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Learning Lean: Part 1

Motivation I’ve been captivated by the recent movement to popularize mathematics formalization through the Lean theorem prover, and this year I’m diving deeper into learning it. For those unfamiliar with this revolution, I highly recommend watching Kevin Buzzard’s talks on YouTube for an overview of why formal mathematics is generating such excitement in the mathematical community. The immediate benefits of formalization are well-documented: it helps catch errors in proofs and reduces the need for trust between collaborators since every step is mechanically verified. However, I believe there’s another compelling advantage that’s less frequently discussed: formalization enables a better separation of concerns in mathematical writing.

rkirov.github.io · Rado's Radical Reflections

Towards Autonomous Mathematics Research

Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from competition-level problem-solving to professional research, however, requires navigating vast literature and constructing long-horizon proofs. In this work, we introduce Aletheia, a math research agent that iteratively generates, verifies, and revises solutions end-to-end in natural language. Specifically, Aletheia is powered by an advanced version of Gemini Deep Think for challenging reasoning problems, a novel inference-time scaling law that extends beyond Olympiad-level problems, and intensive tool use to navigate the complexities of mathematical research. We demonstrate the capability of Aletheia from Olympiad problems to PhD-level exercises and most notably, through several distinct milestones in AI-assisted mathematics research: (a) a research paper (Feng26) generated by AI without any human intervention in calculating certain structure constants in arithmetic geometry called eigenweights; (b) a research paper (LeeSeo26) demonstrating human-AI collaboration in proving bounds on systems of interacting particles called independent sets; and (c) an extensive semi-autonomous evaluation (Feng et al., 2026a) of 700 open problems on Bloom's Erdos Conjectures database, including autonomous solutions to four open questions. In order to help the public better understand the developments pertaining to AI and mathematics, we suggest codifying standard levels quantifying autonomy and novelty of AI-assisted results. We conclude with reflections on human-AI collaboration in mathematics.

arxiv.org · arXiv.org

Gemini Deep Think: Redefining the Future of Scientific Research

Gemini Deep Think is accelerating discovery in maths, physics, and computer science by acting as a powerful scientific companion for researchers.

deepmind.google · Google DeepMind

Hilbert's Program and Infinity

The primary aim of Hilbert's proof theory was to establish the consistency of classical mathematics using finitary means only. Hilbert's strategy for doing this was to eliminate the infinite (in the form of unbounded quantifiers) from formalized proofs using the so-called epsilon substitution method. The result is a formal proof which does not mention or appeal to infinite objects or "concept-formations." However, as later developments showed, the consistency proof itself lets the infinite back into proof theory, through a back door, so to speak. The paper outlines the epsilon substitution method as an example of how proof-theoretic constructions "eliminate the infinite" from formal proofs, and how they aim to establish conservativity and consistency. The proof also requires an argument that this proof theoretic construction always works. This second argument, however, requires possibly infinitary reasoning at the meta-level, using induction on ordinal notations.

arxiv.org · arXiv.org

Generalized Decidability via Brouwer Trees

In the setting of constructive mathematics, we suggest and study a framework for decidability of properties, which allows for finer distinctions than just "decidable, semidecidable, or undecidable". We work in homotopy type theory and use Brouwer ordinals to specify the level of decidability of a property. In this framework, we express the property that a proposition is $α$-decidable, for a Brouwer ordinal $α$, and show that it generalizes decidability and semidecidability. Further generalizing known results, we show that $α$-decidable propositions are closed under binary conjunction, and discuss for which $α$ they are closed under binary disjunction. We prove that if each $P(i)$ is semidecidable, then the countable meet $\forall i\in \mathbb N. P(i)$ is $ω^2$-decidable, and similar results for countable joins and iterated quantifiers. We also discuss the relationship with countable choice. All our results are formalized in Cubical Agda.

arxiv.org · arXiv.org

PBLean: Pseudo-Boolean Proof Certificates for Lean 4

We present PBLean, a method for importing VeriPB pseudo-Boolean (PB) proof certificates into Lean 4. Key to our approach is reflection: a Boolean checker function whose soundness is fully proved in Lean and executed as compiled native code. Our method scales to proofs with tens of thousands of steps that would exhaust memory under explicit proof-term construction. Our checker supports all VeriPB kernel rules, including cutting-plane derivations and proof-by-contradiction subproofs. In contrast to external verified checkers that produce verdicts, our integration yields Lean theorems that can serve as composable lemmas in larger formal developments. To derive theorems about the original combinatorial problems rather than about PB constraints alone, we support verified encodings. This closes the trust gap between solver output and problem semantics since the constraint translation and its correctness proof are both formalized in Lean. We demonstrate the approach on various combinatorial problems.

arxiv.org · arXiv.org

Why formalize mathematics - more than catching errors

Why formalize mathematics - more than catching errors I read a good post by one of the authors of the Isabelle theorem prover, that got me thinking. The author, Lawrence Paulson, observed that most math proofs are trivial, but writing them (preferably with a proof assistant) is a worthwhile activity, for reasons similar to safety checklists - “Not every obvious statement is true.” As I have been a bit obsessed with doing formalized mathematics, this got me thinking about why I am excited to spend many hours recently writing formalized proofs in Lean for exercises from Tao’s Real Analysis (along with this recent attempt to write a companion to Riehl’s Category Theory In Context). On a very personal level, I just like math, computers and puzzles, and writing Lean proofs feels like doing all three at once. But I do believe formalization is important beyond nerd-snipping folks like me.

rkirov.github.io · Rado's Radical Reflections