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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
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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/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.AI/new
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Artificial Intelligence
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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/html/2607.22511v2
[19] show that a calibrated board of LLM reviewers accepts deliberately fabricated papers up to 82%82\% of the time while detecting the fabrication at near-chance rates
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
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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
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/list/cs.IR/new
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Information Retrieval
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https://arxiv.org/list/hep-ph/new
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High Energy Physics - Phenomenology
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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
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
https://arxiv.org/html/1802.00048v2
Description:
arXiv is now an independent nonprofit
https://arxiv.org/html/2607.27188v1
2 Two outputs and two kinds of evidence
3
https://arxiv.org/html/2609.02852v1
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https://arxiv.org/html/2407.10064v4
An Overview of Applications and Insights
1 Introduction
2 Background
2.1 Review of Bridge Operation and maintenance Research
2.2 Overview of LLM-based Agents Research
2.2.1 Larger Language Models
2.2.2 LLM-based Agents
2.3 Why Bridge
https://arxiv.org/html/2407.04503v4
Here, we ask whether multi-turn behaviors conditioned on LLM-generated content cause the appearance of new kinds of biases, undetectable in single-turn behaviors but accumulating across iterated interactions