Tootfinder

The California Report on Frontier AI Policy
Rishi Bommasani, Scott R. Singer, Ruth E. Appel, Sarah Cen, A. Feder Cooper, Elena Cryst, Lindsey A. Gailmard, Ian Klaus, Meredith M. Lee, Inioluwa Deborah Raji, Anka Reuel, Drew Spence, Alexander Wan, Angelina Wang, Daniel Zhang, Daniel E. Ho, Percy Liang, Dawn Song, Joseph E. Gonzalez, Jonathan Zittrain, Jennifer Tour Chayes, Mariano-Florentino Cuellar, Li Fei-Fei

The California Report on Frontier AI Policy
The innovations emerging at the frontier of artificial intelligence (AI) are poised to create historic opportunities for humanity but also raise complex policy challenges. Continued progress in frontier AI carries the potential for profound advances in scientific discovery, economic productivity, and broader social well-being. As the epicenter of global AI innovation, California has a unique opportunity to continue supporting developments in frontier AI while addressing substantial risks that c…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- A Group Theoretic Analysis of the Symmetries Underlying Base Addition and Their Learnability by N...
Cutter Dawes, Simon Segert, Kamesh Krishnamurthy, Jonathan D. Cohen

Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics
Mustafa Demir, Jacob Miratsky, Jonathan Nguyen, Chun Kit Chan, Punya Mishra, Abhishek Singharoy
https://arxiv.org/abs/2506.22520

Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics
This study examines the impact of an Artificial Intelligence tutor teammate (AI) on student curiosity-driven engagement and learning effectiveness during Interactive Molecular Dynamics (IMD) tasks on the Visual Molecular Dynamics platform. It explores the role of the AI's curiosity-triggering and response behaviors in stimulating and sustaining student curiosity, affecting the frequency and complexity of student-initiated questions. The study further assesses how AI interventions shape student …

Sequential Diagnosis with Language Models
Harsha Nori, Mayank Daswani, Christopher Kelly, Scott Lundberg, Marco Tulio Ribeiro, Marc Wilson, Xiaoxuan Liu, Viknesh Sounderajah, Jonathan Carlson, Matthew P Lungren, Bay Gross, Peter Hames, Mustafa Suleyman, Dominic King, Eric Horvitz
https://arxiv.org/abs/2506.22405

Sequential Diagnosis with Language Models
Artificial intelligence holds great promise for expanding access to expert medical knowledge and reasoning. However, most evaluations of language models rely on static vignettes and multiple-choice questions that fail to reflect the complexity and nuance of evidence-based medicine in real-world settings. In clinical practice, physicians iteratively formulate and revise diagnostic hypotheses, adapting each subsequent question and test to what they've just learned, and weigh the evolving evidence…

Improved Image Reconstruction and Diffusion Parameter Estimation Using a Temporal Convolutional Network Model of Gradient Trajectory Errors
Jonathan B. Martin, Hannah E. Alderson, John C. Gore, Mark D. Does, Kevin D. Harkins
https://arxiv.org/abs/2506.14995

Improved Image Reconstruction and Diffusion Parameter Estimation Using a Temporal Convolutional Network Model of Gradient Trajectory Errors
Summary: Errors in gradient trajectories introduce significant artifacts and distortions in magnetic resonance images, particularly in non-Cartesian imaging sequences, where imperfect gradient waveforms can greatly reduce image quality. Purpose: Our objective is to develop a general, nonlinear gradient system model that can accurately predict gradient distortions using convolutional networks. Methods: A set of training gradient waveforms were measured on a small animal imaging system, and used …

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
Jindong Han, Yansong Ning, Zirui Yuan, Hang Ni, Fan Liu, Tengfei Lyu, Hao Liu
https://arxiv.org/abs/2507.00914

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
The long-standing vision of intelligent cities is to create efficient, livable, and sustainable urban environments using big data and artificial intelligence technologies. Recently, the advent of Large Language Models (LLMs) has opened new ways toward realizing this vision. With powerful semantic understanding and reasoning capabilities, LLMs can be deployed as intelligent agents capable of autonomously solving complex problems across domains. In this article, we focus on Urban LLM Agents, whic…