Speaker
Description
In recent work, Constantin et al. (2026) suggested the development of language models that can generate coherent mathematical representations, advancing the automation of physical law discovery. Thanks to structural insights, they believe that one can inform the training or fine-tuning of language models for symbolic reasoning, encouraging them to generate expressions whose statistical and structural features mirror those found in real physics. Such an approach seems to be highly compatible with scientific realism, the “positive epistemic attitude toward the content of our best theories and models, recommending belief in both observable and unobservable aspects of the world described by the sciences” (Chakravartty 2011). We, however, know that LLMs are susceptible by necessity to hallucination and that it is impossible to avoid it. Interestingly, hallucination impacts AI-generated models in similar ways as systematic and random errors affect experimental physics and astrophysics. After exploring this analogy, I suggest an interpretation of hallucination that allows one to consider scientific realism (in a constrained form) compatible with the representation of data obtained through AI models. Finally, by discussing recent examples in agentic AI for science, and astrophysics in particular, I show that we need human understanding and interpretation of mathematical models to identify epistemic structures and practices in astrophysics in a meaningful way.