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https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/list/cs.AR/new
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New submissions (showing 4 of 4 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.SY/new
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New submissions (showing 8 of 8 entries)
[1] arXiv:2608
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/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/2608.02800v1
and Self-Preferencing
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https://arxiv.org/html/2608.02909v1
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https://arxiv.org/list/cs.CR/new
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New submissions (showing 32 of 32 entries)
[1] arXiv:2608
https://arxiv.org/html/2608.02616v1
However, multilingual NER schemas (PER/ORG/LOC) do not cover contact information, financial identifiers, or structured PII types, making direct transfer non-trivial
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/2607.27099v1
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arXiv:2607
https://arxiv.org/html/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2604.05327v4
Variational Preferences
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https://arxiv.org/html/2608.02943v1
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arXiv:2608
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/2407.04889v1
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
https://arxiv.org/pdf/1811.12628
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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
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
https://arxiv.org/html/2607.26188v1
org perpetual non-exclusive license
arXiv:2607