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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/src/2005.14165
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https://arxiv.org/src/2006.01402
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https://arxiv.org/pdf/2608.02867
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https://arxiv.org/src/2006.03594
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https://arxiv.org/src/2006.02833
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https://arxiv.org/src/2006.01598
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https://arxiv.org/src/2211.01910v2
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https://arxiv.org/src/2006.02085
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https://arxiv.org/src/2608.04008
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https://arxiv.org/pdf/2608.03233
T+jq8 M\c~wD`%::P sC` zzTrH`${0F{kʆƄ endstream endobj 411 0 obj > stream On the Diversity of Analogy Making in Large Language ModelsYuanhao ShenDaniel Xavier de SousaCaio César Sifuentes BarcelosHongyu GuoXiaodan Zhuhttp://arxiv

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https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2006.00342
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https://arxiv.org/src/2006.03318
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https://arxiv.org/src/2211.10435v1
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https://arxiv.org/pdf/2303.12712v5
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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

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

https://arxiv.org/abs/2104.08663
Benchmark for Zero-shot Evaluation of Information Retrieval Models, by Nandan Thakur and 4 other authors View PDF HTML (experimental) Abstract:Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of

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

https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation

Information Retrieval

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

Information Retrieval

https://arxiv.org/list/cs.IR/new
In a live A/B test on Shopee Homepage Search, it increases GMV per user by 1

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

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
1 Algorithmic Fairness in Management Science 2

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https://arxiv.org/pdf/2608.03157
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Report GitHub Issue

https://arxiv.org/html/2609.03027v1
Report GitHub Issue Description: arXiv is now an independent nonprofit

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license arXiv:2607

Train Often, Deploy Selectively: Forward-Gated Model Replacement in

https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license arXiv:2607

Every Wrong Answer Counts: Option-Level Psychometrics for LLM Multiple

https://arxiv.org/html/2608.02966v1
In conclusion, we show that incorrect answers carry distinct and useful measurement information rather than representing equivalent mistakes

Report GitHub Issue

https://arxiv.org/html/2608.03358v1
org perpetual non-exclusive license arXiv:2608