiv.org/html/2407.06328v2
I Introduction
II Preliminaries and Problem Description
II-A Task Allocation Game Model
II-B Learning Rule, Stochastic Alarm Clock, and Evolutionary Dynamics Model
II-C Payoff Vector Estimation
III Convergence Analysis and Revision Rate
https://arxiv.org/html/2607.28294v1
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https://arxiv.org/html/2607.27099v1
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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
https://arxiv.org/html/2608.03573v1
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arXiv:2608
https://arxiv.org/html/2309.08431v3
Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision
1 Introduction
2 Constant function markets and concentrated liquidity
2.1 Constant function markets
2.2 Concentrated liquidity markets
3 The wealth of
https://arxiv.org/html/2511.06451v1
1 Data Protocol (arXiv Release)
Synthetic risk–neutral generator
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
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
https://arxiv.org/pdf/2609.18842
[{MCal7]ViP:#nPY
https://arxiv.org/list/cs.CL/new
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