HumanLean
Faithful Lean proofs, made for human review
AI-generated mathematics is increasingly accompanied by AI-generated Lean formalizations. Existing formalization agents optimize for compilation, but compilation alone does not guarantee faithfulness to the original argument, and unfaithfulness undermines the correctness guarantee that formalization is meant to provide. We introduce HumanLean, the first system designed to produce compiling, faithful Lean proofs for human review.
Unfaithfulness is widespread
We found widespread unfaithfulness in formalizations from OpenAI’s recent advances, Axiom’s Stacks project, and various projects listed on Papers with Lean. This significantly undermines the correctness guarantees for the original mathematics.
Navier–Stokes
In the formalization accompanying OpenAI’s Navier–Stokes proof, we identify 24 numbered results with substantive differences. These include 3 with statement or mathematical-object differences and 21 with proof-argument differences.
Quasi-Riemann
In the formalization accompanying OpenAI’s Quasi-Riemann proof, we identify 9 numbered results with substantive differences. These include 4 with statement or mathematical-object differences and 8 with proof-argument differences.
Axiom Stacks
Across five foundational chapters of Axiom Stacks, we identify 94 definitions and results with substantive differences: 6 definition mismatches, 1 statement mismatch and 88 proof replacements.
Various Formalizations on PapersWithLean
We found substantive differences in mathematical statements or proof arguments in 29 AI-assisted formalizations listed on Papers with Lean.
Explore findings in 29 formalizations
Easing human review of formalizations
Unguided faithfulness
With HumanLean (powered by GPT-6-Astra), we observed 100% faithfulness in both statements and proofs across our evaluation of 12 publications and the unit-distance proof without human guidance. High unguided faithfulness reduces the chance of human intervention and thus reduces expected workload.
Compositional formalization
HumanLean proves internal results and exposes external results as explicit axioms. This division is more natural and manageable for human readers.
Minimal formalization
Compared with a raw agent instructed to do the same compositional formalization, HumanLean uses roughly 90% fewer custom definitions per formalized goal across both benchmark models, with fewer axioms and duplicate encodings. A minimal formalization reduces reading difficulty and improves reusability.
Counts per formalized goal (lower is better). Duplicates are overlapping definitions of the same concept.
Interestingly, the stronger Astra model produces more definitions and duplicates per formalized goal in the raw-agent baseline. In HumanLean, it reduces verbosity, producing fewer definitions, proof declarations and duplicates per goal.
Example: a faithful unit-distance proof formalization
A formalization can change a statement, replace an argument, or assume a result the original proof sets out to establish. Thus, an unfaithful formalization cannot establish the correctness of the original proof, even if it compiles.
We formalize the unit-distance proof by OpenAI and compare our formalization with three other publicly available AI-assisted formalizations (plby, Logical Intelligence, and fbundle).
Importantly, we found all three compared formalizations unfaithful to the original proof.
| HumanLean | plby | Logical Intelligence | fbundle | |
|---|---|---|---|---|
| Unfaithful statement | None found | Theorem 2.3Requires a stronger geometric condition. | None found | Lemma 2.6Weakens the packing bound. |
| Unfaithful proof | None found | Prop. 3.2 Prop. 3.8Uses a different field-tower construction. | Prop. 3.2Replaces the discriminant argument. Lemma 2.6Replaces the norm argument with a root-of-unity argument. Prop. 3.7 / A.13Replaces the divisor-sum argument with a zeta-function bound. | Props. 3.2 / 3.8Assumes the internal field-tower construction. |
HumanLean: 4,618 subtotal / 4,618 total lines; plby: 14,647 subtotal / 975,620 total lines; Logical Intelligence: 8,615 subtotal / 31,501 total lines; fbundle: 4,021 subtotal / 5,805 total lines.
Subtotal sums definitions, assumptions, results/proofs and helpers, excluding external/background code. fbundle’s smaller subtotal reflects omitted formalization of a significant part of the proof, which is assumed as an axiom.
Explore all 25 Lean files
Lines exclude blanks and all comment-only lines, including block/doc comments. Gutter numbers are source positions.
Paper statement
More to come
We are extending evaluation to more important AI-generated proofs and will later open-source HumanLean. Our manuscript is currently under review.