Talks

Exactly solvable models have tremendously helped our understanding of condensed matter systems. A notable number of them are "frustration-free" in the sense that all local terms of the Hamiltonian can be minimized simultaneously. It has been particularly successful at describing the physics of gapped phases of matter, such as symmetry protected topological phases and topologically ordered phases. On the other hand, relatively little has been understood about gapless frustration-free Hamiltonians, and their ability to teach us about more generic systems. In this talk, we derive a constraint on the spectrum of frustration-free Hamiltonians. Their dynamical exponent z, which captures the scaling of the energy gap versus the system size, is bounded from below to be z >= 2. This proves that frustration-free Hamiltonians are incapable of describing conformal critical points with z = 1. Further, by a well-known mapping from Markov processes to frustration-free Hamiltonians, we show that the relaxation time for many Markov processes also scale with z >=2. This improves the previously known bound on the relaxation time scaling of z >= 7/4. The talk is based on works with Rintaro Masaoka and Haruki Watanabe.

Scientific research is facing mounting challenges: overwhelmed reviewers, fragmented expertise, an outdated and inefficient system for allocating resources, and dissemination tools that no longer match the complexity and scale of modern scientific output. In this talk, I speak both as a researcher and as the founder of a successful startup to explore what a more coherent, future-ready scientific ecosystem could look like. Drawing on real prototypes and emerging tools, I’ll outline how we might reshape the way we publish, collaborate, and share not just to fix what’s broken, but to unlock what science could become tomorrow.

In the past two years, the LLM has made significant progress in math and reasoning, but it has not been applied widely in scientific research tasks. In this talk I will give a brief introduction to our on-going efforts on building the first AI scientist platform, where all researchers in different fields can contribute to teaching the AI scientists via contributing benchmarks and contributing specialized tools. We believe that by providing AI with the real-time updates of benchmarks and research tools, we are starting to enter an era with innovation driven by new types of human-AI collaboration.

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.