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However, their rigid dataset-specific adaptation inherently restricts cross-site generalization
from the top result below

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/new
Project Page: this https URL [2] arXiv:2608

Artificial Intelligence

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

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https://arxiv.org/pdf/2608.01785
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https://blog.arxiv.org/wp-content/plugins/gutenberg/build/styles/block-library/navigation/style.min.css?m=1781685988
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.01128
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https://arxiv.org/src/2006.03720
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https://arxiv.org/src/2006.01331
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Preying on Leveraged ETFsPreliminary and incomplete. This project was

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.

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https://arxiv.org/pdf/2608.02666
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https://arxiv.org/src/2006.02085
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https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv

Report GitHub Issue

https://arxiv.org/html/2608.02609v1
× Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF

Optimal Execution with Passive Market Impact

https://arxiv.org/html/2607.28323v1
3 Target execution schedules 7 Concluding Remarks 8 Proof of Theorem 3

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https://arxiv.org/src/2608.03994
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Sphere Retraction Normalizations

https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (), Correspondence: Cheng-Fang Su () “Geometry is not true, it is advantageous

1 Introduction

https://arxiv.org/html/2508.02739v1
2 Preliminary 3 Methodology K-line Tokenization Hierarchical Autoregressive Modeling Model Pre-training Dataset Model Training Inference 4 Experiments Experimental Setup Main Results Prediction Tasks Generative Tasks Investment

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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

On the Complexity of Learning to Cooperate with Populations of Socially

https://arxiv.org/html/2407.00419v1
Our results first show that these assumptions alone are insufficient to ensure zero-shot cooperation with members of the target population

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

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

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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/src/2104.08691
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2006.00342
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Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Transmission Parameter Allocation in Multi-Gateway LoRa Networks I INTRODUCTION II RELATED WORK III SYSTEM MODEL III-A The Traffic Pattern of LoRa Networks III-B Capture Effect and Quasi-Orthogonality between SFs III-C Packet Delivery