Andre Freitas

Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specific machinery ( e.g. Dirac notation, vector calculus) imposes additional formalisation challenges that modern LLMs and...

Large language models can produce fluent judgments for clinical natural language inference, yet they frequently fail when the decision requires the correct inferential schema rather than surface matching. We introduce CARENLI , a compartmentalised agentic framework that routes each premise–statem...

Recent works on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. Yet, their effectiveness on clinical natural language inference (CTNLI) remains underexplored. This study presents the first controlled eva...

Despite displaying semantic competence, large language models’ internal mechanisms that ground abstract semantic structure remain insufficiently characterised. To investigate whether and how LLMs develop causally functional representations of semantic roles, we introduce a causal-temporal methodo...