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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
from the top result below
https://arxiv.org/list/cs.AI/new
Extending the pattern-generation paradigm of Neural Cellular Automata (NCA), it navigates the degenerate Boolean search space to satisfy a computational task, rather than regenerating a fixed target state
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/2607.28847v1
4 Positioning and Contribution
3 The Effort-Centric Fairness Framework
3
https://arxiv.org/list/cs.CL/new
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https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/html/2607.22511v2
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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
https://arxiv.org/html/2608.03504v1
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arXiv:2608
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/2608.02943v1
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arXiv:2608
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/2608.01366v2
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arXiv:2608
https://arxiv.org/html/2407.00419v1
If we make no assumptions about the target population, we can do little more than attempt to mimic observed behavior as closely as possible, reducing the problem to one of imitation learning
https://arxiv.org/html/2407.00575v3
Even worse, the manager would need to collect the parameters periodically if they are time-varying, and then solve the large-scale optimization problem again
https://arxiv.org/html/2608.03573v1
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arXiv:2608
https://arxiv.org/html/2608.02609v1
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https://arxiv.org/html/2508.08980v3
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arXiv:2508
https://arxiv.org/html/2608.02975v1
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Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large reasoning models (LRMs) promise even greater improvements
https://arxiv.org/html/2608.02942v1
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arXiv:2608
https://arxiv.org/html/2608.02851v1
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arXiv:2608
https://arxiv.org/html/2403.16413v3
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arXiv:2403
https://arxiv.org/html/2608.02909v1
However, such variables are rarely the target of empirical analyses per se
https://arxiv.org/list/cs.LG/2022-12
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[1] arXiv:2212
https://arxiv.org/html/2508.20108v2
Normalization for Accurate Prediction
1 Introduction
2 Related Works
2.1 Geometric Brownian Motion
2.2 Deep Learning-Based Stock Price Prediction
2.3 Methods for Addressing Distribution Shift
3 Proposed Method
3.1 Overview
3.2 Return
https://arxiv.org/html/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/html/2607.26405v1
& Optimal Design
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https://arxiv.org/html/2412.12213v2
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https://arxiv.org/html/2604.05327v4
This version:
I would like to thank Xu Cheng, Frank Diebold, Wayne Gao, George Mailath, and seminar participants at Chicago Booth, Paris Econometrics Seminar, Toulouse School of Economics, and the University of Pennsylvania for valuable discussions and comments that substantially improved this article
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
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