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Freshness: newest result fetched 24 d ago · median age of these results 24 d · 1 of 30 carry a fetch time
https://arxiv.org/src/2303.12712
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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/list/cs.AI/new
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https://arxiv.org/html/2607.27188v1
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https://arxiv.org/html/2608.03529v1
(2024), where a human expert and an automated system might describe information in different terms
https://arxiv.org/html/2608.02813v1
Wind Turbine Generators
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https://arxiv.org/html/2608.00458v1
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https://arxiv.org/html/2608.03011v1
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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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https://arxiv.org/html/2407.13076v1
Index Terms:
LoRa, multigateway, system energy efficiency, matching-based, multiagent deep reinforcement learning
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/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/2006.04236
Comments: 2019 IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM)
Subjects: Distributed, Parallel, and Cluster Computing (cs
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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Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
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[1] arXiv:2006
https://arxiv.org/pdf/2608.03157
ÄÌ4ºû4àC0ÎøMÈ8$SÙH‹‘Rp¬Æk4>¢eñ8ŠQ�{Ñ›Pp/ªIŠö1˜œñšMõŠc2ââ…^ø¼Q#ïT#ÑáNrFRÂK“àclƒ©‘w'�0I»ƒŸ}¢”ˆŸJüœãþUÞÁñ™h+`d@«WÙul“`¯r0=ÍÑhJl“ŒfÇ6'è‘ÏÅhÞ©&8bAŸƒ|¢ˆ šk³JˆžwÔ„ÔîØF�¹À´ÿù’LÅ·J6Q‰¶�‘†,'ø²¯ÎÄDÓUÁ {P½‰™_GObiw‚I®µ‰& zàk‚ýÑ�¾¥HÂF)Òª0^SÑogRÁ€¨“�ƒœ7™C¦¸ÈÁÑ6¿Ø8â¯W— lÀŸ2N *cåOÕ䂨8S
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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https://arxiv.org/html/2608.02966v1
-Choice Benchmarks
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https://arxiv.org/html/2608.03358v1
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https://arxiv.org/html/2608.02971v1
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