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Four complementary roles organize the framework: an Organizational World defines operations management logic and an organizational behavior world model behind an enterprise from a strategic-institutional horizon; a Site World defines a physically bounded industrial optimization and execution world model from an
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Artificial Intelligence

https://arxiv.org/list/cs.AI/new
We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults

Cryptography and Security

https://arxiv.org/list/cs.CR/new
fewer | more | all New submissions (showing 32 of 32 entries) [1] arXiv:2608

Eliminating Majority Illusion is Easy

https://arxiv.org/html/2407.20187v1
In this paper, we present polynomial-time algorithms which can eliminate majority illusion in a network by altering as few connections as possible

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https://arxiv.org/pdf/1811.12628
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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/2608.02609v1
edu Abstract Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world’s oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed

Report GitHub Issue

https://arxiv.org/html/2607.22511v2
1 Introduction Language models can now generate research artifacts—conjectures, proofs, experiments, and complete papers—more rapidly than they can be evaluated

Condensed Matter

https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries

Optimal testing in a class of nonregular models

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

On the Complexity of Learning to Cooperate with Populations of Socially

https://arxiv.org/html/2407.00419v1
nl 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)

Information Theory

https://arxiv.org/list/cs.IT/new
all New submissions (showing 11 of 11 entries) [1] arXiv:2608

I INTRODUCTION

https://arxiv.org/html/2407.10811v1
For real-world solutions, only flow can be reliably collected, whereas common RL methods need more

Maximizing utility in multi-agent environments by anticipating the

https://arxiv.org/html/2407.04889v1
In multi-agent settings, the decisions of each agent can affect the utilities/losses of the other agents

Report GitHub Issue

https://arxiv.org/html/2608.02942v1
org perpetual non-exclusive license arXiv:2608

Mitigating Distribution Shift in Stock Price Data via Return-Volatility

https://arxiv.org/html/2508.20108v2
How can we address distribution shifts in stock price data to improve stock price prediction accuracy

Stabilization of First-Order Partial Integro-Differential Equations with

https://arxiv.org/html/2608.02851v1
org perpetual non-exclusive license arXiv:2608

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

Multi-Currency AMMs for Decentralized FOREX Markets: Feasibility

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

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https://arxiv.org/pdf/2006.02318
org/pdfua/ns/id/ pdfuaid PDF/UA ID Schema internal Part of PDF/UA standard part Integer PRISM metadata http://prismstandard

Stochastic Games with Minimally Bounded Action Costs

https://arxiv.org/html/2407.18010v2
We prove the game respects a dynamic programming principle and that the Markov perfect equilibrium can be computed as a limit point of a sequence of Bellman operations

Untitled Document

https://arxiv.org/html/2607.28294v1
Although classical bootstrap consistency can fail when the durations have infinite expectation, we argue that the bootstrap remains valid for percentile and reverse-percentile inference and yields asymptotically normal t-statistics

Existence of Richter–Peleg representation for general preferences

https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license arXiv:2508

Stochastic Choice with Advertising

https://arxiv.org/html/2608.03504v1
org perpetual non-exclusive license arXiv:2608

Rainfall is rough

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

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning

https://arxiv.org/html/2407.20441v1
I Introduction Training reinforcement learning (RL) algorithms is well-known to be a critically time consuming task that can require several data samples to achieve a desired level of accuracy

Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
Importantly, FINN extends beyond liquid option markets: it can be trained directly on historical spot prices to construct coherent option prices and Greeks for assets with no listed options

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

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

Bandwidth-Free Inference for Recursive Nonlinear Impulse Response

https://arxiv.org/html/2608.02943v1
org perpetual non-exclusive license arXiv:2608

1 Introduction

https://arxiv.org/html/2407.15077v2
Intuitively, partitioning policies of all agents according to their interdependence and updating joint policy batch-by-batch can effectively balance performance and efficiency

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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv