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Computational Math Gets a Proof-Carrying Knowledge Layer

FARMS accepted 157 of 171 source-derived targets and tied formal premises, algorithms and quantitative guarantees to reusable computational skills.

Published Updated Story ID: mp-2026-10-30-027
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Summary

FARMS accepted 157 of 171 source-derived targets and tied formal premises, algorithms and quantitative guarantees to reusable computational skills.

Automated computational science needs more than prose descriptions of numerical methods. FARMS transforms source material into structured knowledge linking formal statements, algorithmic meaning, context and explicit guarantees. Across 171 targets in randomized numerical linear algebra and numerical partial differential equations, the pipeline accepted 157, with estimated mean costs between 85 cents and $2.29 per target depending on the source. Lean-checked implications connect selected statements to computational skills, illustrated with a heat-flow solve and wave-response compression. The work establishes a verifiable curation pipeline for the tested domains; it does not show that arbitrary computational literature can be formalized or that the resulting skills are always correct outside their premises.

Why it matters

FARMS accepted 157 of 171 source-derived targets and tied formal premises, algorithms and quantitative guarantees to reusable computational skills.

Limits and context

  • The work establishes a verifiable curation pipeline for the tested domains; it does not show that arbitrary computational literature can be formalized or that the resulting skills are always correct outside their premises.

Key claims

  1. FARMS accepted 157 of 171 source-derived targets and tied formal premises, algorithms and quantitative guarantees to reusable computational skills.

    Qualification: The work establishes a verifiable curation pipeline for the tested domains; it does not show that arbitrary computational literature can be formalized or that the resulting skills are always correct outside their premises.

    Evidence: source-2026-10-30-016

Sources

  1. arXiv preprint 2610.40560, original v1arXiv · primary research

Corrections

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