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https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2608.02909v1
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https://arxiv.org/html/2410.19947v2
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arXiv:2410
https://arxiv.org/html/2407.13076v1
Index Terms:
LoRa, multigateway, system energy efficiency, matching-based, multiagent deep reinforcement learning
https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2402.03300
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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.
https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/src/2006.02759
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2508.04671
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https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation
https://arxiv.org/list/cs.IR/recent
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https://arxiv.org/pdf/2608.03157
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https://arxiv.org/html/2609.03027v1
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https://arxiv.org/html/2607.27063v1
-Based Network Model with Information Diffusion
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https://arxiv.org/html/2607.28577v1
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arXiv:2607
https://arxiv.org/html/2608.03358v1
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arXiv:2608
https://arxiv.org/html/2608.03452v1
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https://arxiv.org/html/2608.02971v1
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https://arxiv.org/html/2609.03103v1
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https://arxiv.org/html/2608.03233v1
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arXiv:2608