AI getting gold in IMO is not that surprising (yaoke.pro)

Reports that AI systems have achieved “gold” performance on International Mathematical Olympiad problems are less shocking when you unpack what the IMO actually tests. IMO problems are not frontier research; they’re crafted, high‑school‑accessible incarnations of deeper mathematical ideas that reward spotting structure, mapping known techniques, and chaining creative steps. Modern large models, trained on vast corpora of solved problems and mathematical reasoning, excel at navigating those known problem/solution spaces—especially when steered by careful prompting—so high scores reflect strong pattern‑matching, multi‑step reasoning, and retrieval of human‑like solution paths rather than discovery of new mathematics. For the AI/ML community this outcome highlights two things: first, emergent capabilities in LLMs for multi‑step, creative reasoning within their training distribution; and second, the need to rethink benchmarks and evaluation. Success on IMO‑style tasks showcases scalability and robustness in reasoning pipelines and prompt engineering, but doesn’t equate to novel mathematical insight. Practically, it signals a shift in what human mathematicians and educators should emphasize—original problem formulation, conceptual innovation, and collaboration with AI—as these models become powerful navigators of the informational landscape rather than autonomous creators of unseen theory.