AI Cracks 10 Elite Math Problems: The End of High School Math Skepticism?

The "First Proof" Challenge Unveiled

The narrative surrounding the current capabilities of Large Language Models (LLMs) in high-level mathematics has often been tempered by skepticism, frequently referencing the performance of AI in competitions like the International Mathematical Olympiad (IMO). Following last summer's results, some critics dismissed the state-of-the-art as being proficient only in what amounts to advanced "high school math." In a direct challenge to this perception, a bold experiment—dubbed the "First Proof" challenge—was unveiled. This initiative centered on a bespoke set of ten novel, research-level mathematics questions meticulously crafted by active, practicing mathematicians based on problems arising naturally from their ongoing research.

The premise was intentionally stringent: external experts who held the actual solutions established a one-week window for participating LLMs to generate attempts. This was not a test of recalling known theorems but an active probe into emergent reasoning capabilities applied to unsolved or highly specialized problems. The implicit question posed by the proponents was whether frontier AI could breach the barrier separating high-level engineering or coding proficiency from genuine mathematical discovery.

AI Models Target Elite Mathematical Research Problems

This development represented a significant internal initiative aimed at fundamentally changing the perception of STEM research capabilities across next-generation AI models. By targeting problems vetted by domain experts as genuinely difficult and requiring original insight, the goal was to establish a new, far more challenging benchmark than typical academic benchmarks which can sometimes suffer from dataset contamination or familiarity. External researchers immediately recognized the significance of this challenge, viewing it as a crucial evaluation metric for determining the true frontiers of artificial intelligence development.

The effort focused heavily on an internal model currently undergoing advanced training. While the specific architecture remains proprietary for the time being, the announcement carried a distinctly optimistic tone regarding its potential. Researchers indicated they were hopeful that either this specific iteration, or an immediately improved successor, would be ready for broader public access soon, suggesting a high degree of confidence in the breakthrough potential demonstrated by the preliminary results.

The stakes were clear: success in solving these problems would signal an epochal shift, suggesting AI was moving from sophisticated mimicry to genuine, novel contribution in theoretical fields previously considered the exclusive domain of specialized human intellect.

Preliminary Success and Expert Confidence

The results emerging from the intensive, week-long sprint were striking. The internal model, operating under limited human supervision, managed to generate targeted attempts for all ten proposed problems. This in itself is a testament to the model's ability to parse complex, novel problem statements across diverse mathematical fields.

The critical measure, however, lies in expert validation. Based on the deep domain expertise required for verification—and acknowledging the inherent difficulty in proving the correctness of novel solutions—experts offered a preliminary assessment. They deemed that at least six solutions (specifically Problems 2, 4, 5, 6, 9, and 10) are highly likely to be correct. Furthermore, other attempted solutions exhibited structural integrity and promising lines of reasoning, suggesting that the model was close to cracking several others as well.

This preliminary success casts a long shadow over prior skepticism, strongly suggesting that the frontier of AI reasoning has moved beyond foundational mathematics and into territory previously reserved for doctoral-level research and specialized post-doctoral work.

Disclosure Timeline and Methodological Caveats

The team managing the evaluation provided a precise schedule for public disclosure, adhering to the timeline requested by the original problem creators. The solutions were slated for release after midnight (PT) on the following day, ensuring that the initial evaluation period was not contaminated by public discussion or retroactive adjustments based on external analysis.

To guarantee the integrity of the results against potential post-hoc tampering or claims of having solved the problems earlier, the authors provided cryptographic proof of the forthcoming document’s contents. The SHA256 hash of the solution PDF is explicitly stated: d74f090af16fc8a19debf4c1fec11c0975be7d612bd5ae43c24ca939cd272b1a. This hash allows anyone to verify the document’s contents against the published version at the specified time.

It must be stressed that this was, by the researchers’ own admission, a rapid side-sprint. The entire effort relied on querying one of the models currently deep in its training cycle, meaning the testing conditions were far from the ideal, meticulously controlled environment one would expect for a definitive benchmark release. This context colors the interpretation of the results, urging caution even amidst the excitement.

Evaluation Methodology Limitations

The team was transparent about the methodological shortcomings inherent in this fast-tracked evaluation. Crucially, during the initial modeling run designed to generate the raw proofs, researchers withheld proof ideas or mathematical suggestions from the primary model, ensuring the output was genuinely generated by the AI.

However, the path to the final presented solution was not purely autonomous. In a necessary concession to the timeline and the complexity of the task, limited intervention occurred post-initial generation. This involved asking the model to expand upon existing proof structures based on qualitative feedback provided by the domain experts. Furthermore, a secondary, publicly available model (ChatGPT) was manually leveraged in a basic loop solely for assisting the primary model with formatting, style checking, and ensuring the readability of the final mathematical prose.

A final caveat relates to selection bias: the solutions presented to the public represent the "best of a few attempts" that were then manually judged and curated by human researchers. While the core reasoning was AI-generated, the presentation was filtered, a necessary evil given the high standard required for research publication.

Future Outlook for AI Mathematical Benchmarking

The success of this informal, rapid challenge immediately establishes a new trajectory for AI evaluation. The team involved has explicitly committed to future iterations of the challenge, indicating that this demonstration was merely the prologue. The focus for the next phase will be on executing more controlled and rigorous evaluations, likely involving larger model ensembles and more comprehensive pre-screening of the problem sets to mitigate any potential methodological ambiguities identified in this first run.

This ongoing work reinforces the importance of initiatives like #1stProof, pushing the boundaries of what society expects from artificial intelligence. If these early results hold, the ability of AI to contribute meaningfully to the creation of new scientific knowledge—not just synthesize existing data—is closer than many predicted. The implications for mathematical education, foundational physics, and cryptography are vast, forcing a rapid reassessment of what constitutes 'intelligence' in the 21st century.

Source: Shared by @sama on Feb 14, 2026 · 4:24 AM UTC, via X. Link to Original Post