Assess whether macro-set selection heuristics align MathLib data with A_n log-density predictions

Investigate whether filtering the candidate macro set in MathLib by in-degree percentiles, restricting to definition-like elements (e.g., declarations whose resulting type is Sort), or related selection/optimization approaches produces a macro set for which the three measured relationships (log unwrapped length versus depth, wrapped length versus depth, and log unwrapped length versus wrapped length) better agree with the A_n log-density regime predictions.

Background

The authors compare MathLib measurements with predictions from several monoid regimes and find strongest alignment with the A_n log-density case. However, identifying which MathLib elements should count as the operative macro set remains unresolved.

They suggest simple heuristics (e.g., filtering by in-degree or restricting to certain definition-like elements) and an optimization framing for macro-set selection but explicitly note that it is an open question whether these or similar methods achieve better agreement with the theoretical predictions.