Talks

This talk explores how artificial intelligence could transform theoretical physics over the next 25 years by addressing the crucial challenge of navigating an increasingly complex scientific literature landscape. We introduce Litmaps, a platform leveraging AI and visualization techniques to accelerate literature discovery and insights. We illustrate Litmaps' current capabilities in rapidly identifying relevant connections and advancing theoretical research. We also outline critical engineering challenges, including open access to historical literature, data standardization, and managing uncertainty in AI models. Finally, we highlight the importance of collaboration among physicists, AI researchers, engineers, and entrepreneurs, to realise the AI-enhanced future of theoretical physics research.

From the start of AI, mathematical theorem proving has been an important challenge and technique. We sketch the topics of Automated and Interactive Theorem Proving and present the checking of naturally readable mathematical texts in the Naproche proof system. This involves translations between informal, semi-formal and formal mathematical languages and shows great potential for the use of new AI techniques.

What would effective and usable tools for searching text and graphics in research papers look like? In this talk we sketch a partial answer to this question, with reference to recent work in the Document and Pattern Recognition Lab at RIT. Two multimodal paper search prototypes, one for math (MathDeck) and one for chemistry (ReactionMiner search) will be used for illustration. A simple framework based on 'jars' of available information sources can organize and relate the actions performed by people and automated systems when retrieving, analyzing, and synthesizing sources. We will organize our answer sketch around this framework, and share open questions and research opportunities related to enhancing multi-modal search tools for expert and non-expert users. Note: ReactionMiner was developed in collaboration with NCSA and the Han lab at the University of Illinois, Urbana-Champaign. MathDeck demo: [https://people.rit.edu/ma5339/mathdeck_landing](https://people.rit.edu/ma5339/mathdeck_landing) ReactionMiner search demo: [https://reactionminer.platform.moleculemaker.org/home](https://reactionminer.platform.moleculemaker.org/home) Biography: Richard Zanibbi is a Professor of Computer Science at the Rochester Institute of Technology (RIT, USA) where he directs the Document and Pattern Recognition Lab (dprl@RIT). His research focuses upon the recognition and retrieval of graphical notations, particularly for mathematics and chemistry. He is also a member of the Molecule Maker Lab Institute (MMLI), one of the first NSF AI Centers. He received his PhD from Queen's University (Canada), and was an NSERC Postdoctoral Fellow at the Centre for Pattern Recognition and Machine Learning (CENPARMI) at Concordia University before joining RIT.

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.