proof-writer

To convert an informal proof sketch into a rigorous mathematical proof, you provide the exact statement, assumptions, and partial sketch. The system then fills in missing steps to produce a fully justified, structured proof package without inventing gaps.

Yes, you can verify if a machine learning theory theorem is provable by submitting the claim and its assumptions. A feasibility triage checks whether the conclusion follows, classifying it as provable as stated, requiring extra assumptions, or not currently justified.

When a mathematical proof sketch has unjustified claims, the system performs truth-preserving correction by proposing the minimal corrected statement. It then proves this weakened version instead of attempting to justify the original false conclusion.

To complete missing steps for lemmas and corollaries in ML theory proofs, submit your partial arguments and notation. The system rigorously completes the dependencies and outputs a structured proof map with fully justified logical steps.

Yes, rigorous proof completion outputs a structured package containing the claim, explicit assumptions, notation, strategy, dependency map, and the fully justified proof. If a blockage occurs, it outputs a blockage report instead.

The best way to document proof dependencies for complex mathematical theorems is to generate a structured proof package. This includes an explicit strategy and a dependency map that traces the logical flow from assumptions to the final conclusion.