This paper presents a formal, categorical framework for analysing how humans and large language models (LLMs) transform content into truth-evaluated propositions about a state space of possible worlds W , in order to argue that LLMs do not solve but circumvent the symbol grounding problem.
@article{arxiv.2512.09117,
title = {A Categorical Analysis of Large Language Models and Why LLMs Circumvent the Symbol Grounding Problem},
author = {Luciano Floridi and Yiyang Jia and Fernando Tohmé},
journal= {arXiv preprint arXiv:2512.09117},
year = {2025}
}