Problem
Research questions and friction points this paper is trying to address.
neural clause selection
benchmark sets
theorem proving strategies
Teaching Vampire New Tricks: An Experimental Study of Neural Clause Selection
🤖 AI Summary
研究使用神经子句选择方法改进Vampire定理证明器的表现，发现该方法在特定数据集上有效，但跨数据集应用时需全数据训练以维持性能。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
neural clause-selection guidance
Vampire theorem prover
cross-benchmark application
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