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27 result(s) for “okapi bm25 site terms com pp site www datacom com site paying systems site www sec site:arxiv.org” · 1064 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
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/html/2608.03011v1
Abstract
Recent zero-shot text-to-speech (TTS) systems achieve remarkable naturalness and speaker similarity but typically require high-quality speaker prompts and either strip away or entangle the acoustic environment with speaker characteristics, limiting their real-world applicability
https://arxiv.org/html/2608.03138v1
Existing LLM-based academic writing systems have made progress on literature reviews, survey generation, and general manuscript assistance (Wang et al
https://blog.arxiv.org/2026/07/28/remembering-ralph-wijers/
A talented researcher, lecturer, and mentor, Ralph joined arXiv as a moderator and advisor in 2020 and was chair of both arXiv’s Physics Section Editorial Committee (SEC) and its Editorial Advisory Council
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/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.IR/new
IR)
Large language models (LLMs) have emerged as promising listwise rerankers for recommender systems, but their reliability under equivalent candidate permutations remains unclear
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
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/html/2407.10811v1
3Ziyue Li and Wolfgang Ketter are with the Information Systems Department at the University of Cologne, Germany
https://arxiv.org/html/2608.00426v1
1 Introduction
Large language model (LLM)-based multi-agent systems (MAS) increasingly plan, communicate, use tools, and coordinate over extended horizons [40, 24, 14, 36, 10, 23]
https://arxiv.org/html/2608.03141v1
Token Communication
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https://arxiv.org/list/cs.AI/new
Biological systems, in contrast, exhibit adaptive plasticity, maintaining function through dynamic re-organisation around damage
https://arxiv.org/pdf/2608.03118
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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
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/list/cs.IR/recent
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Information Retrieval
Authors and titles for recent submissions
Wed, 5 Aug 2026
https://arxiv.org/list/cs.DC/2020-06
Rodriguez, Rajkumar Buyya
Comments: submitted as book chapter to Knowledge Management in Development of Data-Intensive Software Systems (KMDDIS)
Subjects: Distributed, Parallel, and Cluster Computing (cs
https://arxiv.org/abs/2006.04236
Comments: 2019 IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM)
Subjects: Distributed, Parallel, and Cluster Computing (cs
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.SI/new
To bridge this gap, we propose Spectral Efficiency Centrality (SEC), a temporal spectral centrality framework that quantifies node importance by evaluating the change in spectral radius caused by node removal across temporal snapshots
https://arxiv.org/html/2407.17789v2
com/modelscope/agentscope/tree/main/examples/paper_large_scale_simulation to inspire further research and development in large-scale multi-agent simulations
https://arxiv.org/html/2508.16279v1
Applications
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https://arxiv.org/html/2609.03371v1
1 Atmosphere, airmass, and the usable sky window
For a ground observation at zenith angle zz the airmass is approximately X≃seczX\simeq\sec z away from the horizon
https://arxiv.org/html/2608.01024v1
Building on this surrogate, we present Stress-Relief Annealing (SRA), which repeats a simple cycle: compute the stress field of the current layout, then relocate shelves away from the most stressed regions, accepting or rejecting each change by simulated annealing (Kirkpatrick, Gelatt Jr, and Vecchi 1983) (Sec
https://arxiv.org/html/2608.02937v1
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https://arxiv.org/html/2407.18010v2
‘menu costs’ or transaction costs in financial systems