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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/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/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/recent
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Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/list/cs.IR/new
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https://arxiv.org/html/2407.16741v3
At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments
https://arxiv.org/list/cs.CY/current
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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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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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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/2608.01934
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https://arxiv.org/pdf/2609.03103
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https://arxiv.org/html/2508.08980v3
org perpetual non-exclusive license
arXiv:2508
https://arxiv.org/pdf/2508.02758
5 % 1 0 obj > >> endobj 2 0 obj > endobj 3 0 obj > stream CTBench: Cryptocurrency Time Series Generation BenchmarkYihao AngQiang WangQiang HuangYifan BaoXinyu XiAnthony K
https://arxiv.org/html/2607.24970v2
one-to-one random markets, IA’s expected average rank is asymptotically logn\log n, just like DA’s
https://arxiv.org/src/2006.03271
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https://arxiv.org/html/2508.20101v1
Using daily log returns of 50 publicly listed firms over a one-year period, we evaluate the model’s predictive performance in a cross-validation study
https://arxiv.org/html/2607.28294v1
Building on recent limit theory for heavy-tailed and integrated ACD processes, we analyze recursive bootstrap schemes that either fix the time span (yielding a random sample size) or fix the number of durations (yielding a random time span)
https://arxiv.org/html/2607.28410v1
7 AI Assistance Statement
License: arXiv
https://arxiv.org/html/2508.20108v2
These shifts occur not only over time as market conditions change but also across different stocks, each exhibiting unique statistical properties such as return and volatility
https://arxiv.org/html/2608.02612v1
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arXiv:2608
https://arxiv.org/html/2607.28323v1
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https://arxiv.org/html/2407.08254v2
However, the computational complexity of Dec-POMDPs presents a significant hurdle, making direct optimal solution search infeasible in polynomial-time [5]
https://arxiv.org/html/2608.03153v1
0%, and correcting for differential selection into full-time wage employment with semiparametric, machine-learning, and bounds methods leaves it unchanged
https://arxiv.org/html/2607.27099v1
9 Detailed results for Météo France data at small time scales
License: arXiv
https://arxiv.org/html/2508.02739v1
, Jian Li1
Abstract
The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs)
https://arxiv.org/html/2309.08431v3
In this paper, we characterise the continuous-time wealth dynamics of strategic LPs who dynamically adjust their range of liquidity provision in CL pools
https://arxiv.org/html/2407.02342v1
To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications
https://arxiv.org/html/2608.03486v1
At the same time, the recent theoretical work of Kolesár and Plagborg-Møller (2025) has demonstrated that statistical identification strategies developed under linearity may fail when the structural relationship between the reduced-form innovations and shocks is nonlinear, even if the shocks themselves satisfy