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23 result(s) for “okapi bm25 site hotchips pp site github com site text of de site iot apache org site:arxiv.org” · 941 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
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 8787/77/4848 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations
https://arxiv.org/html/2608.03048v1
To address these issues, we propose PI-Mem (Parallel-Iterative Memory), a mechanism that processes all chunks in parallel and iteratively refines a shared memory over a bounded number of turns
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
However, their performance heavily depends on the clarity and completeness of data semantics
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
2 Agent Runtime: How Execution of Actions Results in Observations
2
https://arxiv.org/list/cs.CL/new
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 87/7/48 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations
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/src/2006.01354
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https://arxiv.org/abs/2608.03883
The task requires systems to rank candidate Reddit sentences according to their relevance to each of the 18 symptoms in the Adult ADHD Self-Report Scale (ASRS-v1
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/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
Thesis, Fall 2021, University of Edinburgh
Subjects: Distributed, Parallel, and Cluster Computing (cs
https://arxiv.org/list/cs.IR/recent
CL) [pdf, html, other]
Title: SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG
Kaysarul Anas Apurba, Md
https://arxiv.org/abs/2006.03994
Ethereum and Fog Computing, by Shirin Tahmasebi and 1 other authors
View PDF
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
A prediction problem in an MEC-based IoT environment typically corresponds to a collection of tasks with each task solved in a specific MEC environment based on the data accumulated locally, which can be regarded as a Multi-task Learning (MTL) problem
https://arxiv.org/list/cs.IR/new
We provide the core implementation of KGD at this https URL
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.DC/2020-06
arXiv admin note: text overlap with arXiv:2005
https://arxiv.org/list/cs.CR/2023-12
00031 [pdf, other]
Title: Crypto analysis of the key distribution scheme using noise-free resistances
Laszlo B
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