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20 result(s) for “okapi bm25 site hotchips pp site github com site rare if site rss avast com site:arxiv.org” · 695 ms
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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
https://arxiv.org/list/cs.AI/new
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Artificial Intelligence
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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/2608.03480v1
com Arabic Institute for Translation, Algiers, Algeria CERIST, Algeria Nasredine Semmar CEA LIST, Paris-Saclay, France Hassina Aliane CERIST, Algeria
Abstract
The adoption of large pre-trained multilingual models for neural machine translation (MNMT) faces a major challenge: excessive memory and computational
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/2407.16741v3
com/All-Hands-AI/OpenHands
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https://arxiv.org/html/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2608.03095v1
We release codes and datasets at https://github
https://arxiv.org/html/2608.02999v1
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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/astro-ph.IM/new
In this way we obtain the errors of the astrometric parameters if one were to drop different elements of the relativity model
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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Electrical Engineering and Systems Science
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https://arxiv.org/html/2401.02063v2
arXiv:2401.02063v2 [astro-ph.IM] 03 Apr 2024
The 2023 Windows on the Universe Workshop White Paper Working Group: Tomás Ahumada Affiliation: Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA
https://arxiv.org/html/2407.01757v1
Opportunities and Technology Feasibility
1 Introduction
1.1 What is a distributed instrument?
1.1.1 Distributed Instruments and Sensor Networks
1.1.2 Distributed Instruments and Swarms
1.2 Contribution and Organization
2 State of the Art