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OpenAI models helped researchers—operating as part of our Patch the Planet initiative—discover critical security flaws in PHP, an open-source programming language used to run an estimated 70% of websites. Over the past two months, we led a coalition of maintainers, researchers, and security providers to prepare fixes and protections ahead of disclosure. This duration was necessary because the fix was quite intricate and required a major change to the PHP engine. Every site using PHP can now safely upgrade their version to stay secure, in addition to applying mitigations from various security vendors. Due to the severity of these flaws and PHP’s widespread use, we consider this bug to be one of the most impactful vulnerabilities in recent memory. It also marks a new phase, where AI can find security bugs with internet-scale impact. This is an example of the Defender’s Window in action: to our knowledge, frontier AI was able to find this vulnerability ahead of attackers, giving defenders time to prepare. https://openai.com/index/coordinated-disclosure-of-critical-vulnerabilities/

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

Banger paper on evolving agent interfaces. Pay close attention to this new trend if you build with agents. Like optimizing harnesses, it's remarkable what you can achieve with agents that have access to better interfaces. Computer-use agents usually act through interfaces that people design by hand, so an agent may type a spreadsheet cell by cell when one line of code would do. Simular's Agent S4 has a meta-agent that builds those interfaces instead. It reads the task agent's trajectories, with no success labels, and writes new tools and notes on when to use them. With the evolved interfaces, an open 27B model reaches 21.3% on OSWorld V2, 3.8 times that of the same model with hand-built interfaces. That is above GPT-5.5 (13.0%) and Claude Opus 4.8 (20.6%), at about 4.3% and 1.5% of their cost. Population-based selection keeps the interface repositories that do best on validation tasks the meta-agent never sees, so the changes carry over to new tasks. On ScienceBoard, the evolved interfaces raise success from 41.4% to 58.6% with the same model. The code is open source. https://arxiv.org/abs/2610.28601 Chat with Paper: https://academy.dair.ai/papers/evolving-agent-interfaces-2610.28601

GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. https://openai.com/index/scaling-ai-research-for-complex-biological-data/

Watch live as SpaceX discusses our Q3 2026 earnings at 3:30pm CT today → https://x.com/i/broadcasts/1PKqrNbLZbPGb