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https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2608.02966v1
Across 189 LLMs and 31,554 items from 14 benchmarks, LLM-NRM predicts held-out LLM-item interactions more accurately than binary Item Response models and conventional nominal-response baselines, and its ability estimates achieve the strongest Spearman correlation of 0
https://arxiv.org/html/2609.03027v1
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https://arxiv.org/html/2607.28323v1
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https://arxiv.org/html/2608.03086v1
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arXiv:2608
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/2412.12213v2
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https://arxiv.org/html/2407.02342v1
com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learnin
https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2607.27188v1
) /Title (Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/list/cs.IR/new
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[17] arXiv:2608
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/2607.27188v1
5 Real NIFTY data: adaptation helps but does not close the gap
6 Discussion
6
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/2607.28577v1
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https://arxiv.org/html/2608.03358v1
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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/2407.04974v1
Unlike many existing dec-POMDP-based RL approaches, the proposed algorithm is suitable for model-free multi-agent reinforcement learning as it does not require knowledge of a transition model
https://arxiv.org/html/2608.03141v1
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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2608.02817
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https://arxiv.org/pdf/2006.01128
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2607.27070v1
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/src/2005.13905
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