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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
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
The read step processes all chunks in parallel, with each chunk-level read conditioned on the same global memory, thereby avoiding sequential memory overwrites that may discard earlier evidence and reducing inference latency through parallel chunk processing
https://arxiv.org/pdf/2608.02615
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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/abs/2607.29380
The framework distinguishes Internalized Mastery (deep domain knowledge from sustained practice) from Distributed Mastery (orchestrating human-AI systems), and develops the Validation Tether: effective AI oversight depends on the expertise AI adoption may undermine
https://arxiv.org/html/2508.08980v3
A characterization for binary relations that may fail transitivity, however, has remained elusive
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/src/2211.01910v2
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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/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/list/cs.SI/new
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New submissions (showing 4 of 4 entries)
[1] arXiv:2608
https://arxiv.org/pdf/2508.02686
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/pdf/2608.00151
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https://arxiv.org/src/2205.11916v1
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https://arxiv.org/pdf/2608.03105
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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
https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2109.12196
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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/pdf/2608.04341
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https://arxiv.org/pdf/2006.00812
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https://arxiv.org/pdf/2608.02694
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https://arxiv.org/pdf/2608.02764
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/pdf/2607.28577v1
) /Title (Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2608.02978
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