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Crosslisted article(s) found for q-fin.GN. https://arxiv.org/list/q-fin.GN/new
[1/1]:
- NoLBERT: A No Lookahead(back) Foundational Language Model for Empirical Research
Ali Kakhbod, Peiyao Li

"The understanding that the total supremacy of the “data” discourse was always a problematic, neoliberal way of seeing and structuring the world, of legitimizing violence according to the needs of those in power."
(Original title: The “Data” Narrative eats itself)
https://tante.cc/2025/09/15/…

The "Data" Narrative eats itself
“The first model fakes the data, then the second model trains on the fake data. Any problems in the synthetic data set are amplified further. Then the second model — based on fake data — is used to treat real patients. This is, of course, all fine.” Today I want to take David Gerard’s recent […]

Just finished "Once For Yes" by Allie Millington. A phenomenal book dealing with tragedy, gentrification, grief, and community, it's preposterously poetic, but unfortunately has a twisted neoliberal politics lurking behind the scenes that makes me hesitate to recommend it. I enjoyed it greatly, especially the tightly choreographed prose, and the plot was both very well-paced and touching. It's fun for adults but also written for kids, which makes it all the more frustrating that despite touching on gentrification, it valorizes someone who is objectively a pretty scummy landlord, and fails to interrogate land ownership or rent in the slightest. It wouldn't be nearly the same story without the way things wrap up, but that doesn't make me comfortable with the larger messages it's sending, even if I think its messaging about grief is good, including for children.
#AmReading

HyperFlexis: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling
Zahra Yousefijamarani, Xinglu Wang, Qian Wang, Morgan Lindsay Heisler, Taha Shabani, Niloofar Gholipour, Parham Yassini, Hong Chang, Kan Chen, Qiantao Zhang, Xiaolong Bai, Jiannan Wang, Ying Xiong, Yong Zhang, Zhenan Fan
https://arxiv.org/abs/2508.15…

HyperFlexis: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling
Modern large language model (LLM) serving systems face challenges from highly variable requests with diverse lengths, priorities, and stage-specific service-level objectives (SLOs). Meeting these requires real-time scheduling, rapid and cost-effective scaling, and support for both collocated and disaggregated Prefill/Decode (P/D) architectures. We present \textbf{HyperFlexis}, a unified LLM serving system that integrates algorithmic and system-level innovations to jointly optimize scheduling …