{"ID":2831401,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09117","arxiv_id":"2512.09117","title":"A Categorical Analysis of Large Language Models and Why LLMs Circumvent the Symbol Grounding Problem","abstract":"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.","short_abstract":"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.","url_abs":"https://arxiv.org/abs/2512.09117","url_pdf":"https://arxiv.org/pdf/2512.09117v1","authors":"[\"Luciano Floridi\",\"Yiyang Jia\",\"Fernando Tohmé\"]","published":"2025-12-09T20:59:46Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}