We present UniMath-35B-A3B, an open-source olympiad-level mathematical reasoning model with 35 billion total parameters and 3 billion activated per token. Developed by UniPat AI, UniMath-35B-A3B is obtained by further post-training Qwen3.6-35B-A3B on fine-grained proof-synthesis data covering the full arc of a proof: initial attempt, self-critique, obstruction diagnosis, targeted repair, and final synthesis. This activates a reusable self-evolving reasoning capability rather than a fixed answer format, and at test time UniMath-35B-A3B spends additional compute auditing, repairing, and reconciling its own proof attempts instead of resampling blindly. With this test-time self-evolution, UniMath-35B-A3B reaches human gold-medal-contestant level on IMO 2025 (35/42) and USAMO 2026 (36/42), and scores 86.0% on IMO-ProofBench.
Fine-Grained Proof-Evolution Data: Training trajectories supervise how a proof changes, not only what the final proof says — an initial route with visible proof obligations, a critical self-assessment naming the first real obstruction, a targeted repair or reroute that preserves earned mathematics, and a final synthesis that fuses drafts, critiques, and surviving routes into one rigorous argument.
Test-Time Self-Evolution: At inference time the model reuses this learned proof-editing loop instead of blind resampling — it samples and self-ranks a pool of candidate proofs, refines the strongest non-perfect candidates by repairing their first real obstruction, and merges surviving proofs so a clean lemma from one route can rescue a stronger route from another.
Adaptive Inference Compute: The self-evolution loop short-circuits straight to synthesis once enough self-verified proofs accumulate, so extra inference budget is spent only where a problem is genuinely hard.
1@misc{unipat2026unimath,
2 title = {UniMath: From Proof-Synthesis Data to Test-Time Self-Evolution},
3 author = {UniPat AI},
4 year = {2026},
5 url = {https://unipat.ai/blog/UniMath},
6}
