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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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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/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.
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/pdf/1811.12628
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https://arxiv.org/list/cs.CL/new
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https://arxiv.org/html/2607.22511v2
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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/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/html/2407.00419v1
Two natural assumptions in such settings are that: 1) all agents in the population are individually rational learners, and 2) when any two members of the population are paired together, with high-probability they will achieve at least the same utility as they would under some Pareto efficient equilibrium strategy
https://arxiv.org/html/2407.00575v3
This would require the manager to collect this information from the players, and then solve a large-scale optimization problem
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/pdf/2006.02318
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https://arxiv.org/html/2608.02942v1
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https://arxiv.org/html/2608.02851v1
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https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2608.02909v1
However, such variables are rarely the target of empirical analyses per se
https://arxiv.org/list/cs.CR/new
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https://arxiv.org/html/2608.03573v1
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https://arxiv.org/html/2608.03452v1
Probing five architectures, twelve trained models each, under lemma-disjoint cross-validation with controls and surface baselines, we show that the models encode the L-shaped class itself, not just its visible alternations
https://arxiv.org/html/2607.28847v1
2 Proof of Lemma 1 (Optimality of Boundary Crossing)
D
https://arxiv.org/list/cs.LG/2022-12
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[1] arXiv:2212
https://arxiv.org/html/2608.02609v1
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https://arxiv.org/html/2407.04974v1
The primary reason for focusing on this MARL setting is that off-policy learning [6, 7, 8, 9] provides a more general setting compared to its on-policy counterpart, as it allows the behavioral policy to differ from the estimated target policy
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/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv