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30 result(s) for “okapi bm25 site hotchips pp site github com site re if site www nid nl nil site:arxiv.org” of 59 matched · 614 ms

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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
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https://arxiv.org/html/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

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https://arxiv.org/src/2608.03883
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https://arxiv.org/src/2608.03882
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https://info.arxiv.org/help/econ/index.html
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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2407.00132
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/src/2006.01354
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/src/2006.02230
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https://arxiv.org/pdf/2006.02759
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/src/2006.02464
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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/pdf/2608.03833
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https://arxiv.org/src/2402.03300
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https://arxiv.org/pdf/2607.27070v1
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https://arxiv.org/pdf/2608.02935
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https://arxiv.org/src/2006.02924
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https://arxiv.org/src/2211.01910v2
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https://arxiv.org/src/2006.01402
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https://arxiv.org/pdf/2508.02691
Statistical modeling of SOFR term structureTeemu PennanenWaleed Taoumhttp://arxiv.org/licenses/nonexclusive-distrib/1.0/q-fin.ST endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj > endobj 8 0 obj > endobj 9 0 obj >

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https://arxiv.org/pdf/2608.03233
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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

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https://arxiv.org/pdf/2608.02851v1
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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

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https://arxiv.org/html/2608.03048v1
com/JetAstra/PI-Mem Introduction The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al