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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2407.00125
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https://arxiv.org/pdf/2607.26188v1
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https://arxiv.org/pdf/2607.26405v1
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https://arxiv.org/pdf/2608.02867
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https://arxiv.org/src/2006.03720
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https://arxiv.org/src/2006.02155
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https://arxiv.org/src/2104.08691
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https://arxiv.org/pdf/2608.00028
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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/html/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/src/2608.04009
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https://arxiv.org/pdf/2507.17609
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https://arxiv.org/html/2501.13956v1
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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
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
https://arxiv.org/html/2608.01366v2
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https://arxiv.org/pdf/2608.02764
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https://arxiv.org/src/2508.02739
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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
https://arxiv.org/list/cs.IR/recent
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Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/html/2407.16741v3
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https://arxiv.org/pdf/2210.00113
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https://arxiv.org/pdf/2508.08980v3
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https://arxiv.org/html/2407.01093v1
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https://arxiv.org/pdf/2006.02464
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https://arxiv.org/pdf/2608.02361
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https://arxiv.org/list/cs.AI/new
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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