Tootfinder

FYI: @…
https://bsky.app/profile/carlbergstrom.com/post/3m3vnlqah522u

Communication Bias in Large Language Models: A Regulatory Perspective
Adrian Kuenzler, Stefan Schmid
https://arxiv.org/abs/2509.21075 https://arxiv.org/pdf…

Communication Bias in Large Language Models: A Regulatory Perspective
Large language models (LLMs) are increasingly central to many applications, raising concerns about bias, fairness, and regulatory compliance. This paper reviews risks of biased outputs and their societal impact, focusing on frameworks like the EU's AI Act and the Digital Services Act. We argue that beyond constant regulation, stronger attention to competition and design governance is needed to ensure fair, trustworthy AI. This is a preprint of the Communications of the ACM article of the same t…

New preprint: "The Reverse File System: Towards open cost-effective secure WORM storage devices for logging"
https://arxiv.org/abs/2509.17969

Markovian families for pseudo-Anosov flows
Ioannis Iakovoglou
https://arxiv.org/abs/2509.19530 https://arxiv.org/pdf/2509.19530

Markovian families for pseudo-Anosov flows
Generalizing the classification approach described for transitive Anosov flows in dimension 3 in a previous preprint of the author, in this paper we describe a method for classifying (not necessarily transitive) pseudo-Anosov flows on 3-manifolds up to orbital equivalence. To every pseudo-Anosov flow $Φ$ (with no 1-prongs) on $M^3$ is associated a bifoliated plane $\mathcal{P}$ endowed with an action of $π_1(M)$. It is known that the previous action characterizes $Φ$ up to orbital equivalenc…

Kicking off the @… annual meeting with some insitu observations with #ECR Shenjie Zhou.
Get the data preprint at the QR code in the image below. Will also go into @soos map

aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
Pengsong Zhang, Xiang Hu, Guowei Huang, Yang Qi, Heng Zhang, Xiuxu Li, Jiaxing Song, Jiabin Luo, Yijiang Li, Shuo Yin, Chengxiao Dai, Eric Hanchen Jiang, Xiaoyan Zhou, Zhenfei Yin, Boqin Yuan, Jing Dong, Guinan Su, Guanren Qiao, Haiming Tang, Anghong Du, Lili Pan, Zhenzhong Lan, Xinyu Liu

aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack ri…

You Have Been LaTeXpOsEd: A Systematic Analysis of Information Leakage in Preprint Archives Using Large Language Models
Richard A. Dubniczky, Bertalan Borsos, Tihanyi Norbert
https://arxiv.org/abs/2510.03761

You Have Been LaTeXpOsEd: A Systematic Analysis of Information Leakage in Preprint Archives Using Large Language Models
The widespread use of preprint repositories such as arXiv has accelerated the communication of scientific results but also introduced overlooked security risks. Beyond PDFs, these platforms provide unrestricted access to original source materials, including LaTeX sources, auxiliary code, figures, and embedded comments. In the absence of sanitization, submissions may disclose sensitive information that adversaries can harvest using open-source intelligence. In this work, we present the first lar…

GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection (Preprint)
Tianxiang Xu, Zhichao Wen, Xinyu Zhao, Qi Hu, Yan Li, Chang Liu
https://arxiv.org/abs/2510.07285

GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection (Preprint)
The escalating complexity of network threats and the inherent class imbalance in traffic data present formidable challenges for modern Intrusion Detection Systems (IDS). While Graph Neural Networks (GNNs) excel in modeling topological structures and Temporal Convolutional Networks (TCNs) are proficient in capturing time-series dependencies, a framework that synergistically integrates both while explicitly addressing data imbalance remains an open challenge. This paper introduces a novel deep le…

The firefly luciferase is being used as a 'reporter gene' here, to reveal the Kalanchoe's 24-hour rhythms in the lab. Otherwise they are invisible.
Actually the luciferase glow is not directly visible either! It's so faint that the video was taken with super-sensitive cameras, closer to astronomy kit than to your smartphone. These are transgenic plants, developed for lab science in my now-#EdinburghUni colleague Jessica Pritchard's PhD, with James Hartwell in Liverpool.
She reported this cool new science in a #preprint last year:
📖 https://doi.org/10.1101/2024.12.19.629446
and I just caught up with it, although... 2/n

"The two were honored for cofounding the preeminent biomedical preprint servers bioRxiv and medRxiv."
Totally deserved, if you ask me. Also if you don't ask me.
https://www.cshl.edu/inglis-and-sever-earn-royal-society-research-culture-awa…

Inglis and Sever earn Royal Society Research Culture Award | Cold Spring Harbor Laboratory
Cold Spring Harbor Laboratory’s (CSHL’s) John Inglis and Richard Sever have earned The Royal Society’s 2025 Research Culture Award. The two were honored for cofounding the preeminent biomedical preprint servers bioRxiv and medRxiv. Established in 2013 and 2019 respectively, these online repositories empower scientists worldwide to share their research fast, fairly, and free of charge....

