One of the core promises of advanced AI is its potential to accelerate scientific discovery for the benefit of all. By helping researchers explore more ideas, test them faster, and translate findings into real-world impact, AI is poised to become an indispensable partner in the lab. Over the past year, OpenAI has worked closely with scientists across mathematics, physics, biology, and computer science to understand precisely where AI can assist—and where current systems still fall short. With the release of GPT‑5.2, those early gains are now becoming more consistent and reliable.
Where Precision Matters Most
GPT‑5.2 Pro and GPT‑5.2 Thinking represent the strongest models yet for scientific and mathematical work. Robust mathematical reasoning is foundational for reliability in technical domains; it allows models to follow complex, multi-step logic, maintain consistency across calculations, and avoid subtle errors that can compound in real-world analyses—from running simulations and statistics to forecasting and modeling. Improvements on demanding benchmarks like FrontierMath reflect not just a narrow skill, but stronger general reasoning and abstraction. These are capabilities that translate directly into scientific workflows, including coding, data analysis, and experimental design.
These advancements are also closely linked to progress toward more general intelligence. A system that can reliably reason through abstraction, maintain consistency across long chains of thought, and generalize across domains exhibits traits foundational to AGI. These are not task-specific tricks, but broad, transferable reasoning skills critical across science, engineering, and real-world decision-making.
The performance speaks for itself. On GPQA Diamond, a rigorous graduate-level benchmark, GPT‑5.2 Pro achieves 93.2%, with GPT‑5.2 Thinking close behind at 92.4%. On the expert-level FrontierMath (Tier 1–3) evaluation, GPT‑5.2 Thinking set a new state of the art, solving 40.3% of the problems.
A Groundbreaking Case Study in Statistical Theory
The capabilities of GPT‑5.2 extend far beyond answering test questions. We are now regularly seeing frontier models contribute solutions to previously unsolved—and increasingly subtle—open problems in mathematics and the sciences.
A compelling case study involves a longstanding question in statistical learning theory: “If you collect more data, do your results reliably get better?” Intuitively, we expect the answer to be yes—that a learning curve tracking error should monotonically decrease with more data. However, research sparked by a 2019 open problem showed this intuition can fail, even in simple setups, leading to non-monotonic curves where adding data increases error.
One core, textbook case remained unresolved: what happens in the cleanest scenario with a correct statistical model and normally distributed data? Researchers knew small changes could break monotonicity, but the answer for this fundamental case was unknown.
A new paper, On Learning-Curve Monotonicity for Maximum Likelihood Estimators, demonstrates that in this clean setting, intuition prevails—more data does reliably improve learning. What makes this paper remarkable is how the proof was obtained. The authors did not provide a strategy or outline. Instead, they asked GPT‑5.2 Pro to solve the open problem directly. The model generated a complete proof, which was then meticulously verified, reviewed, and validated by external subject-matter experts.
Furthermore, through simple follow-up questions, GPT‑5.2 Pro extended the result beyond the original problem to higher-dimensional settings and other common statistical models. Throughout this process, the human role remained firmly centered on verification, clear writing, and contextual understanding, rather than supplying the mathematical scaffolding.
The Path Forward for AI in Science
This result points to a powerful direction for AI-assisted research, particularly in domains with strong theoretical foundations like mathematics and theoretical computer science. In such settings, frontier models can help explore proofs, test hypotheses, and identify connections that might otherwise require substantial human effort to uncover.
It is crucial to emphasize that these systems are not independent researchers. Expert judgment, rigorous verification, and deep domain understanding remain irreplaceable. Even highly capable models can make mistakes or rely on unstated assumptions. However, they can also produce detailed, structured arguments worthy of careful human study and refinement. Reliable progress with AI therefore depends on workflows that keep validation, transparency, and collaboration firmly in the loop.
Viewed as a case study, this illustrates an emerging mode of research practice. Models like GPT‑5.2 can serve as powerful tools for supporting mathematical reasoning and accelerating early-stage exploration. The responsibility for correctness, interpretation, and context stays with human researchers. Used thoughtfully, such systems can help streamline significant aspects of theoretical work without displacing the central, irreplaceable role of human judgment in scientific inquiry.
Published: December 11, 2025 | Author: OpenAI