Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics research is not yet mature. This position paper argues that LLM agents can potentially help accelerate theoretical, computational, and applied physics when properly integrated with domain knowledge and toolbox. We analyze current LLM capabilities for physics -- from mathematical reasoning to code generation -- identifying critical gaps in physical intuition, constraint satisfaction, and reliable reasoning. We envision future physics-specialized LLMs that could handle multimodal data, propose testable hypotheses, and design experiments. Realizing this vision requires addressing fundamental challenges: ensuring physical consistency, and developing robust verification methods. We call for collaborative efforts between physics and AI communities to help advance scientific discovery in physics.

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. Pronouns: He/hi (show all) Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. That's not to say useful quantum computers have actually arrived yet. This is because physics is an incredibly complex and challenging field of study. And the difficulty gets cranked up exponentially when you start adding "theoretical" and "quantum" to the research.

Brian Nord wants to build a self-driving telescope. The Fermilab astrophysicist envisions an instrument that, when presented with a hypothesis about the nature of the Universe, figures out the best observations to make on its own. He anticipates that it could take up to thirty years to understand and put together the project's nuts and bolts. One known component is artificial intelligence (AI)--algorithms similar to those that underpin facial recognition software and nascent self-driving car technology. Building toward his telescope dream, Nord has begun applying AI to problems in astronomy, such as identifying unusual astronomical objects known as gravitational lenses.

Quantum computing is one of the most exciting technologies there is, but its basis in quantum physics makes it a pain in the ass to understand and even harder to do anything with. A recent breakthrough in physics research, however, might change all of that and start a computing revolution. It wouldn't be the first time this has happened. IBM's Thomas J Watson (the person the Watson AI was named after) famously said "I think there is a world market for maybe five computers," in 1943. That's probably because, at the time, a computer filled up an entire room. But, in 1971 that changed with the development of the world's first microprocessors.