Newest Papers
Ideological bias in the production of research findings
George J. Borjas, Nate Breznau
Science Advances·2026
<jats:p>When studying policy-relevant topics, researchers’ policy preferences may shape analytical decisions and results interpretations. Detecting this bias is challenging because the research process is not normally part of an observed experimental setting. Our study exploits an opportunity to observe 158 researchers working independently in 71 teams during an experiment. After being asked their position on immigration policy, they used the same data to answer the same empirical question: Does immigration affect public support for social welfare programs? The researchers estimated 1253 alternative regression models, and the estimated impacts ranged from strongly negative to strongly positive. We find that teams composed of pro-immigration researchers estimated more positive impacts of immigration on public support for social programs, while anti-immigration teams estimated more negative impacts. The differences arise because different teams adopted different model specifications. The underlying research design decisions are the mechanism through which ideology enters the process of producing parameter estimates.</jats:p>
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View paper →The Kimwolf Botnet is Stalking Your Local Network – Krebs on Security
Brian Krebs
krebsonsecurity.com·2026
The story you are reading is a series of scoops nestled inside a far more urgent Internet-wide security advisory. The vulnerability at issue has been exploited for months already, and it’s time for a broader awareness of the threat. The short version is that everything you thought you knew about the security of the internal network behind your Internet router probably is now dangerously out of date.
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View paper →2025 letter | Dan Wang
Dan Wang
Dan Wang·2026
Corgis, compute, Cold War; Ecclesiastes; ties; Stendhal; humor; Pascal's Wager; deep infrastructure; Germanic obedience; Texas State Fair
★ 5.0 (1)
View paper →Recent breakthroughs in sulfide-functionalized metal–organic frameworks for electrocatalytic carbon dioxide reduction and hydrogen evolution reactions and their life cycle assessment
Faiza Arshad, Peng Ren, Xuemei Yang, Huarui Sun, Yuchen Qiao
Coordination Chemistry Reviews·2026·1 citations
No abstract available
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View paper →The Dry January for Readers
Steven Reese
Substack·2025
Take the challenge this January!
★ 4.0 (1)
View paper →mHC: Manifold-Constrained Hyper-Connections
Zhenda Xie, Yixuan Wei, Huanqi Cao, Chenggang Zhao, Chengqi Deng, Jiashi Li, Damai Dai, Huazuo Gao, Jiang Chang, Liang Zhao, Shangyan Zhou, Zhean Xu, Zhengyan Zhang, Wangding Zeng, Shengding Hu, Yuqing Wang, Jingyang Yuan, Lean Wang, Wenfeng Liang
arXiv·2025
Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models.
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View paper →Delivering Exploitation: A Marxist Analysis of India’s Gig Economy
Kushal
Substack·2025
The high human price we pay for the convenience of a 10-minute delivery.
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View paper →Recursive Language Models
Alex L. Zhang, Tim Kraska, Omar Khattab
arXiv·2025
We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query.
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View paper →Pertpy: an end-to-end framework for perturbation analysis
Lukas Heumos, Yuge Ji, Lilly May, Tessa D. Green, Stefan Peidli, Xinyue Zhang, Xichen Wu, Johannes Ostner, Antonia Schumacher, Karin Hrovatin, Michaela Müller, Faye Chong, Gregor Sturm, Alejandro Tejada, Emma Dann, Mingze Dong, Gonçalo Pinto, Mojtaba Bahrami, Ilan Gold, Sergei Rybakov, Altana Namsaraeva, Amir Ali Moinfar, Zihe Zheng, Eljas Roellin, Isra Mekki, Chris Sander, Mohammad Lotfollahi, Herbert B. Schiller, Fabian J. Theis
Nature Methods·2025
Abstract
Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size and complexity of such studies require a scalable analysis framework that takes existing biological context into account. Here we present pertpy, a Python-based modular framework for the analysis of large-scale single-cell perturbation experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods, such as automatic metadata annotation or perturbation distances, to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing single-cell analysis libraries and is designed to be easily extended.
★ 4.0 (1)
View paper →To Speak, Not to Speak, or How to Speak: Atypical Bodies and the Politics of Language
Damilola R. Oydeji
Language Literature and Interdisciplinary Studies·2025
<jats:p>Sé dìndìnrìn ni é ni?—“Are you mentally disabled?” —my mother would shout at me and my siblings in Yorùbá when we were still children, a question often provoked by any range of behaviors she deemed unusual. Whether it was bursting into tears when reporting a wrong done by another or responding sluggishly to a question of hers, any difference from expected emotional or behavioral norms prompted this remark. To this day, I can still hear the words, intoned in her Ibadan dialect, echoing through the walls of our one-story home in Lagos, Nigeria.</jats:p>
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View paper →Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
Shane A. McQuarrie, Mengwu Guo, Anirban Chaudhuri
arXiv·2025
This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation of virtual assets in a digital twin. Data-driven ROMs are explainable, computationally efficient scientific machine learning models that aim to preserve the underlying physics of complex dynamical simulations. Since the quality of data-driven ROMs is sensitive to the quality of the limited training data, we seek to identify training parameters for which using the associated training data results in the best possible parametric ROM. Our approach uses the operator inference methodology, a regression-based strategy which can be tailored to particular parametric structure for a large class of problems. We establish a probabilistic version of parametric operator inference, casting the learning problem as a Bayesian linear regression. Prediction uncertainties stemming from the resulting probabilistic ROM solutions are used to design a sequential adaptive sampling scheme to select new training parameter vectors that promote ROM stability and accuracy globally in the parameter domain. We conduct numerical experiments for several nonlinear parametric systems of partial differential equations and compare the results to ROMs trained on random parameter samples. The results demonstrate that the proposed adaptive sampling strategy consistently yields more stable and accurate ROMs than random sampling does under the same computational budget.
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