New preprint article based on an open letter (which I've signed onto) against the uncritical adoption of AI:
#AI #LLMs #GenAI

This long work from @… and @…, among many other interesting ideas,
calls the new kind of "AI" by a new name to distinguish it from older, often more ethical kinds of stats models:
"Displacement AI"
I lov…

Olivia Guest · Ολίβια Γκεστ (@olivia@scholar.social)
Attached: 3 images New preprint 🌟 Psychology is core to cognitive science, and so it is vital we preserve it from harmful frames. @Iris & I use our psych and computer science expertise to analyse and craft: Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1 🧵 1/

Chiral twist-bend liquid crystals
E. I. Kats
https://arxiv.org/abs/2509.14686 https://arxiv.org/pdf/2509.14686

Chiral twist-bend liquid crystals
Recently published preprint ( A. Ashkinazi, H.Chhabra, A.El.Moumane, M.M.C.Tortora, J.P.K. Doye, ''Chirality transfer in lyotropic twist-bend nematics'', arXiv:2508.03544v1 (2025)) has reawakened also interest to various mechanisms of chirality transfer from microscopic (molecular) level into the macroscopic chirality of the structure. In this communication we present a simple theoretical analysis how the transfer occurs for the Landau model of phase transition between cholesteric and chiral tw…

Preprint: Learning visual-to-auditory sensory substitution reveals flexibility in image-to-sound mapping https://osf.io/preprints/psyarxiv/6w278_v1

L2M-AID: Autonomous Cyber-Physical Defense by Fusing Semantic Reasoning of Large Language Models with Multi-Agent Reinforcement Learning (Preprint)
Tianxiang Xu, Zhichao Wen, Xinyu Zhao, Jun Wang, Yan Li, Chang Liu
https://arxiv.org/abs/2510.07363

L2M-AID: Autonomous Cyber-Physical Defense by Fusing Semantic Reasoning of Large Language Models with Multi-Agent Reinforcement Learning (Preprint)
The increasing integration of Industrial IoT (IIoT) exposes critical cyber-physical systems to sophisticated, multi-stage attacks that elude traditional defenses lacking contextual awareness. This paper introduces L2M-AID, a novel framework for Autonomous Industrial Defense using LLM-empowered, Multi-agent reinforcement learning. L2M-AID orchestrates a team of collaborative agents, each driven by a Large Language Model (LLM), to achieve adaptive and resilient security. The core innovation lies …

Many optimization problems assume exact inputs and total orders.
But what if uncertainty affects f — or if it returns a tuple, or a set instead of a single value?
Our new preprint explores how to handle such cases systematically:
Optimization under uncertainty — understanding orders and testing programs with specifications
👥 Botta, Jansson & Richter
📄

🚨 New preprint out🚨 now, why has Antarctica stopped (net) losing mass?
We explore how declining sea ice but mostly increasingly heavy and frequent atmospheric rivers have actually led to a small increase in the mass of #Antarctica, in spite of increased discharge from outlet glaciers.
The increase is unlikely to be sustained, and a small increase in calving or discharge over the grounding line can easily turn it upside down, but nevertheless another long-term prediction about the Antarctic appears to becoming true.
Lots of important subtleties in this one!
In review at Nature communications but I feel it's too important to wait for the review process.
https://arxiv.org/abs/2510.03590

Nice summary paper on DRAM addressing @… et al!
I like the setup btw.
https://florian.adamsky.it/research/pu

Predicting Diabetic Retinopathy Using a Two-Level Ensemble Model
Mahyar Mahmoudi, Tieming Liu
https://arxiv.org/abs/2510.01074 https://arxiv.org/pdf/2510.0…

Predicting Diabetic Retinopathy Using a Two-Level Ensemble Model
Preprint Note: This is the author preprint version of a paper accepted for presentation at the IISE Annual Conference & Expo 2025. The final version will appear in the official proceedings. Diabetic retinopathy (DR) is a leading cause of blindness in working-age adults, and current diagnostic methods rely on resource-intensive eye exams and specialized equipment. Image-based AI tools have shown limitations in early-stage detection, motivating the need for alternative approaches. We propose a …

