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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/pdf/2608.03883
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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

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/2608.02758
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2508.11152
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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/pdf/2608.03157
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https://arxiv.org/pdf/2006.15704
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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/src/2005.13905
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https://arxiv.org/pdf/2607.26188v1
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/src/2006.02893
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https://arxiv.org/pdf/2608.02867
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https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2607.26405v1
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https://arxiv.org/src/2006.03720
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https://arxiv.org/pdf/2608.03421
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https://arxiv.org/src/2006.02155
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