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20 result(s) for “okapi bm25 site terms com pp site github com site git tony eu site ben barter site:arxiv.org” · 818 ms
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Did you mean okapi bm25 site terms com pp site github com site git tony eu site ben barber site:arxiv.org?
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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
from the top result below
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/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv
https://arxiv.org/src/2006.00342
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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
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
https://arxiv.org/html/2403.08299v1
The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/pdf/1911.06462
‡Äî0â�c‚ÔjõëÓ/€.×Öe6^�F?¾nNxsÞüô"”ç÷ï_üõuÞD¡*Ã2ºyw“Ç7yXª0�·ææCPÏãhú©{Di c*~ü†q_õu[uLhûøhqº®ƒiàîz€Î~®:ÇéÅŒwÃx>||ÿ/8ëm¤Uš”|¦‡CÃøé%ŒÌ³ÀÎõ [aP!WyÔããeøèuk¬:Üj ¿?µ2€Ÿ!1¯Çj[32�8âCÛ˜[{MÕpÈŽ{†»«Ù“©O}ûûldþ©š¸ãTYG’™—aB!�Lx«�ãÝÍ}S�aœ
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/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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Wed, 5 Aug 2026
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/new
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https://arxiv.org/abs/2403.08299v1
Abstract:The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot
https://arxiv.org/abs/2403.08299
Abstract:The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot
https://arxiv.org/html/2608.02620v1
com/index/retiring-gpt-4o-and-older-models
https://arxiv.org/list/cs.CR/new
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https://arxiv.org/src/1910.03771
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