Re-axiomisation / model theory
Applying formalised reasoning outside the norm.
The assumptions of a model determine which questions it can answer. Changing those assumptions can reveal a different structure.
01 / our work
Clearer reasoning for consequential questions.
When a question mixes family history, paperwork and unfamiliar rules, the first useful step is to establish what is actually being claimed.
GHMI combines logical investigation, mathematics, computation and research. We organise evidence, expose assumptions and test how the pieces fit together, making the reasoning inspectable.
The aim is a clearer account of what is known, what remains uncertain and which questions need specialist advice. The home-page laboratories offer small examples of the methods behind that work.
We provide research and logical analysis. Legal advice and representation are outside our services.
A few areas of current interest, activity, and research.
Abstracting syntax trees
Learning grammars to understand structure
Search over structured expressions can generate candidates worth investigating. Grammar constrains the search; mathematical checking determines what survives.
Semantic classification
Aligning messy taxonomies with neural embeddings
Combining language models with fuzzy string matching pipelines to map high-entropy, real-world data against strict structural standards.
When numerics aren't enough
Solving symbolic problems, numerically, exactly
Numerical continuation can locate approximate solutions; exact reconstruction and verification determine which symbolic candidates are justified.