We’re introducing a framework to measure progress toward AGI, and launching a Kaggle hackathon to build the relevant evaluations.
OpenAI reflects on ten years of progress, from early research breakthroughs to widely used AI systems that reshaped what’s possible. We share lessons from the past decade and why we remain optimistic about building AGI that benefits all of humanity.
arXiv:2606. 12683v1 Announce Type: new Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.
Our mission is to ensure that artificial general intelligence—AI systems that are generally smarter than humans—benefits all of humanity.
A vision for the future of AI, focusing on access, safety, and shared prosperity as OpenAI works to ensure AGI benefits everyone.
The paper argues that large language models (LLMs) are evaluated too narrowly, focusing on isolated technical metrics rather than holistic, developmental, and societal aspects. It proposes a diagnostic ontology that links evaluation dimensions to the LLM training pipeline, turning evaluation into a root‑cause analysis tool. The authors introduce an anthropomorphic framework—IQ, PQ, EQ, and VQ—to assess LLM capabilities, operationalize it with a modular architecture, and validate it through meta‑analysis of over 200 benchmarks, outlining key challenges and future directions.
At OpenAI, we proactively adapt, including by building comprehensive security measures directly into our infrastructure and models.
arXiv:2607. 14673v1 Announce Type: new Abstract: Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public benchmarks rarely match a team's users, context, or policies, and human review is often tedious to scale.
arXiv:2510. 15236v2 Announce Type: replace Abstract: Contemporary AGI evaluations report multidomain capability profiles, yet they typically assign symmetric weights and rely on snapshot scores.
Ten years since AlphaGo, we explore how it is catalyzing scientific discovery and paving a path to AGI.
arXiv:2602.24055v5 Announce Type: replace Abstract: This study proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI syste...
arXiv:2605. 28508v2 Announce Type: replace Abstract: Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality.
arXiv:2509. 14474v3 Announce Type: replace Abstract: The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-level performance versus replicating human-like cognitive processes.