👾 Large language models can execute complete ransomware attacks autonomously, research shows
https://techxplore.com/news/2025-09-large-language-ransomware-autonomously.html

Large language models can execute complete ransomware attacks autonomously, research shows
Criminals can use artificial intelligence, specifically large language models, to autonomously carry out ransomware attacks that steal personal files and demand payment, handling every step from breaking into computer systems to writing threatening messages to victims, according to new research from NYU Tandon School of Engineering posted to the arXiv preprint server.

Automated Code Repair for C/C Static Analysis Alerts
David Svoboda, Lori Flynn, William Klieber, Michael Duggan, Nicholas Reimer, Joseph Sible
https://arxiv.org/abs/2508.02820

Automated Code Repair for C/C++ Static Analysis Alerts
(Note: This work is a preprint.) Static analysis (SA) tools produce many diagnostic alerts indicating that source code in C or C++ may be defective and potentially vulnerable to security exploits. Many of these alerts are false positives. Identifying the true-positive alerts and repairing the defects in the associated code are huge efforts that automated program repair (APR) tools can help with. Our experience showed us that APR can reduce the number of SA alerts significantly and reduce the ma…

Development of management systems using artificial intelligence systems and machine learning methods for boards of directors (preprint, unofficial translation)
Anna Romanova
https://arxiv.org/abs/2508.03769

Development of management systems using artificial intelligence systems and machine learning methods for boards of directors (preprint, unofficial translation)
The study addresses the paradigm shift in corporate management, where AI is moving from a decision support tool to an autonomous decision-maker, with some AI systems already appointed to leadership roles in companies. A central problem identified is that the development of AI technologies is far outpacing the creation of adequate legal and ethical guidelines. The research proposes a "reference model" for the development and implementation of autonomous AI systems in corporate management. This…

... a quick search found that 24 of Jessica's datasets are now public on our BioDare2 resource - for example,
#OpenData
I used to work on this area - references in the preprint from 1992(!). Still, great to see how useful this approach can be. 3/n

Are arXiv submissions on Wednesday better cited? Introducing Big Data methods in undergraduate courses on scientific computing
St\'ephane Delorme, Leon Mach, Hubert Paszkiewicz, Richard Ruiz
https://arxiv.org/abs/2509.09601

Are arXiv submissions on Wednesday better cited? Introducing Big Data methods in undergraduate courses on scientific computing
Extracting information from big data sets, both real and simulated, is a modern hallmark of the physical sciences. In practice, students face barriers to learning ``Big Data'' methods in undergraduate physics and astronomy curricula. As an attempt to alleviate some of these challenges, we present a simple, farm-to-table data analysis pipeline that can collect, process, and plot data from the 800k entries common to the arXiv preprint repository and the bibliographical database inSpireHEP. The pi…

Rxiv-Maker: An Automated Template Engine for Streamlined Scientific Publications
Bruno M. Saraiva, Guillaume Jaquemet, Ricardo Henriques
https://arxiv.org/abs/2508.00836 https:/…

Rxiv-Maker: An Automated Template Engine for Streamlined Scientific Publications
Scientific publishing increasingly relies on preprint servers for rapid dissemination, yet researchers often struggle with manuscript preparation and quality control. Here we present Rxiv-Maker, a GitHub-native framework that converts markdown content to publication-ready PDFs through automated LaTeX processing. The system addresses reproducibility challenges in computational biology and imaging research by treating manuscripts as executable outputs rather than static documents. Rxiv-Maker inte…

PreprintToPaper dataset: connecting bioRxiv preprints with journal publications
Fidan Badalova, Julian Sienkiewicz, Philipp Mayr
https://arxiv.org/abs/2510.01783 https://…

PreprintToPaper dataset: connecting bioRxiv preprints with journal publications
The PreprintToPaper dataset connects bioRxiv preprints with their corresponding journal publications, enabling large-scale analysis of the preprint-to-publication process. It comprises metadata for 145,517 preprints from two periods, 2016-2018 (pre-pandemic) and 2020-2022 (pandemic), retrieved via the bioRxiv and Crossref APIs. Each record includes bibliographic information such as titles, abstracts, authors, institutions, submission dates, licenses, and subject categories, alongside enriched p…