2 citations · 3 across the 5 of their papers we have counts for
6 papers
Efficient Learning of Mechanism Designs and Compositionality
João Pedro Gandarela, Thiago Rios, Stefan Menzel +1
Learning mechanism behaviour requires a representation of how connected parts jointly constrain motion. We study whether exposing reusable constraint subassemblies improves predict…
R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search
João Pedro Gandarela, Thiago Rios, Stefan Menzel +1
Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizons, fluency does not ensure r…
Language Models Refine Mechanical Linkage Designs Through Symbolic Reflection and Modular Optimisation
João Pedro Gandarela, Thiago Rios, Stefan Menzel +1
Designing mechanical linkages involves combinatorial topology selection and continuous parameter fitting. We show that language models can systematically improve linkage designs th…
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…
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…
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…