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30 result(s) for “okapi bm25 site hotchips pp site www 4shared com site www sophrologue antics fr site:arxiv.org” of 87 matched · 567 ms

Scope: Nishi’s own index (our crawl + Common Crawl) — not the whole live web. Some sites block all automated crawlers (anti-bot walls), so their pages may be absent here even though we never filter lawful content.

Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)

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
from the top result below

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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/pdf/2608.03883
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2303.12712v5
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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/2508.04671
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https://arxiv.org/html/2501.13956v1
com Abstract We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark

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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

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https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv

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https://arxiv.org/pdf/2608.03157
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https://arxiv.org/pdf/2608.02817
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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/pdf/2607.26188v1
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https://arxiv.org/pdf/2608.02666
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https://arxiv.org/pdf/2608.02867
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https://arxiv.org/pdf/2608.03411
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https://arxiv.org/src/2211.10435v1
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https://arxiv.org/pdf/2006.15704
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https://arxiv.org/src/2006.03720
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/src/2006.02464
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https://arxiv.org/pdf/2006.02759
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https://arxiv.org/pdf/2508.02691
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https://arxiv.org/pdf/2607.27070v1
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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