proof-writer

To write rigorous mathematical proofs for machine learning theory, you provide a theorem statement and assumptions to generate a fully justified proof package. This process enforces proof-status triage, explicit notation separation, and a verification pass to avoid unjustified steps.

Proof feasibility triage evaluates whether an ML theorem is provable as stated, requires additional assumptions, or remains unjustified. It systematically validates theorem assumptions by mapping dependencies and tracking which assumptions justify each nontrivial mathematical step.

You formalize a proof sketch by submitting it with your theorem statement and assumptions. The system completes missing steps, structures the strategy and dependency map, and outputs an appendix-ready proof package with verification checks.

Yes, you can assess whether a claimed theorem proof can be completed by providing the claim, stated assumptions, and existing proof sketch. The triage process determines if the proof is valid, needs extra assumptions, or cannot be justified as stated.

Automated theorem proving for AI theory claims is limited by the accuracy of your provided assumptions and notation, meaning it cannot justify steps beyond stated dependencies. It relies on proof-status triage to identify missing assumptions rather than inventing them.