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1 Search Zep implements three search functions: cosine semantic similarity search (φcos)subscript𝜑cos(\varphi_{\text{cos}})( italic_φ start_POSTSUBSCRIPT cos end_POSTSUBSCRIPT ), Okapi BM25 full-text search (φbm25)subscript𝜑bm25(\varphi_{\text{bm25}})( italic_φ start_POSTSUBSCRIPT bm25 end_POSTSUBSCRIPT )
from the top result below

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

Report GitHub Issue

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

Report GitHub Issue

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

Computation and Language

https://arxiv.org/list/cs.CL/new
Recent studies have therefore examined how to automatically derive optimization problems from natural-language descriptions, but existing benchmarks focus on settings where objectives and constraints can be written explicitly as mathematical expressions

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

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https://arxiv.org/pdf/2006.02318
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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/pdf/2104.00446
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2006.04122
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2608.00151
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/pdf/2608.01366
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https://arxiv.org/pdf/2109.12196
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https://arxiv.org/pdf/2608.04341
) /Title (Continuous Improvement and Parallel Autonomous Exploration: An LLM-Agent Framework for Searching Large Solution Spaces) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2608.02764
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https://arxiv.org/pdf/2607.28577v1
) /Title (Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2607.27188v1
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