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https://arxiv.org/src/2006.03271
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https://arxiv.org/src/2006.01354
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https://arxiv.org/pdf/2101.00190
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https://arxiv.org/src/2006.01128
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https://arxiv.org/pdf/2309.08431
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2006.01331
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https://arxiv.org/src/2006.02464
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https://arxiv.org/src/2104.08691
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https://arxiv.org/src/2608.03882
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https://arxiv.org/src/2608.04008
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https://arxiv.org/src/2006.02155
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https://arxiv.org/src/2006.03318
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https://arxiv.org/pdf/2608.03421
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https://arxiv.org/src/2211.10435v1
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https://arxiv.org/src/2006.02085
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https://arxiv.org/src/2608.04003
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https://arxiv.org/pdf/2608.02918
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https://arxiv.org/html/2608.03624v1
In domains such as healthcare, this is a hard requirement; sensitive data cannot leave the premises, so models must run on-site, often on limited hardware Garg et al

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https://arxiv.org/pdf/2608.01366
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https://arxiv.org/src/2006.01251
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https://arxiv.org/pdf/2608.00151
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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/2609.02951
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https://arxiv.org/pdf/2503.11576
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/pdf/2608.01891
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https://arxiv.org/pdf/2608.03014
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https://arxiv.org/pdf/2608.01564
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https://arxiv.org/pdf/2609.02852
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