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This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback
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Artificial Intelligence

https://arxiv.org/list/cs.AI/new
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly

Cryptography and Security

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

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

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

Computation and Language

https://arxiv.org/list/cs.CL/new
LG) 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

Mathematics

https://arxiv.org/list/math/new
It is natural to expect that if many pairs $(x_i,x_j)$ lie at distance close to $1$ from each other, then some clustering phenomenon must occur, implying that a significant number of these pairs are also very close to each other

Information Theory

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

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
A common approach is to close the research loop with a large language model (LLM) reviewer

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

1 Introduction

https://arxiv.org/html/2511.06451v1
3 Dimensionless evaluation metrics No–Arbitrage Score (NAS; higher is better)

On the Complexity of Learning to Cooperate with Populations of Socially

https://arxiv.org/html/2407.00419v1
Our main contribution is an upper-bound showing that, for partners drawn from a socially intelligent (consistent and compatible) population, we can learn to cooperate with far fewer samples than would be required by a pure imitation learning approach

Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
This project was begun on July 20, 2026; the present version is dated August 4, 2026

Report GitHub Issue

https://arxiv.org/html/2608.02609v1
It is not a barrier between two living communities that might learn each other’s languages, but a one-way temporal asymmetry: ancient texts can be partially decoded for modern readers, but modern users have no productive channel into the ancient script

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

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

https://arxiv.org/html/2608.03452v1
2 Membership is decodable beyond the surface forms 4

Learning to Control Unknown Strongly Monotone Games

https://arxiv.org/html/2407.00575v3
By controlling parameters in the utility functions of the players, the manager can aim to change the game such that the NE is efficient

Existence of Richter–Peleg representation for general preferences

https://arxiv.org/html/2508.08980v3
A relation admits such a representation if and only if it is strongly acyclic and its transitive closure is separable, where separability means embeddability in a preorder possessing a countable separating stratification—a countable family of pairwise disjoint subsets, none containing a strictly ranked pair, that

Stochastic Choice with Advertising

https://arxiv.org/html/2608.03504v1
555It is termed the “representative utility” in Train (2009), which can also be interpreted as the average utility of an alternative among the populatio

You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4
For these problems, it is therefore always optimal for the decision-maker to proceed as if the likelihood were correctly specified and select the resulting optimal decision rule

Maximizing utility in multi-agent environments by anticipating the

https://arxiv.org/html/2407.04889v1
(2019b); however, in general settings it is not known how to best play against mean-based learners, even if the agent knows everything about the learners, and can predict with certainty what actions they would take as a function of the history of play

Bandwidth-Free Inference for Recursive Nonlinear Impulse Response

https://arxiv.org/html/2608.02943v1
5 Why the horizon is fixed B Generated residuals and empirical quantiles B

Stochastic Games with Minimally Bounded Action Costs

https://arxiv.org/html/2407.18010v2
Since acting at each available opportunity would accumulate prohibitively large costs, the resulting decision problem is one in which players must make strategic decisions about when to execute actions in addition to their choice of action

Homogeneous Distributed Observers for Quasilinear Systems

https://arxiv.org/html/2407.05763v1
A system is said to be finite-time stable if it is Lyapunov stable and its states reach zeros at a finite instant of time (which may depend on the initial state of the system)

Report GitHub Issue

https://arxiv.org/html/2608.02942v1
On-policy distillation is a natural remedy for this mismatch, but it leaves open how far each transition should advance: matching only the teacher’s next action limits compression, while indiscriminately merging future actions can violate intermediate dependencies

Optimal testing in a class of nonregular models

https://arxiv.org/html/2403.16413v3
On the other hand, for regular statistical models, it is known that standard testing methods (such as the likelihood ratio, Wald, and score tests) can achieve certain asymptotic optimal power properties for testing general composite hypotheses (see Chapter 15 of Lehmann and Romano, 2022)

I INTRODUCTION

https://arxiv.org/html/2407.10811v1
com5Hua Wei is an assistant professor in the School of Computing and Augmented Intelligence at Arizona State University

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

Multi-Currency AMMs for Decentralized FOREX Markets: Feasibility

https://arxiv.org/html/2607.26405v1
Among other reasons, when bilateral trade between two currencies is moderate or low, neither side can sustain a deep enough market to offer competitive spreads, so traders gravitate toward an intermediate currency with high liquidity

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
4 Research Question 2: Can Our Method Address Masked Inequality

Rainfall is rough

https://arxiv.org/html/2607.27099v1
4 Rainfall is rough at large time scales 1