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Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
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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

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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2608.02872
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https://arxiv.org/src/2608.04007
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https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/pdf/2205.11916v2
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https://arxiv.org/pdf/2608.03494
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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2410.05779
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https://arxiv.org/src/2608.04003
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https://arxiv.org/src/2608.04009
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2508.11152
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https://arxiv.org/src/2006.02924
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https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon

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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/src/2212.07841
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https://arxiv.org/src/2402.03300
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https://arxiv.org/src/2303.12712
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Existence of Richter–Peleg representation for general preferences

https://arxiv.org/html/2508.08980v3
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Maximizing utility in multi-agent environments by anticipating the

https://arxiv.org/html/2407.04889v1
Therefore, if an agent is good at anticipating the behavior of the other agents, in particular how they will make decisions in each round as a function of their experience that far, it could try to judiciously make its own decisions over the rounds of the interaction so as to influence the other agents to behave in a