Tootfinder

User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Jennifer King, Kevin Klyman, Emily Capstick, Tiffany Saade, Victoria Hsieh
https://arxiv.org/abs/2509.05382

User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to …

How to tell a vibe coder of lying when they say they check their code.
People who will admit to using LLMs to write code will usually claim that they "carefully check" the output since we all know that LLM code has a lot of errors in it. This is insufficient to address several problems that LLMs cause, including labor issues, digital commons stress/pollution, license violation, and environmental issues, but at least it's they are checking their code carefully we shouldn't assume that it's any worse quality-wise than human-authored code, right?
Well, from principles alone we can expect it to be worse, since checking code the AI wrote is a much more boring task than writing code yourself, so anyone who has ever studied human-computer interaction even a little bit can predict people will quickly slack off, stating to trust the AI way too much, because it's less work. I'm a different domain, the journalist who published an entire "summer reading list" full of nonexistent titles is a great example of this. I'm sure he also intended to carefully check the AI output, but then got lazy. Clearly he did not have a good grasp of the likely failure modes of the tool he was using.
But for vibe coders, there's one easy tell we can look for, at least in some cases: coding in Python without type hints. To be clear, this doesn't apply to novice coders, who might not be aware that type hints are an option. But any serious Python software engineer, whether they used type hints before or not, would know that they're an option. And if you know they're an option, you also know they're an excellent tool for catching code defects, with a very low effort:reward ratio, especially if we assume an LLM generates them. Of the cases where adding types requires any thought at all, 95% of them offer chances to improve your code design and make it more robust. Knowing about but not using type hints in Python is a great sign that you don't care very much about code quality. That's totally fine in many cases: I've got a few demos or jam games in Python with no type hints, and it's okay that they're buggy. I was never going to debug them to a polished level anyways. But if we're talking about a vibe coder who claims that they're taking extra care to check for the (frequent) LLM-induced errors, that's not the situation.
Note that this shouldn't be read as an endorsement of vibe coding for demos or other rough-is-acceptable code: the other ethical issues I skipped past at the start still make it unethical to use in all but a few cases (for example, I have my students use it for a single assignment so they can see for themselves how it's not all it's cracked up to be, and even then they have an option to observe a pre-recorded prompt session instead).

Canceled the Google account for my small business... Less money going to AI bullshit and Google's evil practices.
And I let them know.
(I had to click through a lot of screens where they tried to get me to stay, offered discounts, or offered to archive data for a few dollars a month. No thanks.)
#noAI

Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025
Jiahao Qiu, Jingzhe Shi, Xinzhe Juan, Zelin Zhao, Jiayi Geng, Shilong Liu, Hongru Wang, Sanfeng Wu, Mengdi Wang
https://arxiv.org/abs/2509.01659

Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025
Physics provides fundamental laws that describe and predict the natural world. AI systems aspiring toward more general, real-world intelligence must therefore demonstrate strong physics problem-solving abilities: to formulate and apply physical laws for explaining and predicting physical processes. The International Physics Olympiad (IPhO)--the world's most prestigious physics competition--offers a rigorous benchmark for this purpose. We introduce Physics Supernova, an AI agent system with supe…

Learning to Use AI for Learning: How Can We Effectively Teach and Measure Prompting Literacy for K-12 Students?
Ruiwei Xiao, Xinying Hou, Ying-Jui Tseng, Hsuan Nieu, Guanze Liao, John Stamper, Kenneth R. Koedinger
https://arxiv.org/abs/2508.13962

Learning to Use AI for Learning: How Can We Effectively Teach and Measure Prompting Literacy for K-12 Students?
As Artificial Intelligence (AI) becomes increasingly integrated into daily life, there is a growing need to equip the next generation with the ability to apply, interact with, evaluate, and collaborate with AI systems responsibly. Prior research highlights the urgent demand from K-12 educators to teach students the ethical and effective use of AI for learning. To address this need, we designed an Large-Language Model (LLM)-based module to teach prompting literacy. This includes scenario-based d…

NeurStore: Efficient In-database Deep Learning Model Management System
Siqi Xiang, Sheng Wang, Xiaokui Xiao, Cong Yue, Zhanhao Zhao, Beng Chin Ooi
https://arxiv.org/abs/2509.03228

