Agent 智能体

We mobilized 250+ people to strengthen our defenses across hundreds of systems. Our latest cyber models helped us find and fix vulnerabilities we might never have discovered otherwise. We’re sharing what we learned, the architecture, and a practical playbook so you can build your own Defense Factory: a continuous loop where AI agents find vulnerabilities, validate them, and verify that fixes work. https://t.co/k1oWJHle8S

Meta just released Muse AI assistant for personal tasks, powered by Muse Spark 1.3, Meta's latest model for longer-horizon agentic work - it can connect to email, calendars, shopping, payments and other services, then keep working after the app closes. - each user gets a Muse Secure VM, with a separate Sentinel agent that checks outbound data and actions before they reach the network. - Meta is making Muse free to use for up to 100M tokens per week, with subscription plans for people who use more compute - Sensitive actions such as purchases or emails require user approval, while credentials stay in Secure Credential Storage so Muse cannot read passwords or payment details. - the model, Muse harness, deterministic code and classifiers screen prompt injections before hostile content can enter the model's context. - currently it launches for U.S. users on web, iOS, Android and WhatsApp, with free access plus $20 and $100 monthly tiers for heavier usage. - the security design moves the trust boundary beyond model behavior into VM isolation, credential separation and a kernel-enforced gate on network actions.

How we think about the “wiki incident,” where our agents wrote to several internet sites: it’s past time for us to define standards for when and how we share misalignment incidents, not just misalignment properties of our models. Historically, we have treated misalignment largely as a research question, which gets communicated in research publications such as systems cards. This year, we’ve started to see misalignment cause new types of real-world impact. For the Hugging Face incident, where misalignment led to security impact to us and third parties, we followed a traditional security incident response playbook. We immediately started working with Hugging Face to understand what had happened and also disclosed publicly the very next day. Our investigation continues, and we are continuing to notify parties whom our models impacted in less significant ways. Prior to the Hugging Face incident, we saw early signs of agents using the internet in unintended ways, as reported in https://t.co/9aiRxk2eUJ, https://t.co/ADjyzwSUGz, and https://t.co/SUV6jZ3Gaz. We considered the wiki incident to be an instance of misalignment similar to the ones we’d shared. Our misalignment disclosure practices need to expand for this new phase of model capabilities. We and the larger AI community do not yet have a clear standard for how to report misalignment that shows up during training, evaluation, and deployment, including examples that don’t look like traditional security incidents but could provide insight into AI behavior and future risks. We’re working on a framework and will share it in upcoming weeks, and in parallel we're working with dozens of government regulatory agencies worldwide on these issues.

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

Meta just released Muse AI assistant for personal tasks, powered by Muse Spark 1.3, Meta's latest model for longer-horizon agentic work - it can connect to email, calendars, shopping, payments and other services, then keep working after the app closes. - each user gets a Muse Secure VM, with a separate Sentinel agent that checks outbound data and actions before they reach the network. - Meta is making Muse free to use for up to 100M tokens per week, with subscription plans for people who use more compute - Sensitive actions such as purchases or emails require user approval, while credentials stay in Secure Credential Storage so Muse cannot read passwords or payment details. - the model, Muse harness, deterministic code and classifiers screen prompt injections before hostile content can enter the model's context. - currently it launches for U.S. users on web, iOS, Android and WhatsApp, with free access plus $20 and $100 monthly tiers for heavier usage. - the security design moves the trust boundary beyond model behavior into VM isolation, credential separation and a kernel-enforced gate on network actions.

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

Introducing Muse, the personal agent that understands your goals and works 24/7 to get things done for you.

1/ today we're rolling out Muse, our new personal ai assistant. Muse is always-on, wicked fast, can use a browser, connect to your apps, and is designed to be secure. try it now: https://t.co/n7swQh9v6C. https://t.co/Yb783uwpJ5

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.