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https://arxiv.org/src/2211.10435v1
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/src/2006.01331
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https://arxiv.org/pdf/2508.16279
AgentScope 1.0: A Developer-Centric Framework for Building Agentic ApplicationsDawei GaoZitao LiYuexiang XieWeirui KuangLiuyi YaoBingchen QianZhijian MaYue CuiHaohao LuoShen LiLu YiYi YuShiqi HeZhiling LuoWenmeng ZhouZhicheng ZhangXuguang

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https://arxiv.org/src/2608.03882
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https://arxiv.org/pdf/2608.03233
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https://arxiv.org/src/2006.01402
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https://arxiv.org/pdf/2608.02851v1
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https://arxiv.org/pdf/2608.02666
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https://arxiv.org/src/2006.02055
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https://arxiv.org/src/2006.02464
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https://arxiv.org/pdf/2608.03833
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https://arxiv.org/src/2006.01598
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2608.02935
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https://arxiv.org/src/2608.04003
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https://arxiv.org/src/2006.02833
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https://arxiv.org/pdf/2006.01402
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Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
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

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

Optimizing Age of Information in Vehicular Edge Computing with Federated

https://arxiv.org/html/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin

Global coal trade is resilient to maritime chokepoints

https://arxiv.org/html/2608.00328v2
6 Model definition References License: arXiv

MDGAM-Based Cooperative Task Scheduling for Communication-Constrained

https://arxiv.org/html/2608.00648v1
rl (rl)-based nco (nco) methods learn solution-construction policies directly from optimization objectives, reducing the reliance on handcrafted heuristics or labeled datasets

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
5 Real NIFTY data: adaptation helps but does not close the gap 6 Discussion 6

Efficient Multilingual Neural Machine Translation via Corpus-Driven

https://arxiv.org/html/2608.03480v1
com Arabic Institute for Translation, Algiers, Algeria CERIST, Algeria Nasredine Semmar CEA LIST, Paris-Saclay, France Hassina Aliane CERIST, Algeria Abstract The adoption of large pre-trained multilingual models for neural machine translation (MNMT) faces a major challenge: excessive memory and computational

Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
com/zaneopen/PACE 1 Introduction Reducing execution costs is a central objective for financial institutions (Bertsimas and Lo 1998)

Report GitHub Issue

https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction