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30 result(s) for “okapi bm25 site www eyespy cy site time log gov site www is gov site:arxiv.org” of 263 matched · 888 ms
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86 against log Common Crawl share), producing a steep low-resource penalty that is nonetheless narrowing with successive model releases
from the top result below
https://arxiv.org/list/cs.CL/new
Many practically important problems are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable
https://arxiv.org/list/cs.AI/new
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly
https://arxiv.org/html/2608.02613v1
5 Composition: reader-bound LLM time + reader-free search
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https://arxiv.org/html/2501.13956v1
Consequently, additional context is required to provide out-of-domain (OOD) knowledge and reduce hallucinations
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
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
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https://arxiv.org/html/2607.22511v2
A common approach is to close the research loop with a large language model (LLM) reviewer
https://arxiv.org/pdf/2402.03300
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https://arxiv.org/list/cs.SI/new
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New submissions (showing 4 of 4 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.LG/2024-12
3251-3298
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[1] arXiv:2412
https://arxiv.org/src/2006.02318
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https://arxiv.org/pdf/2407.00345
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https://arxiv.org/src/2205.11916v3
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/src/2006.01048
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https://arxiv.org/src/2608.03994
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https://arxiv.org/pdf/2608.02615
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https://arxiv.org/pdf/2508.04671
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https://arxiv.org/src/2303.12712
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https://arxiv.org/abs/2608.03883
arXiv is committed to these values and only works with partners that adhere to them
https://arxiv.org/abs/2608.03860
arXiv is committed to these values and only works with partners that adhere to them
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/list/cs.IR/new
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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
https://arxiv.org/html/2407.16741v3
Interaction with users is implemented through a chat-based user interface that visualizes the agent’s current actions and allows for real-time feedback (Fig
https://arxiv.org/list/cs.CY/current
251-256
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[1] arXiv:2608
https://arxiv.org/abs/1411.2842
2842] Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons
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arXiv is now an independent nonprofit
https://arxiv.org/abs/2607.29380
arXiv is committed to these values and only works with partners that adhere to them
https://arxiv.org/abs/2510.01395
01395] Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
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arXiv is now an independent nonprofit
https://arxiv.org/list/econ.GN/recent
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Tue, 4 Aug 2026 (showing 16 of 16 entries )
[13] arXiv:2608
https://arxiv.org/pdf/2609.03103
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