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LLM agents increasingly act on information retrieved from public sources they treat as credible, which turns a manipulated recommendation into a transaction, a dependency, or a decision with physical consequences. The security literature models this threat as instruction injection, in which an adversary writes a command into retrieved content and deployed defenses learn to recognize command syntax. We show that an attacker can instead steer an agent through the agent's own harm-avoidance. A single fabricated hazard, written as an ordinary review, leads the agent to disqualify the legitimate options and select the attacker's target by its own reasoning, with no command for a detector to recognize. We call this voluntary servitude. These payloads steer agents about as often as instruction injection (60.4% vs. 66.0%), and deployed injection detectors score them as benign, so behind a filter they are 1.7-4.4x more effective. We measure this at the generation stage across six models and ten consumer-service domains, for three attack families, and show that it transfers to production browser agents, outperforming instruction injection, and to a retrieval pipeline (1.0-57.0% end-to-end success, depending on the retriever). We then show that a corroboration requirement added to the agent's instructions removes roughly 95% of the exposure against a non-adaptive attacker without retraining. With the requirement in place, the agent still reads the planted hazard and no longer acts on it.

A research prototype for zero-knowledge proofs of AI inference. Not maintained. - anthropics/FlashProve

Starting today with the iOS 27.3, iPadOS 27.3, macOS 27.3, and visionOS 27.3 betas, a new Developer Preview enables any developer to integrate their app with Siri AI, giving businesses and developers new ways to reach and serve their customers.Siri AI, an entirely new version of Siri that is profoundly more capable, conversational, and personal, is designed to be deeply integrated into Apple’s operating systems and the apps people use every day, including the apps from our vast ecosystem of developers. Thousands of developers have already built integrations with Siri to let users ask for directions, manage a to-do list, send a message, or catch up on an audiobook. Siri AI is available in many languages, including English, French, Japanese, Korean, Portuguese, and Spanish and will soon expand to support Traditional Chinese, Norwegian, Turkish, and Vietnamese. People around the world are turning to Siri AI to get things done every day, which means more customers can discover and use apps just by asking.Today’s preview allows any app to bring its content and actions directly into Siri AI by using App Intents, making it easier for all apps to tap into Siri AI’s newest capabilities, including personal context understanding, onscreen awareness, natural language, and more. So people can just ask for help in their own words, and Siri AI carries out any custom action you’ve built, from checking a delivery to booking a reservation to placing an order. For complex requests, we’re also introducing skills to let you teach Siri AI how to combine several actions into a single experience. And Siri AI can now surface any entity in the Spotlight semantic index, not just the ones that fit an existing schema.We started rolling out these new capabilities across many Apple apps, including Calendar, Files, Finder, Journal, Mail, Maps, Messages, Music, Notes, Photos, Preview, Reminders, Safari, Shortcuts, and Weather, so users can do things like find a coffee shop they visited last week or archive files that haven’t been opened in over a year. There are many more actions available now as examples for you to reference. To see what you can do in each app, just ask Siri AI.This developer beta offers a sneak peek at Siri AI app intents for developers. With this early Developer Preview, your app can work directly with Siri AI, giving you a first look at new ways to reach and serve your customers. This preview provides early access before first shipping to customers in a few months; and with submission to the App Store opening up in the spring, developers have time to explore, build, and help shape their experiences before launch. Try it out and share your feedback.As you explore early, we will follow up with more details about the terms and usage policies for experiences enabled through Siri AI. More to share soon.To learn more about the new APIs, how to use them, and how to evaluate your app’s experience with Siri AI, read new documentation. You can also connect with Apple engineers in Apple Intelligence Group Labs on November 4–5 to ask questions, get advice, and follow the discussion.Sign up for November 4, 9–10 a.m. (PT)Sign up for November 5, 6–7 p.m. (PT)
Siri AI is available in beta in iOS 27.3, iPadOS 27.3, macOS 27.3, watchOS 27.3, and visionOS 27.3 and requires an Apple Intelligence-enabled device set to a supported language. It is available in English, French, Japanese, Korean, Portuguese, and Spanish. Some features may not be available in all regions or languages.

Siri AI is currently unavailable in the EU in iOS, iPadOS, and watchOS. Apple is working constructively with the EU in an attempt to find a path forward.

Siri AI is not available for users under 13.

Certain Apple Intelligence features that rely on server-side models are subject to daily usage limits, including but not limited to Siri AI, intelligent photo editing tools, Image Playground, and AFM 3 Cloud models in Shortcuts. Daily limits may vary by feature, request complexity, system demand, system policies, and other factors. Expanded access to such features will be available for a fee in the future. Use of these features is subject to the Apple Intelligence terms and conditions. Learn more at support.apple.com/148587.

