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https://arxiv.org/html/2608.02851v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
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/2607.26405v1
After World War II, the stable US economy with its dollar attached to the gold standard made it an attractive peg codified through the 1944 Bretton Woods Agreement
https://arxiv.org/html/2608.02800v1
com in China, Walmart Marketplace and Target Plus in the United States, Otto in Germany, Flipkart in India, and Rakuten in Japan
https://arxiv.org/html/2508.20108v2
Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0
https://arxiv.org/list/cs.CG/new
PR)
The Johnson--Lindenstrauss lemma asserts that every set of $n$ points in $d$-dimensional Euclidean space embeds into $O(\varepsilon^{-2}\log n)$-dimensional Euclidean space with distortion at most $1+\varepsilon$
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/html/2508.20101v1
The model combines classical GARCH(pp, qq) dynamics with spatially correlated innovations and spatially varying parameters, estimated using local likelihood methods
https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3
https://arxiv.org/list/cs.LG/2022-12
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Machine Learning
Authors and titles for December 2022
Total of 2004 entries : 1
https://arxiv.org/list/cs.DS/new
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Data Structures and Algorithms
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https://arxiv.org/html/2608.03573v1
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https://arxiv.org/html/2608.02943v1
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Hardware Architecture
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The physical world is not changed by a reconfiguration of the observer, but its complex representation in the observation world is
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Systems and Control
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https://arxiv.org/html/2407.00419v1
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Abstract
Artificially intelligent agents deployed in the real-world will require the ability to reliably cooperate with humans (as well as other, heterogeneous AI agents)
https://arxiv.org/list/cs.CR/new
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Cryptography and Security
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https://arxiv.org/html/2407.08254v2
Results suggest that, in the presence of frequent failures, our solution improves substantially over the best existing approaches in terms of global utility and scalability
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/2407.20441v1
However, in real-world multi-agent systems, inter-node communication imposes strong constraints, such as limited link capacity and transmission delays [4, 5, 6]
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
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/2407.20187v1
1 Introduction
1.1 The Illusion Elimination Problem
1.2 Our Contributions.
1.3 Related Work.
2 Preliminaries
3 Majority Illusion Addition Elimination (MIAE)
3.1 A First Attempt
3.2 A Second Attempt
3.3 A TDI System
3.4 A Polynomial Time
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
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/2407.04889v1
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