collaborators

5 papers

physics.ed-ph2025

Can Large Language Models Correctly Interpret Equations with Errors?

Lachlan McGinness, Peter Baumgartner

This paper explores the potential of Large Language Models to accurately extract and translate equations from typed student responses into a standard format. This is a useful task…

cs.AI2025

Large Language Models' Reasoning Stalls: An Investigation into the Capabilities of Frontier Models

Lachlan McGinness, Peter Baumgartner

Empirical methods to examine the capability of Large Language Models (LLMs) to use Automated Theorem Prover (ATP) reasoning strategies are studied. We evaluate the performance of S…

cs.AI2025

Large Language Models Imitate Logical Reasoning, but at what Cost?

Lachlan McGinness, Peter Baumgartner

We present a longitudinal study which evaluates the reasoning capability of frontier Large Language Models over an eighteen month period. We measured the accuracy of three leading…

cs.AI2025

The AlphaPhysics Term Rewriting System for Marking Algebraic Expressions in Physics Exams

Peter Baumgartner, Lachlan McGinness

We present our method for automatically marking Physics exams. The marking problem consists in assessing typed student answers for correctness with respect to a ground truth soluti…

cs.CL2025

Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science

Lachlan McGinness, Peter Baumgartner

Large Language Models (LLMs) were used to assist four Commonwealth Scientific and Industrial Research Organisation (CSIRO) researchers to perform systematic literature reviews (SLR…