Venture capital is moving into the physical infrastructure behind the next wave of technology. Space communications, advanced semiconductors, autonomous robots, missile technology, and industrial automation dominated the most significant startup funding announcements over the past 24 hours. Northwood Space led the day's verified dollar-denominated rounds with a $375 million Series C at a $2 billion

WATERTOWN, Mass., November 02, 2026--Tutor Intelligence, the robotics research and deployment company, today announced its $75 million Series B led by Andreessen Horowitz, bringing the company’s total capital raised to $117 million. The round will support Tutor’s frontier robot foundation model training, as well as the manufacturing and deployment of Sonny and Cassie, Tutor’s robot workers, which run the company’s models and work jobs across the United States. Union Square Ventures and Neo, whic

I capitali nel Tech Made in Italy crescono, ma i round diminuiscono. Ecco la nuova sfida per startup, scaleup e investitori italiani nel 2026.

DeepSeek has unveiled a new option to quickly create custom plugins that help users to tackle a range of complex tasks, the latest move by the Chinese lab to bolster its AI agent offerings.

OpenAI plans community agreements and infrastructure commitments as it prepares to rebrand data centers amid growing public opposition

Order placed, order confirmed, order cancelled, Amazon is allegedly pulling the plug on GPU purchases, and even Epic’s Tim Sweeney got caught up in it

Qualcomm today is announcing that they have completed initial validation on their High Bandwidth Compute technology. The DRAM-stacked-upon-compute architecture moves on to commercialization, with Dragonfly AI250 set to sample in the first half of 2027

AI Field Notes #129

OpenAI is putting ChatGPT and Codex on one token meter in December. A long coding session now spends the same budget as your chats.

- Anthropic's amended IPO filing: a future Claude may "imminently" hit its threshold for AI that speeds up its own progress
- Same filing: $17B Q…

Qwen3.8-Flash-Next on any consumer hardware: one-click install for Windows / Linux. Strata inference engine, OpenAI/Anthropic API on localhost, optional image input. - Niko1221/Strata

Looped transformers are a parameter-efficient alternative to standard transformers for language modeling. By reapplying the same layers across multiple loop steps, recent looped language models match the performance of non-looped models several times their size. While this architecture effectively reduces parameter memory, each loop step still stores its own keys and values, so the key-value (KV) cache grows linearly with the number of loop steps and becomes the main memory bottleneck. We observe that the KV cache of looped models is highly redundant along the loop axis and that the loop steps differ substantially in their sensitivity to quantization. Based on these observations, we propose $\textbf{LoopVQ}$, a KV cache vector quantization method for looped transformers that codes channels of multiple loop steps jointly, and fine-tunes the codebooks to match the output of the full-precision model. At 0.5 bits per element, LoopVQ outperforms existing KV cache vector quantization methods for standard LLMs on general, long chain-of-thought reasoning, and long-context retrieval tasks with Ouro and Huginn models. Its design is orthogonal to these methods, and combined with TaSQ, it retains much of the BF16 accuracy even at 0.25 bits per element. We also implement LoopVQ in SGLang, where it nearly halves the decode latency at a 32K-token context and raises the peak serving throughput by up to $4.3\times$ and $6.1\times$ on the 1.4B and 2.6B Ouro models compared to the BF16 baseline.

Fine-tuning a large language model (LLM) on harmful data in a narrow domain, such as bad medical advice, can make it broadly misaligned. How realistic this risk is remains unclear, since models are very rarely fine-tuned on explicitly harmful data in practice. In this work, we show that fine-tuning on data the model believes to be false but that has no harmful content, can also induce broad misalignment. Across mathematics, trivia, cooking and sports, the more strongly the model disbelieves the answers it is trained on, the more misaligned it becomes. In mathematics, misaligned answers rise from 4% to over 60% as the training answers become more wrong, with little loss of general capability. After fine-tuning, the model states the wrong answers but its multiple-choice probabilities still prefer the true ones, which suggests that misalignment comes from this internal inconsistency. We then study a more realistic setting, where a single fact is updated, as in knowledge editing or continual learning. The same relationship holds and the more the model still believes the true fact after training on the false one, the more misaligned it becomes. When we frame the same false fact as part of a coherent story, the model comes to believe it more deeply and becomes less misaligned than when we instruct it to assert the false fact. Content filters and persona vectors cannot tell these two framings apart, even though they lead to different levels of misalignment. These results suggest a risk for model updates, where training a model that does not adopt the new fact as true can induce misalignment even when the data looks harmless.

The five-year-old startup raised a $1.3 billion round led by Bessemer.

Two startups tackling the data center water problem are joining forces in a $16 million deal.

Here's a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation?

The one-year-old startup is now valued at $300 million.

OpenAI said that ChatGPT and Codex will draw usage from the same allocation pool after this change.

Russian owners of non-state data centers will be required to reserve 10% of their capacity to help keep Russia's IT infrastructure running, according to a decree signed by President Vladimir Putin on Monday.

Centuries-old stone steps on Mount Taishan turned into an unlikely racetrack on Monday as nearly 90 robot teams from leading enterprises, universities and research institutes gathered in Tai'an, East China's Shandong Province, for the inaugural CMG World Robot Mount Taishan Climbing Competition.