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https://arxiv.org/pdf/2402.03300
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https://arxiv.org/html/2608.02613v1
2 pp, exceeding MemSearch reader scaling (+10

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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/src/2608.03883
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/src/2006.01354
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https://arxiv.org/src/2006.02230
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https://arxiv.org/src/2211.01910v2
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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/2608.03233
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https://arxiv.org/pdf/2608.02851v1
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https://arxiv.org/src/2006.01402
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https://arxiv.org/pdf/2508.16279
AgentScope 1.0: A Developer-Centric Framework for Building Agentic ApplicationsDawei GaoZitao LiYuexiang XieWeirui KuangLiuyi YaoBingchen QianZhijian MaYue CuiHaohao LuoShen LiLu YiYi YuShiqi HeZhiling LuoWenmeng ZhouZhicheng ZhangXuguang

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https://arxiv.org/pdf/2407.02402
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https://arxiv.org/pdf/2608.01785
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https://arxiv.org/pdf/2608.00785
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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

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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

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https://arxiv.org/pdf/2109.12196
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/src/2006.00441
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https://arxiv.org/pdf/2608.02615
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https://arxiv.org/pdf/2407.00132
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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

Computation and Language

https://arxiv.org/list/cs.CL/new
2 pp, exceeding MemSearch reader scaling (+10

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https://arxiv.org/src/2509.02523
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1 Introduction

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

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
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[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking