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Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Su is with the School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China (E-mail: zhousu@ieee

Approximating Choice Data by Discrete Choice Models

https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset M Additional Empirical Application: Fishing-site Choice M

Report GitHub Issue

https://arxiv.org/html/2607.22511v2
In theoretical work, it presents a more direct risk: an incorrect theorem may be indistinguishable from a correct one until it is verified by an expert, but expert verification is slow and costly

Asymptotic Equivalence of Immediate and Deferred Acceptance

https://arxiv.org/html/2607.24970v2
In this random market, classical and influential results from the 1970s by Wilson (1972) and Knuth (1976) show, through beautiful and elementary arguments, that DA produces an expected average rank for students of log⁡n\log n

Data Structures and Algorithms

https://arxiv.org/list/cs.DS/new
per page: fewer | more | all New submissions (showing 7 of 7 entries) [1] arXiv:2608

Consensus Measures for Unstructured Biomedical Text Annotations

https://arxiv.org/html/2608.03529v1
(2024), where a human expert and an automated system might describe information in different terms

Optimal testing in a class of nonregular models

https://arxiv.org/html/2403.16413v3
org perpetual non-exclusive license arXiv:2403

On the Non-Specificity of Statistical Measures Used in Script

https://arxiv.org/html/2608.02999v1
org perpetual non-exclusive license arXiv:2608

United We Stand: Decentralized Multi-Agent Planning With Attrition

https://arxiv.org/html/2407.08254v2
We prove that our approach guarantees that the average joint action of all agents converges to a Nash equilibrium (NE) if every agent applies the same RM procedure in any cooperative game with a submodular utility function

On the Complexity of Learning to Cooperate with Populations of Socially

https://arxiv.org/html/2407.00419v1
We provide upper and lower bounds on the number of samples needed to learn an effective cooperation strategy

Does the Gender Wage Gap Originate at Labor Market Entry? Evidence from

https://arxiv.org/html/2608.03153v1
If large entry-level gaps and substantial selection bias were to show up anywhere, this economy is a plausible place to expect them

1 Introduction

https://arxiv.org/html/2608.02854v1
A common approach is to assess an outcome’s risk profile using a risk measure, such as Value-at-Risk, Tail Value-at-Risk, and Wang transform, that are special cases of generalised distortion risk measures (signed Choquet integral)

SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of

https://arxiv.org/html/2608.03573v1
org perpetual non-exclusive license arXiv:2608

Forthcoming in SIAM Journal on Financial Mathematics Decentralised

https://arxiv.org/html/2309.08431v3
Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP’s range of liquidity

1 Introduction

https://arxiv.org/html/2608.03486v1
Identification is achieved by exploiting variation in the conditional distributions of the mutually independent structural shocks induced by an observed exogenous variable

1 Introduction

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

Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
An LETF must trade in the direction of the day’s move at a close that also measures it, so its demand rises in price, and arbitrageurs buying ahead of it enlarge the order by trading at the print, then unload into what they manufactured

Mathematics

https://arxiv.org/list/math/new
An attack on $S$ is defendable if $S$ has a defense, which provides at least a

Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
de Abstract When a transformer learns an irregular morphological pattern, what has it learned

A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for

https://arxiv.org/html/2508.20101v1
In an empirical application, we demonstrate how the model can be applied to financial stock networks

Untitled Document

https://arxiv.org/html/2607.28294v1
We conclude with an empirical application to cryptocurrency ETFs

Distributed, Parallel, and Cluster Computing

https://arxiv.org/list/cs.DC/new
Several components are the subject of pending United States patent applications by the first author and are presented here as candidate open architectural principles [2] arXiv:2608

Mitigating Distribution Shift in Stock Price Data via Return-Volatility

https://arxiv.org/html/2508.20108v2
ReVol leverages three key strategies to mitigate these shifts: (1) normalizing price features to remove sample-specific characteristics, including return, volatility, and price scale, (2) employing an attention-based module to estimate these characteristics accurately, thereby reducing the influence of market

Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license arXiv:2607

Report GitHub Issue

https://arxiv.org/html/2608.02612v1
org perpetual non-exclusive license arXiv:2608

Optimal Execution with Passive Market Impact

https://arxiv.org/html/2607.28323v1
We formulate and solve an optimal liquidation problem in which the trader controls the aggressiveness of passive sell quotes

1 Introduction

https://arxiv.org/html/2402.03300v3
2 ArXiv Papers Seem Ineffective in Improving Mathematical Reasoning 5

Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
What distinguishes one retraction from another is only how the magnitude of an update is converted into a rotation angle within the plane spanned by the hidden state and the update

Rainfall is rough

https://arxiv.org/html/2607.27099v1
org perpetual non-exclusive license arXiv:2607

Reviving Micro Real Rigidities: The Importance of Demand ShocksThe views

https://arxiv.org/html/2608.02817v1
The authors are grateful for the invaluable feedback they received on an earlier draft from Jón Steinsson