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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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Computation and Language

https://arxiv.org/list/cs.CL/new
Recent studies have therefore examined how to automatically derive optimization problems from natural-language descriptions, but existing benchmarks focus on settings where objectives and constraints can be written explicitly as mathematical expressions

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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
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2104.00446
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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/2303.12712
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/pdf/2006.04122
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https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2609.03103
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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/2608.02943
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/pdf/2608.01564
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Existence of Richter–Peleg representation for general preferences

https://arxiv.org/html/2508.08980v3
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https://arxiv.org/pdf/2608.01366
) /Title (Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2508.02758
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https://arxiv.org/src/2403.08299
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https://arxiv.org/pdf/2608.02942
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https://arxiv.org/pdf/2609.18842
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https://arxiv.org/pdf/2508.02685
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