Tootfinder

Safe and Efficient In-Context Learning via Risk Control
Andrea Wynn, Metod Jazbec, Charith Peris, Rinat Khaziev, Anqi Liu, Daniel Khashabi, Eric Nalisnick
https://arxiv.org/abs/2510.02480

Safe and Efficient In-Context Learning via Risk Control
Large language models (LLMs) demonstrate a remarkable ability to learn new tasks from a few in-context examples. However, this flexibility introduces safety concerns: LLMs can be influenced by incorrect or malicious demonstrations -- for example, if an adversary tampers with or injects harmful examples without a human supervisor noticing. This motivates principled designs in which the system itself includes built-in mechanisms to guard against such attacks. We propose a novel approach to limit …

Ethical AI prompt recommendations in large language models using collaborative filtering
Jordan Nelson, Almas Baimagambetov, Konstantinos Avgerinakis, Nikolaos Polatidis
https://arxiv.org/abs/2510.06924

Ethical AI prompt recommendations in large language models using collaborative filtering
As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability concerns. Traditional oversight methods struggle with scalability, necessitating dynamic solutions. This paper proposes using collaborative filtering, a technique from recommendation systems, to enhance ethical prompt selection. By leveraging user interactions, it promotes ethical guidelines while reducing bias. Contri…

Identifying the secondary jet in the RadioAstron image of OJ~287
Mauri J. Valtonen, Lankeswar Dey, Staszek Zola, Alok C. Gupta, Shubham Kishore, Achamveedu Gopakumar, Paul J. Wiita, Minfeng Gu, Kari Nilsson, Zhongli Zhang, Rene Hudec, Katsura Matsumoto, Marek Drozdz, Waldemar Ogloza, Andrei V. Berdyugin, Daniel E. Reichart, Markus Mugrauer, Tapio Pursimo, Stefano Ciprini, Tatsuya Nakaoka, Makoto Uemura, Ryo Imazawa, Michal Zejmo, Vladimir V. Kouprianov, James W. Davidson, Jr., Alberto …

Identifying the secondary jet in the RadioAstron image of OJ~287
The 136 year long optical light curve of OJ~287 is explained by a binary black hole model where the secondary is in a 12 year orbit around the primary. Impacts of the secondary on the accretion disk of the primary generate a series of optical flares which follow a quasi-Keplerian relativistic mathematical model. The orientation of the binary in space is determined from the behavior of the primary jet. Here we ask how the jet of the secondary black hole projects onto the sky plane. Assuming that…

Punctuation and Predicates in Language Models
Sonakshi Chauhan, Maheep Chaudhary, Koby Choy, Samuel Nellessen, Nandi Schoots
https://arxiv.org/abs/2508.14067 https://

Punctuation and Predicates in Language Models
In this paper we explore where information is collected and how it is propagated throughout layers in large language models (LLMs). We begin by examining the surprising computational importance of punctuation tokens which previous work has identified as attention sinks and memory aids. Using intervention-based techniques, we evaluate the necessity and sufficiency (for preserving model performance) of punctuation tokens across layers in GPT-2, DeepSeek, and Gemma. Our results show stark model-sp…

Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
Ruyu Liu, Dongxu Zhuang, Jianhua Zhang, Arega Getaneh Abate, Per Sieverts Nielsen, Ben Wang, Xiufeng Liu
https://arxiv.org/abs/2510.00797

Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semantic com ponents. This study introduces SF-SPA (Semantic Facade Solar-PV Assessment), an automated framework that transforms street-view photographs into quantitative PV deployment assessments. The approach combines com puter vision and artificial intelligence techniques to addres…

NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Yuqing Zhang, Ecesu \"Urker, Tessa Verhoef, Gemma Boleda, Arianna Bisazza
https://arxiv.org/abs/2509.22479

NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeling) cannot get at causal mechanisms, and experimental paradigms with humans are hard to apply to semantic change due to the extended diachronic processes involved. This work introduces NeLLCom-Lex, a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system (e.g. En…

Beyond the Algorithm: A Field Guide to Deploying AI Agents in Clinical Practice
Jack Gallifant, Katherine C. Kellogg, Matt Butler, Amanda Centi, Patrick F. Doyle, Sayon Dutta, Joyce Guo, Matthew J. Hadfield, Esther H. Kim, David E. Kozono, Hugo JWL Aerts, Adam B. Landman, Raymond H. Mak, Rebecca G. Mishuris, Tanna L. Nelson, Guergana K. Savova, Elad Sharon, Benjamin C. Silverman, Umit Topaloglu, Jeremy L. Warner, Danielle S. Bitterman

Beyond the Algorithm: A Field Guide to Deploying AI Agents in Clinical Practice
Large language models (LLMs) integrated into agent-driven workflows hold immense promise for healthcare, yet a significant gap exists between their potential and practical implementation within clinical settings. To address this, we present a practitioner-oriented field manual for deploying generative agents that use electronic health record (EHR) data. This guide is informed by our experience deploying the "irAE-Agent", an automated system to detect immune-related adverse events from clinical …