NeurStore: Efficient In-database Deep Learning Model Management System
With the prevalence of in-database AI-powered analytics, there is an increasing demand for database systems to efficiently manage the ever-expanding number and size of deep learning models. However, existing database systems typically store entire models as monolithic files or apply compression techniques that overlook the structural characteristics of deep learning models, resulting in suboptimal model storage overhead. This paper presents NeurStore, a novel in-database model management system…

Pilot Study on Generative AI and Critical Thinking in Higher Education Classrooms
W. F. Lamberti, S. R. Lawrence, D. White, S. Kim, S. Abdullah
https://arxiv.org/abs/2509.00167 …

Pilot Study on Generative AI and Critical Thinking in Higher Education Classrooms
Generative AI (GAI) tools have seen rapid adoption in educational settings, yet their role in fostering critical thinking remains underexplored. While previous studies have examined GAI as a tutor for specific lessons or as a tool for completing assignments, few have addressed how students critically evaluate the accuracy and appropriateness of GAI-generated responses. This pilot study investigates students' ability to apply structured critical thinking when assessing Generative AI outputs in i…

PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
Zheng Zhao, Clara Vania, Subhradeep Kayal, Naila Khan, Shay B. Cohen, Emine Yilmaz
https://arxiv.org/abs/2506.09902

PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
Large language models (LLMs) have advanced conversational AI assistants. However, systematically evaluating how well these assistants apply personalization--adapting to individual user preferences while completing tasks--remains challenging. Existing personalization benchmarks focus on chit-chat, non-conversational tasks, or narrow domains, failing to capture the complexities of personalized task-oriented assistance. To address this, we introduce PersonaLens, a comprehensive benchmark for evalu…

Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse
Paulina DeVito, Akhil Vallala, Sean Mcmahon, Yaroslav Hinda, Benjamin Thaw, Hanqi Zhuang, Hari Kalva
https://arxiv.org/abs/2506.16412

Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse
Generative AI (GAI) technologies are quickly reshaping the educational landscape. As adoption accelerates, understanding how students and educators perceive these tools is essential. This study presents one of the most comprehensive analyses to date of stakeholder discourse dynamics on GAI in education using social media data. Our dataset includes 1,199 Reddit posts and 13,959 corresponding top-level comments. We apply sentiment analysis, topic modeling, and author classification. To support th…

Kevin: Multi-Turn RL for Generating CUDA Kernels
Carlo Baronio, Pietro Marsella, Ben Pan, Simon Guo, Silas Alberti
https://arxiv.org/abs/2507.11948 https:/…

Kevin: Multi-Turn RL for Generating CUDA Kernels
Writing GPU kernels is a challenging task and critical for AI systems' efficiency. It is also highly iterative: domain experts write code and improve performance through execution feedback. Moreover, it presents verifiable rewards like correctness and speedup, making it a natural environment to apply Reinforcement Learning (RL). To explicitly incorporate the iterative nature of this process into training, we develop a flexible multi-turn RL recipe that addresses unique challenges encountered in…

Collective dynamics of strategic classification
Marta C. Couto, Flavia Barsotti, Fernando P. Santos
https://arxiv.org/abs/2508.09340 https://arxiv.org/pdf/…

Collective dynamics of strategic classification
Classification algorithms based on Artificial Intelligence (AI) are nowadays applied in high-stakes decisions in finance, healthcare, criminal justice, or education. Individuals can strategically adapt to the information gathered about classifiers, which in turn may require algorithms to be re-trained. Which collective dynamics will result from users' adaptation and algorithms' retraining? We apply evolutionary game theory to address this question. Our framework provides a mathematically rigoro…

The Case for Contextual Copyleft: Licensing Open Source Training Data and Generative AI
Grant Shanklin, Emmie Hine, Claudio Novelli, Tyler Schroder, Luciano Floridi
https://arxiv.org/abs/2507.12713

The Case for Contextual Copyleft: Licensing Open Source Training Data and Generative AI
The proliferation of generative AI systems has created new challenges for the Free and Open Source Software (FOSS) community, particularly regarding how traditional copyleft principles should apply when open source code is used to train AI models. This article introduces the Contextual Copyleft AI (CCAI) license, a novel licensing mechanism that extends copyleft requirements from training data to the resulting generative AI models. The CCAI license offers significant advantages, including enhan…