Thor integrates language models with automated theorem-prover hammers

The framework uses theorem-prover hammers for premise selection while leaving other proof decisions to the language model.

What happened?

Thor increased a language model's success on the PISA dataset from 39% to 57% and matched leading miniF2F methods with less compute.

Why it matters

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.

Historical context

Primary evidence

Evidence & status at a glance

Significance
H2 · Field milestone
Verification
Paper released
Evidence level
E2 · Research record
AI role
AI primary, human verified
Formal assurance
No formal assurance

How should these labels be read? →

Contribution & verification

Mathematical novelty
Tooling only
Interface
Formal, Symbolic, Hybrid
AI contribution
The language model generated proof actions while automated provers supplied premise-search assistance.
Human contribution
Researchers designed the integration protocol and evaluation across theorem-proving environments.
Verification
Paper released · E2 · Research record · No formal assurance
Last reviewed
2026-09-11

Technical details

Systems
thor
Organizations
university-of-cambridge, microsoft-research
Problems
pisa, minif2f
Methods
hammer, premise-selection, language-model

Sources & artifacts

Verification history

  • 2022-05-22 · Paper released · E2 · Research record

    The Chronicle records this event from the cited primary or official source; the status does not imply independent Chronicle verification.

Corrections

No corrections recorded.