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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
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Artificial Intelligence New submissions Cross-lists Replacements See recent

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https://arxiv.org/pdf/2608.03883
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https://arxiv.org/src/2006.01048
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/pdf/2608.03118
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1 Introduction

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

Report GitHub Issue

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

1 Introduction

https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands Slack http://bit

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https://arxiv.org/pdf/2607.26405v1
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https://arxiv.org/pdf/2506.01844
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsMustafa ShukorDana AubakirovaFrancesco CapuanoPepijn KooijmansSteven PalmaAdil ZouitineMichel AractingiCaroline PascalMartino RussiAndres MarafiotiSimon

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https://arxiv.org/pdf/2608.03233
T+jq8 M\c~wD`%::P sC` zzTrH`${0F{kʆƄ endstream endobj 411 0 obj > stream On the Diversity of Analogy Making in Large Language ModelsYuanhao ShenDaniel Xavier de SousaCaio César Sifuentes BarcelosHongyu GuoXiaodan Zhuhttp://arxiv

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https://arxiv.org/src/2006.02833
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https://arxiv.org/pdf/2608.03157
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https://arxiv.org/pdf/2608.03788
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https://arxiv.org/pdf/2608.02851v1
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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

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/2608.03421
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https://arxiv.org/pdf/2006.00064
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https://arxiv.org/src/2006.02085
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https://arxiv.org/src/2006.02155
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https://arxiv.org/pdf/2006.01402
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https://arxiv.org/pdf/2608.02867
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https://arxiv.org/src/2006.03720
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https://arxiv.org/pdf/2407.01722
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https://arxiv.org/src/2006.01128
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https://arxiv.org/pdf/2212.07841
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https://arxiv.org/pdf/2601.04660
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https://arxiv.org/src/2006.02464
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https://arxiv.org/src/2212.07841
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