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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
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Artificial Intelligence

https://arxiv.org/list/cs.AI/new
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2104.00446
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/src/2608.03994
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https://arxiv.org/src/2006.02759
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https://arxiv.org/pdf/2608.02758
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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/2608.03883
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2006.04122
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2508.04671
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2608.02615
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https://arxiv.org/src/2303.12712
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Computation and Language

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

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https://arxiv.org/pdf/2407.02402
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https://arxiv.org/pdf/2608.02244
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https://arxiv.org/pdf/2609.02852
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https://arxiv.org/pdf/2608.02609
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https://arxiv.org/pdf/2307.09702
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https://arxiv.org/pdf/2010.15980
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https://arxiv.org/src/2403.08299
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https://arxiv.org/pdf/2608.03105
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