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25 result(s) for “okapi bm25 site www eyespy cy site www dcxmoto cy site:arxiv.org” of 235 matched · page 8 · 720 ms
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https://arxiv.org/src/2402.03300
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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/pdf/2407.17789
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https://arxiv.org/pdf/2508.16279
AgentScope 1.0: A Developer-Centric Framework for Building Agentic ApplicationsDawei GaoZitao LiYuexiang XieWeirui KuangLiuyi YaoBingchen QianZhijian MaYue CuiHaohao LuoShen LiLu YiYi YuShiqi HeZhiling LuoWenmeng ZhouZhicheng ZhangXuguang
https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2508.11152
5 application/pdf AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions arXiv 2025-08-18T00:20:17Z Text sample-authordraft
https://arxiv.org/pdf/2608.00028
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https://arxiv.org/pdf/2608.02851v1
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https://arxiv.org/pdf/2608.03833
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https://arxiv.org/pdf/2608.03157
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https://arxiv.org/pdf/2507.17609
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https://arxiv.org/pdf/2608.02935
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https://arxiv.org/pdf/2608.03788
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https://arxiv.org/pdf/2508.07408
-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant TradingYueyi WangQiyao Weihttp://creativecommons.org/licenses/by/4.0/q-fin.STcs.CLcs.LG endstream endobj 4 0 obj > endobj 5 0 obj
https://arxiv.org/src/2006.03594
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https://arxiv.org/pdf/2608.04008
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https://arxiv.org/pdf/2608.03233
T+jq8 M\c~wD`%::P sC` zzTrH`${0F{kʆƄ endstream endobj 411 0 obj > stream On the Diversity of Analogy Making in Large Language ModelsYuanhao ShenDaniel Xavier de SousaCaio César Sifuentes BarcelosHongyu GuoXiaodan Zhuhttp://arxiv
https://arxiv.org/pdf/2608.02854v1
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https://arxiv.org/pdf/2608.01861
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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
IR)
Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/html/2407.16741v3
2 OpenHands Architecture
2.1 Agent Definition and Implementation
2.2 Agent Runtime: How Execution of Actions Results in Observations
2.3 Agent Skills: The Extensible Agent-Computer Interface
2.4 Agent Delegation: Cooperative Multi-agent