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https://arxiv.org/html/2608.03452v1
And when a learner reproduces the pattern, does it thereby represent it, or only its visible alternations
https://arxiv.org/html/2609.03027v1
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https://arxiv.org/html/2608.03067v1
Computational Language Phenotypes in a Large Language Model
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https://arxiv.org/html/2607.28323v1
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https://arxiv.org/html/2608.02813v1
Wind Turbine Generators
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https://arxiv.org/html/2608.03529v1
(2024), where a human expert and an automated system might describe information in different terms
https://arxiv.org/html/2608.03011v1
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arXiv:2608
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
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
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
https://arxiv.org/list/cs.IR/recent
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Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/html/2608.03233v1
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https://arxiv.org/html/2608.03141v1
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arXiv:2608
https://arxiv.org/html/2608.03655v1
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arXiv:2608
https://arxiv.org/pdf/2608.02817
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