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https://arxiv.org/pdf/2005.14165
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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

1 Introduction

https://arxiv.org/html/2501.13956v1
While existing retrieval-augmented generation (RAG) frameworks for large language model (LLM)-based agents are limited to static document retrieval, enterprise applications demand dynamic knowledge integration from diverse sources including ongoing conversations and business data

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
PI-Mem: Pushing Long-Context Reasoning to 3

Computation and Language

https://arxiv.org/list/cs.CL/new
Accepted to C3NLP @ ACL 2026 Subjects: Computation and Language (cs

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
To bridge this gap, we introduce a novel and comprehensive Interactive SEmantic Enrichment system (ISEE)

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https://arxiv.org/pdf/2407.00125
) /Title (A Survey on Failure Analysis and Fault Injection in AI Systems) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/2005.13905
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https://arxiv.org/pdf/2407.00323
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https://arxiv.org/pdf/2608.00785
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https://arxiv.org/pdf/2407.00273
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https://arxiv.org/pdf/2608.03233
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https://arxiv.org/pdf/2006.04122
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https://arxiv.org/pdf/2006.00342
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Information Retrieval

https://arxiv.org/list/cs.IR/new
IR) Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed

Information Retrieval

https://arxiv.org/list/cs.IR/recent
IR) Tue, 4 Aug 2026 (showing first 34 of 45 entries ) [17] arXiv:2608

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883
arXiv is committed to these values and only works with partners that adhere to them

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663
arXiv is committed to these values and only works with partners that adhere to them

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

https://arxiv.org/abs/2608.03860
arXiv is committed to these values and only works with partners that adhere to them

1 Introduction

https://arxiv.org/html/2407.16741v3
edu Abstract Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways

Computers and Society Aug 2026

https://arxiv.org/list/cs.CY/current
251-256 Showing up to 50 entries per page: fewer | more | all [1] arXiv:2608

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https://arxiv.org/src/2006.02085
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https://arxiv.org/pdf/2608.03421
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https://arxiv.org/src/2006.02155
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https://arxiv.org/pdf/2607.26405v1
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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/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv

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https://arxiv.org/pdf/1802.07228
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https://arxiv.org/pdf/2608.01785
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