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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
In practice, many field descriptions remain ambiguous or incomplete, as much of the essential context (e

Computation and Language

https://arxiv.org/list/cs.CL/new
LG) Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed

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https://arxiv.org/pdf/2608.03358
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https://arxiv.org/pdf/2407.00215
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https://arxiv.org/pdf/2608.00426
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https://arxiv.org/pdf/2608.03411
{~l F )=PH# ӑHton> stream DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language ModelsYixin BuRunze XiaGuanyun ZouYupeng JiHaodong LiuPiji Lihttp://creativecommons

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https://arxiv.org/pdf/2607.26327v3
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https://arxiv.org/pdf/2608.03421
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https://arxiv.org/pdf/2510.03215
7 % 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > stream Cache-to-Cache: Direct Semantic Communication Between Large Language ModelsTianyu FuZihan MinHanling ZhangJichao YanGuohao DaiWanli OuyangYu Wanghttp://arxiv

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https://arxiv.org/pdf/2608.03833
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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/2608.02613v1
7 Extrapolation across readers for cache-replay backends TTFT rows in Table 3

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
To address these issues, we propose PI-Mem (Parallel-Iterative Memory), a mechanism that processes all chunks in parallel and iteratively refines a shared memory over a bounded number of turns

Computers and Society Aug 2026

https://arxiv.org/list/cs.CY/current
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https://arxiv.org/pdf/2006.01402
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https://arxiv.org/src/2506.01844
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https://arxiv.org/src/2006.01598
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/src/2405.20232
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https://arxiv.org/src/2006.02464
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https://arxiv.org/src/2608.03882
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https://arxiv.org/pdf/2211.01910
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https://arxiv.org/src/2006.02155
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https://arxiv.org/src/2608.04003
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[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
All submitted systems follow a staged retrieval design in which BM25 retrieves candidates at scale and semantic or LLM rerankers refine the final rankings