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https://arxiv.org/pdf/2608.03883
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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

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

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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv

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

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https://arxiv.org/pdf/2002.02145
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https://arxiv.org/pdf/2006.01354
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https://arxiv.org/src/2608.03898
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https://arxiv.org/src/2006.00441
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https://arxiv.org/pdf/1911.06462
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https://arxiv.org/src/2006.03271
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https://arxiv.org/pdf/2006.02759
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https://arxiv.org/src/2608.03882
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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/src/2006.03318
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https://arxiv.org/src/2608.03883
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https://arxiv.org/src/2407.17789
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https://arxiv.org/pdf/2212.07841
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https://arxiv.org/pdf/2608.00328
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https://arxiv.org/src/2211.01910v2
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https://arxiv.org/src/2006.02833
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1 Introduction

https://arxiv.org/html/2407.16741v3
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https://arxiv.org/src/2508.16279
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[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

Information Retrieval

https://arxiv.org/list/cs.IR/recent
IR) Tue, 4 Aug 2026 (showing first 34 of 45 entries ) [17] arXiv:2608