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1 Search Zep implements three search functions: cosine semantic similarity search (φcos)subscript�cos(\varphi_{\text{cos}})( italic_φ start_POSTSUBSCRIPT cos end_POSTSUBSCRIPT ), Okapi BM25 full-text search (φbm25)subscript�bm25(\varphi_{\text{bm25}})( italic_φ start_POSTSUBSCRIPT bm25 end_POSTSUBSCRIPT )
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1 Introduction

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/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://info.arxiv.org/help/policies/privacy_policy.html
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https://arxiv.org/pdf/2504.17033
Breaking the Sorting Barrier for Directed Single-Source Shortest PathsRan DuanJiayi MaoXiao MaoXinkai ShuLonghui Yinhttp://arxiv.org/licenses/nonexclusive-distrib/1.0/cs.DS endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj

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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2508.02685
Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve FinanceChi-Sheng ChenAidan Hung-Wen Tsaihttp://arxiv.org/licenses/nonexclusive-distrib/1.0/q-fin.STcs.LGq-fin.TR endstream endobj 4 0 obj > endobj 5 0 obj > endobj

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https://arxiv.org/pdf/2607.28410v1
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https://arxiv.org/pdf/2608.02851v1
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https://arxiv.org/pdf/2608.02244
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2608.03833
‰S‰¿ÇÉJ%VÇÏÜ{Ç–À!!8D e1„¡€"žB Q–@1�oV+`ºìá rîf^ I !žðl§kmÇ©Åf·½Œþ0KXÚbp$—Q× »#šâ¼t3ëõ·„SBFÈ'Î=ÅÜo2ÐešËhÒvÚºU•ó®zk†úˆ]p+‚|î)¸ôº¡gä{&žéÜ÷—¸œ6í±°º1ãyn”y!¿�àsÁïyé´ì›ëkZ™SÜÿ?ášÜ=ö®—ô¥=ZU*«\>zhö¨jì�¶gO-šÍ”DúÚ`+m\

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

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

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

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

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

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

1 Introduction

https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands Slack http://bit

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https://arxiv.org/src/2006.02893
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https://arxiv.org/pdf/2608.03437
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https://arxiv.org/pdf/2101.00190
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https://arxiv.org/pdf/2608.03086
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https://arxiv.org/pdf/2006.01402
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https://arxiv.org/pdf/2407.00132
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https://arxiv.org/src/2006.01318
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https://arxiv.org/pdf/2608.04008
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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/2412.20138
TradingAgents: Multi-Agents LLM Financial Trading FrameworkYijia XiaoEdward SunDi LuoWei Wanghttp://creativecommons.org/licenses/by/4.0/q-fin.TRcs.AIcs.CEcs.LG endstream endobj 4 0 obj > endobj 5 0 obj > endobj 6 0 obj > endobj 7 0 obj >

Information Retrieval

https://arxiv.org/list/cs.IR/recent
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Information Retrieval Authors and titles for recent submissions Wed, 5 Aug 2026

Information Retrieval

https://arxiv.org/list/cs.IR/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Information Retrieval New submissions Cross-lists Replacements See recent

Computation and Language

https://arxiv.org/list/cs.CL/new
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Artificial Intelligence New submissions Cross-lists Replacements See recent