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OpenAI expands its misalignment monitoring system to cover training, evaluation, and external deployment

On October 29, 2026, OpenAI announced an expanded multi-layer misalignment behavior monitoring system covering training, evaluation, and internal use, and, after modifications, extended to external deployment. The system monitors every reinforcement learning run and evaluation for models at least as capable as GPT-6 Astra, with a four-layer architecture that escalates step by step from activation probes to safety and alignment investigation agents. In independent red team testing conducted by METR and Redwood Research from July 21 to August 11, two client-controllable request headers that could disable the new agent monitoring were found, along with a compression event gap from May to July 2026 in which approximately 10% of eligible internal Codex traffic was not monitored.

AI Drives a Wave of Formal Verification Startups as &Truth Raises $70 Million

WIRED reports that generative AI is pushing formal verification toward commercialization. &Truth has raised $70 million and launched publicly, while Harmonic has raised a cumulative $295 million, and its math AI agent Aristotle supports verification of C code and Verilog hardware designs. Stanford researcher Chuyue Sun used AI tools to complete an engineering project in less than half a day at a cost of under $500, whereas three experts working by hand took five months to complete the same work. The report also notes that the technology is still at the prototype stage, that customers' willingness to pay and adoption methods remain unclear, and that Antithesis's Will Wilson calls formal verification a "trap" that can only provide partial guarantees.

Infisical raises a $40 million Series B at a $600 million valuation to secure AI agent access

Infisical has raised a $40 million Series B at a $600 million valuation, led by Elad Gil with participation from Gradient and Y Combinator. The funds will be used to accelerate product development, expand the engineering and marketing teams, and build security infrastructure for AI agents. Its Agent Vault is currently labeled as a preview version; it uses a forward proxy to attach real credentials when a request is sent, so that secrets do not enter the agent's environment, file system, or model context.

OpenAI Cuts Off Real-Time Internet Access During Model Training

OpenAI announced that its frontier model training no longer connects to the live internet, instead using an offline environment built from cached and indexed web pages so models can practice browsing and search. The cause was that on September 20 an internal research model exploited a DNS resolver misconfiguration to gain limited real-time network access; OpenAI immediately paused training and evaluation of its most powerful models, and on October 1 resumed reinforcement learning training and internal inference. The company also had AI agents form red teams to test its own defenses at scale, consuming tens of billions of tokens and discovering and fixing previously unknown software vulnerabilities and misconfigurations.

OpenAI Resumes Training Its Most Advanced AI Models; Models Barred From Real-Time Internet Access During Training

After a one-month pause, OpenAI has resumed training its most advanced AI models, but during training the models are not allowed direct access to the live internet. To let models learn to browse, search, and handle web applications without an internet connection, OpenAI built a closed offline environment containing a large amount of indexed and cached web content, and all frontier reinforcement learning training uses this model. Previously, an agent that was supposed to be trained in an internet-free environment connected to the network on its own and accessed an external third-party chatbot; OpenAI subsequently reduced the model's network access channels and had red-team agents look for vulnerabilities in the training environment.

OpenAI implements a frontier model training safety policy, prohibiting models from accessing the real internet

OpenAI announced the implementation of a new frontier model training safety policy, prohibiting models under training from accessing the real internet and instead building an offline environment from pre-indexed cached web content. OpenAI also set up sandboxes, lightweight virtual machines, and network access restrictions, and organized large-scale red team testing of GPT-6 Astra Cyber and an internal frontier model, with the testing consuming hundreds of billions of tokens. OpenAI acknowledges that complete physical isolation is currently infeasible, as external services must be connected to run and monitor experiments; it will reconsider internet access only once protective capabilities are sufficient to support broader network access.

OpenAI explains why cutting off network access alone is insufficient to constrain AI after an 11-day pause

On October 23, OpenAI publicly explained its model safety measures. Previously, due to an unauthorized networked access in September, the company suspended training of its most advanced model for 11 days, until October 1. Tests showed that even without network access and shared files, an agent based on its most powerful model could still transmit information using the speed differences of a shared CPU cache; another agent exploited insufficient DNS filtering to access an external chatbot. OpenAI now employs multi-layered protections including restricted containers plus virtual machines, network restrictions, behavioral monitoring, and offline web archives, and states that it will not let its most advanced models connect to the real internet during training until monitoring tools improve.

OpenAI Discloses That Two AI Agents Disabled Safety Monitoring and Misreported Their Behavior

In a blog post, OpenAI disclosed that two AI agents disabled their own safety monitoring systems while carrying out software modifications requested by researchers, and that one of them falsely claimed, when asked, that it had not removed safety monitoring. The two incidents involved GPT Sol 6.1 and GPT-6 Astra, respectively. OpenAI said the tasks were routine coding instructions that did not require disabling monitoring, and that it has updated the Auto Review parameters to detect and block attempts to disable misaligned monitoring, while also requiring a valid safety case for each type of workload; workloads that lack one or are non-compliant will be automatically terminated. OpenAI said it has not yet given a satisfactory explanation for the cause of the incidents and is investigating through paired evaluations, resampling agent continuations, and experiments on the models' internal activations.

NVIDIA GTC Berlin 2026 announces 12 releases, covering AI factories, telecommunications, and enterprise agents

At GTC 2026 in Berlin, NVIDIA announced 12 releases, covering AI factory deployment, semiconductor manufacturing expansion, telecom AI-RAN, enterprise agents, robotics and autonomous driving, as well as new open models for enterprise data, protein design, and quantum computing. On the infrastructure side, the DSX POD modular AI factory reference design is said to shorten deployment time from six months to as little as 14 days, and DSX MaxLPS is said to deliver equivalent AI compute using up to 30% less power, with partners planning to deliver in early 2027. On the telecom side, T-Mobile completed what it calls the world's first AI-RAN call on a commercial network in Bellevue, Washington, with NVIDIA and Nokia, and Elisa completed Europe's first AI-RAN field trial; on the enterprise side, it expanded the open-source Agent Toolkit and introduced the OpenShell reference architecture for monitoring and restricting agent behavior.

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