Talks

This short talk will outline some of LaTeXML's uses as infrastructure, as well as its enabling effect for search, AI, assistive technologies and the mobile web. We have been on a journey towards scholarly articles with web-native mathematics since the dawn of the internet. The physics Open Science movement has led the way along with LaTeX, its authoring framework of choice. NIST's LaTeXML is a conversion tool that in the last twenty years has increasingly bridged that gap.

The rise of AI, in particular recent LLM based tools, has had an immediate impact on the production of physics, in ways both good and bad. I discuss some of the impact seen on arXiv in particular, what the status quo and prospects are, and speculate on the longer term impact.

UniverseTBD is an interdisciplinary community of astronomers, AI researchers, engineers, artists and enthusiasts aligned on a bold mission to democratise Science for everyone. From releasing the first large language model in Astronomy, AstroLLaMA-1, to the AI-enabled literature discovery tool Pathfinder, and through our research with AstroPT and HypoGen, our team has pushed the boundaries of AI for Science for the past two years. In this talk, I discuss for the first time how UniverseTBD came to be, our vision, our values, and what drives us and has enabled us to scale our team projects in our commitment to share our learnings with the broader scientific community. I also briefly discuss our latest results with hypothesis generation (HypoGen), multimodal language models (AstroLlaVA-1) and agentic AI (AstroCoder). I conclude with a vision for the future where AI teams up with human researchers to "help us understand the Universe".

The newest large-language reasoning models are for the first time powerful enough to perform mathematical reasoning in theoretical physics at graduate level. In the mathematics community, data sets such as FrontierMath are being used to drive progress and evaluate models, but theoretical physics has so far received less attention. In this talk I will present our dataset TPBench (arxiv:2502.15815, tpbench.org), which was constructed to benchmark and improve AI models specifically for theoretical physics. We find extremely rapid progress of models over the last months, but also significant challenges at research level difficulty. I will also briefly outline strategies to improve these models for theoretical physics.

Recent advancements have positioned Large Language Models (LLMs) as transformative tools for scientific research, capable of addressing complex tasks that require reasoning, problem-solving, and decision-making. Their exceptional capabilities suggest their potential as scientific research assistants, but also highlight the need for holistic, rigorous, and domain-specific evaluation to assess effectiveness in real-world scientific applications. This talk describes a multifaceted methodology for Evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory. This methodology incorporates four primary classes of evaluations. 1) Multiple Choice Questions to assess factual recall; 2) Open Response to evaluate advanced reasoning and problem-solving skills; 3) Lab-Style Experiments involving detailed analysis of capabilities as research assistants in controlled environments; and 4) Field-Style Experiments to capture researcher-LLM interactions at scale in a wide range of scientific domains and applications. These complementary methods enable a comprehensive analysis of LLM strengths and weaknesses with respect to their scientific knowledge, reasoning abilities, and adaptability. Recognizing the rapid pace of LLM advancements, we designed the methodology to evolve and adapt so as to ensure its continued relevance and applicability. This talk describes the current methodology's state. Although developed within a subset of scientific domains, the methodology is designed to be generalizable to a wide range of scientific domains.

Morphological summary statistics, such as Minkowski functionals, Betti numbers and Minkowski tensors, provide a route to carry out cosmological data analysis which is complementary to traditional mode decomposition based statistics such as the power spectrum. We summarize the properties of these statistics and demonstrate their information content by using the gravitational evolution of matter and 21-cm brightness temperature as physical examples.