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30 result(s) for “okapi bm25 site www eyespy cy site does could good com site developer mozilla org site:arxiv.org” of 235 matched · 992 ms
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This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback
from the top result below
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/list/cs.CL/new
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Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise
https://arxiv.org/html/2608.02613v1
6 Ingest Overhead: When Does Structured Memory Pay Off
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
OpenDevin), a platform for the development of powerful and flexible AI agents that interact with the world in similar ways to those of a human developer: by writing code, interacting with a command line, and browsing the web
https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/pdf/2006.02318
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/pdf/2608.03883
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/abs/2607.29380
the Regeneration of Professional Expertise
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Computer Science > Computers and Society
arXiv:2607.29380 (cs)
[Submitted on 31 Jul
https://arxiv.org/pdf/2608.01366
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https://arxiv.org/pdf/2508.16279
0: A Developer-Centric Framework for Building Agentic ApplicationsDawei GaoZitao LiYuexiang XieWeirui KuangLiuyi YaoBingchen QianZhijian MaYue CuiHaohao LuoShen LiLu YiYi YuShiqi HeZhiling LuoWenmeng ZhouZhicheng ZhangXuguang HeZiqian ChenWeikai LiaoFarruh Isakulovich KushnazarovYaliang LiBolin DingJingren Zhouhttp:
https://arxiv.org/pdf/2608.03788
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https://arxiv.org/pdf/2607.27070v1
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https://arxiv.org/pdf/2608.04341
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https://arxiv.org/pdf/2608.02764
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https://arxiv.org/pdf/2607.28577v1
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https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2607.27188v1
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https://arxiv.org/pdf/2608.03063
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https://arxiv.org/pdf/2608.02122
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https://arxiv.org/pdf/2407.00125
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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/pdf/2608.01785
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https://arxiv.org/list/cs.IR/new
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Information Retrieval
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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
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.SI/new
How then does such polarisation occur in a system, where mainstream media do not systematically align with party positions