Teaching Vampire New Tricks: An Experimental Study of Neural Clause Selection

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

A neural clause-selection guidance approach in the Vampire theorem prover was recently shown to substantially improve the success rate of the prover's default strategy on the TPTP benchmark. We experimentally study the impact of the approach across several ITP-derived benchmark sets and its interaction with theorem proving strategies. We find that while the neural guidance consistently improves performance within individual benchmark domains, cross-benchmark application of guiding models underperforms the plain default strategy. This can be remedied by training a single model on all datasets at once. Such a model, although more expensive to obtain, helps Vampire almost catch up in performance across all datasets. The picture when considering combined strategies is less clear-cut, indicating persisting value of neural guidance but under diminishing returns.