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https://arxiv.org/src/2608.04003
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https://arxiv.org/pdf/2608.02122
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https://arxiv.org/src/2407.17789
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https://arxiv.org/src/2005.13905
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/src/2405.20232
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https://arxiv.org/pdf/2608.03118
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https://arxiv.org/src/2006.01354
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https://arxiv.org/pdf/2608.02918
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https://arxiv.org/src/2006.01598
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https://arxiv.org/pdf/2005.13905
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Report GitHub Issue

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

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

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Through a user study, automated user simulation, quantitative evaluation, and case study, we demonstrate that ISEE significantly reduces cognitive load, improves description quality, and enhances downstream task performance

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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/pdf/2006.02602
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https://arxiv.org/pdf/2006.15704
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You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4
2 Proof of Theorem 6 D Asymmetric Loss Functions: The Case of Linex License: CC BY-NC-SA 4

Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
Ideally, researchers would like to commit to a choice of the three elements listed above such that they are certain that, even in the worst case admitted by those minimal assumptions, such choices will not be too far from being optimal

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https://arxiv.org/pdf/2212.07841
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https://arxiv.org/src/2006.00441
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https://arxiv.org/pdf/2508.07408
-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant TradingYueyi WangQiyao Weihttp://creativecommons.org/licenses/by/4.0/q-fin.STcs.CLcs.LG endstream endobj 4 0 obj > endobj 5 0 obj

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https://arxiv.org/pdf/2407.01489
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https://arxiv.org/pdf/2506.01844
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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

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/2607.28410v1
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https://arxiv.org/src/1910.03771
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https://arxiv.org/pdf/2006.00441
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https://arxiv.org/pdf/2407.00225
5 % 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > stream Large-scale, Independent and Comprehensive study of the power of LLMs for test case generationWendkûuni C