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23 result(s) for “okapi bm25 site hotchips pp site github com site text os dev site iot apache org site:arxiv.org” · 509 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
from the top result below
https://arxiv.org/html/2608.02613v1
1K text-only ego-observed tokens/agent/day)
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/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2407.13076v1
I INTRODUCTION
Owing to their long communication range and low power consumption, low-power wide area networks (LPWANs) [1] are extensively utilized in the development of autonomous wireless networks for various Internet of Things (IoT) applications, such as smart agriculture [2] and smart cities [3]
https://arxiv.org/list/cs.AI/new
Research artifact available under the Apache-2
https://arxiv.org/src/2006.01354
TdIjǖI,D ? =x`],Ă3֯v;!픻֭.t-W8I{參&-{s/}+Yp"X&v*i ; b"^ |aǶ >!ZQ}N?}=c߳? b? {.؏ '3'ѱ^[ͦa.ll X{,c7-ي}
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/list/cs.CL/new
1K text-only ego-observed tokens/agent/day)
https://arxiv.org/html/2407.02342v1
org) Wen Chen is with the Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China(e-mail: wenchen@sjtu
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
We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark
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.DC/2019-11
01941 [pdf, other]
Title: Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges
Sukhpal Singh Gill, Shreshth Tuli, Minxian Xu, Inderpreet Singh, Karan Vijay Singh, Dominic Lindsay, Shikhar Tuli, Daria Smirnova, Manmeet Singh, Udit Jain
https://arxiv.org/list/cs.DC/2020-06
arXiv admin note: text overlap with arXiv:2005
https://arxiv.org/list/cs.DC/2018-11
00652 [pdf, other]
Title: Modeling IoT-aware Business Processes - A State of the Art Report
Nadja Brouns, Samir Tata, Heiko Ludwig, E
https://arxiv.org/list/cs.DC/2021-12
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Distributed, Parallel, and Cluster Computing
Authors and titles for December 2021
Total of 203 entries : 1-50 51-100 101-150 151-200
https://arxiv.org/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/abs/2006.03994
Ethereum and Fog Computing, by Shirin Tahmasebi and 1 other authors
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Abstract:With the recent considerable developments in the Internet of Things (IoT), billions of resource-constrained devices are interconnected through the internet
https://arxiv.org/abs/2006.04122
04122] Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environments
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https://arxiv.org/list/cs.IR/new
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Information Retrieval
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https://arxiv.org/list/cs.NI/2020-05
00121 [pdf, other]
Title: Leveraging Context-awareness to Better Support the IoT Cloud-Edge Continuum
Liliana I
https://arxiv.org/list/cs.CR/2023-12
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Cryptography and Security
Authors and titles for December 2023
Total of 511
https://arxiv.org/list/eess.SP/new
This effectively overcomes the stringent bandwidth and power constraints of space platforms while extending federated learning (FL) to pervasive Internet-of-things (IoT) deployments