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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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https://arxiv.org/list/cs.AI/new
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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/src/2006.01048
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https://static.arxiv.org/js/bibex-dev/bibex.css?20200709
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2608.01651v1
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https://arxiv.org/src/2608.03994
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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/pdf/2006.02318
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https://arxiv.org/pdf/2608.02615
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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/2508.04671
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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2006.04122
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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/src/1911.06462
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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2608.02609
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https://arxiv.org/pdf/2407.02402
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https://arxiv.org/pdf/2608.02618
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https://arxiv.org/pdf/2508.02686
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