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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
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly
https://arxiv.org/html/2501.13956v1
Consequently, additional context is required to provide out-of-domain (OOD) knowledge and reduce hallucinations
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/2608.03048v1
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
edu
Abstract
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways
https://arxiv.org/list/cs.CL/new
Many practically important problems are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable
https://arxiv.org/pdf/2402.03300
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2608.03994
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https://arxiv.org/pdf/2608.02615
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https://arxiv.org/pdf/2006.02318
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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/2608.03883
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/pdf/2508.04671
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https://arxiv.org/src/2303.12712
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https://arxiv.org/abs/2608.03883
arXiv is committed to these values and only works with partners that adhere to them
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
arXiv is committed to these values and only works with partners that adhere to them
https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/abs/2607.29380
arXiv is committed to these values and only works with partners that adhere to them
https://arxiv.org/abs/1411.2842
2842] Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons
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arXiv is now an independent nonprofit
https://arxiv.org/abs/2510.01395
01395] Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
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arXiv is now an independent nonprofit
https://arxiv.org/pdf/2609.03103
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https://arxiv.org/html/2508.16279v1
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/pdf/2608.02943
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