Primary evidence
- Thor: Wielding Hammers to Integrate Language Models and Automated Theorem ProversS1 · Primary research · Paper · 2022-05-22
The framework uses theorem-prover hammers for premise selection while leaving other proof decisions to the language model.
Thor increased a language model's success on the PISA dataset from 39% to 57% and matched leading miniF2F methods with less compute.
It highlighted a durable design principle: use symbolic tools where exhaustive reliability is strong and learned models where generation is needed.
Significance rationale: Thor was an early explicit neuro-symbolic architecture in which language models delegated premise-selection bottlenecks to classical automated provers.
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