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https://arxiv.org/src/2402.03300
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https://arxiv.org/pdf/2006.01402
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https://arxiv.org/src/2006.00342
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https://arxiv.org/pdf/2211.01910
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https://arxiv.org/pdf/2006.03178
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https://arxiv.org/pdf/2006.04122
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/src/2506.01844
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https://arxiv.org/src/2006.01318
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https://arxiv.org/html/2412.12213v2
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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/2407.10811v1
Our rigid experiments show that our method has good cycle-flow relation and superior performance
https://arxiv.org/html/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin
https://arxiv.org/html/2608.00328v2
6 Model definition
References
License: arXiv
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
https://arxiv.org/html/2607.27188v1
5 Real NIFTY data: adaptation helps but does not close the gap
6 Discussion
6
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
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)
https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction
https://arxiv.org/html/2608.03086v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/list/cs.IR/new
IR)
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2607.28577v1
Production forecasting systems retrain models regularly, but a retrained candidate does not necessarily outperform a continuously maintained incumbent that has continued to learn
https://arxiv.org/html/2608.03452v1
And when a learner reproduces the pattern, does it thereby represent it, or only its visible alternations
https://arxiv.org/html/2608.02966v1
(c) Because these distractor preferences vary with ability, their identities provide Fisher information that correctness does not retain
https://arxiv.org/html/2609.03027v1
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https://arxiv.org/html/2607.27063v1
The design does not claim that every momentum or reversal episode is caused by herding; instead, it provides a disciplined mechanism through which herding can amplify informational price adjustment
https://arxiv.org/html/2608.03358v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2607.28323v1
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