Measuring Model Intelligence via 'Hint Distance' in Math Proofs
danshipper · x · 2026-08-03
The author explores how to quantitatively assess the intelligence of AI models and their value in scientific discovery. He proposes measuring how far a model needs to start from the answer when solving a new mathematical result.
For instance, giving just the conjecture versus pointing directly to the specific area of math containing the solution. The gradient of 'hint distance' serves as a strong proxy for evaluating relative model intelligence and their practical value in discovering new ideas.
Related event: Prompt Distance: Can Weaker Models Reproduce Frontier Proofs?(6 posts)→
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