3 papers
cs.AI2026
FormalScience: Scalable Human-in-the-Loop Autoformalisation of Science with Agentic Code Generation in Lean
Jordan Meadows, Lan Zhang, 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-specifi…
cs.CL2025
Controlling Equational Reasoning in Large Language Models with Prompt Interventions
Jordan Meadows, Marco Valentino, Andre Freitas
This paper investigates how hallucination rates in Large Language Models (LLMs) may be controlled via a symbolic data generation framework, exploring a fundamental relationship bet…
cs.CL2024
Exploring the Limits of Fine-grained LLM-based Physics Inference via Premise Removal Interventions
Jordan Meadows, Tamsin James, Andre Freitas
Language models (LMs) can hallucinate when performing complex mathematical reasoning. Physics provides a rich domain for assessing their mathematical capabilities, where physical c…