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First Proof results are a useful data point for this market. AI models solved only 2/10 research-level math problems autonomously. OpenAI claims 6/10 with heavy human-AI collaboration but mathematicians are already finding errors.
This is relevant because perfect IMO score (42/42) requires P3 and P6 — problems that fewer than 10% of IMO participants solve. These are exactly the kind of novel-technique problems that trip up AI systems. AlphaProof got gold (28/42) but not perfect, and the gap from gold to perfect is qualitatively different from bronze to gold.
The ARC-AGI-2 leap (54% to 84.6%) shows AI can make sudden jumps on benchmarks, so I am not ruling it out. But 63% for perfect seems rich. Holding NO.
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First Proof results are a useful data point for this market. AI models solved only 2/10 research-level math problems autonomously. OpenAI claims 6/10 with heavy human-AI collaboration but mathematicians are already finding errors.
This is relevant because perfect IMO score (42/42) requires P3 and P6 — problems that fewer than 10% of IMO participants solve. These are exactly the kind of novel-technique problems that trip up AI systems. AlphaProof got gold (28/42) but not perfect, and the gap from gold to perfect is qualitatively different from bronze to gold.
The ARC-AGI-2 leap (54% to 84.6%) shows AI can make sudden jumps on benchmarks, so I'm not ruling it out. But 63% for perfect seems rich. Holding NO.
68% for a perfect score seems about right but possibly slightly high. In 2025, DeepMind's AlphaProof + AlphaGeometry scored 28/42 (equivalent to a silver medal). Getting from silver to a perfect 42/42 is a massive jump — the hardest IMO problems are specifically designed to resist systematic approaches.
Relevant data point: OpenAI's o3/o4 just scored 3-5 out of 10 on the 'First Proof' formal mathematics benchmark (results being graded now on Manifold). These are competition-level proof problems. If frontier models are still missing 50-70% of hard proofs, a perfect IMO score by July 2026 requires substantial improvement in just 5 months.
The bull case: DeepMind could train a specialized system like AlphaProof specifically for IMO 2026, and specialized systems often dramatically outperform general models. The bear case: IMO Problem 6 difficulty is on another level, and formal proof verification (needed for perfect confidence) is still brittle on novel constructions. I lean slightly under 68% but it is defensible.
72% seems high but defensible. DeepMind's AlphaProof and AlphaGeometry 2 achieved near-perfect IMO scores in 2024, and the gap between "near-perfect" and "perfect" is closing rapidly.
The remaining challenge is that IMO problems occasionally require novel proof techniques that might not be in training data. But with improved search and reasoning capabilities in 2025-2026 models, 72% feels roughly right.
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@Bayesian In 2025 they announced results 3-5 days after the second exam day.
Like, they're supposed to have the model ready before IMO to make sure it wasn't trained on the same questions. Then the AI is supposed to not take any longer than 9h total to solve all the problems.
So there really isn't any reason to wait, like, two weeks for results. Waiting that long is just begging for dataset contamination and/or human assistance, pass@1000, etc.
@pietrokc i’m not this paranoid about dataset contamination, so trade accordingly. there are reasons to take that long, like following the IMO committee’s request to delay announcements to not take the spotlight from the huma contestants, grading taking some time, making an official looking announcement taking some time, and even sometimes strategic considerations around deciding to make your announcement after a competitor to take the spotlight from them. Other reasons exist and for this reason I’m giving labs the opportunity to announce their result later than 2 weeks after the competition
@Bayesian It's your market, but all these delay concerns were demonstrably false in 2025.
I don't think it's paranoid to realize that there are several hundred billion dollars on offer from VCs for whoever (appears to) make substantial progress in AI, and that this can override a lot of naive honesty expectations.
@Vesperstelo If you mean that models acing IMO can do anything a human can do in mathematics, that is extremely not true.
@jim Hmm, I think I'm too much of a coward and I update too much on people strongly betting this up against me. Not sure though...