Why mathematics matters for AI safety
Mathematical reasoning sits underneath work in science, engineering, finance and code. If a model learns to reach answers by invalid steps, those habits carry into every field that depends on it.
Checking reasoning, not just results, is what mathematicians and statisticians are trained to do. It is also what step-by-step training data needs.
What mathematics experts do
Mathematics experts write full solutions and proofs, grade model reasoning step by step, and rank alternative solutions on validity and clarity. They mark the exact step where an argument fails.
Statisticians review analyses, test choices and interpretation of results. Experts also evaluate models on new problems and produce training data that rewards correct reasoning over lucky answers.
- Expert-written solutions and proofs
- Step-by-step grading of model reasoning
- Ranking alternative solutions
- Review of statistical analysis and interpretation
- Evaluation on new problems
Failure modes mathematics experts catch
Experts catch correct final answers reached by invalid reasoning, proofs that assume what they set out to show, and steps that skip a case. They notice when a model claims a result it has not established.
Statisticians catch misapplied tests, ignored assumptions, confusions between correlation and cause, and confident conclusions drawn from too little data.
How we vet mathematics experts
Candidates are interviewed by AI voice agents on how they approach problems and verify their own work. They then complete real-world work tests, such as finding the error in a flawed proof or grading a model's solution.
Credentials and experience are reviewed as part of vetting. Statisticians are tested on applied analysis, mathematicians on proof and problem-solving.