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Google Ads will launch financial services verification in the US and Canada in early 2027

Google announced that in early 2027 it will expand its Financial Services Verification program to the United States and Canada, requiring financial advertisers to prove they are authorized by regulators before running ads, in order to stop brand impersonation and financial scams. The program currently covers 42 countries and about 2 billion internet users; in 2025, Gemini has helped the system block more than 99% of violating ads before they are served. Google is partnering with the American Bankers Association in the United States and the Canadian Bankers Association in Canada, and will provide advertisers with registry lists and onboarding guidance before enforcement begins.

Google launches a small business hub and brand and finance tools for Gemini

Google has launched a business landing page for Gemini aimed at small business owners, where they can manage branding, finances, and customer insights, and connect Google Business Profile, Google Ads, Google Workspace, as well as third-party apps such as Adobe, Canva, Dropbox, Shopify, Wix, Zoho, and Xero. Users can define their brand style by uploading their website, brand kit, fonts, or color palettes, and with Nano Banana 2.1 generate marketing visuals with a consistent style; Gemini can also turn spreadsheets, receipts, and purchase records into financial charts and interactive models. These features will roll out gradually worldwide to all Gemini app users over the coming days.

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Google integrates TorchTPU into TorchTitan to optimize model training for TPUs

Google integrates TorchTPU into Meta's TorchTitan; Cloud TPU can now train PyTorch models directly through the native backend torch.device("tpu"), without rewriting them into JAX. The TorchTitan TPU branch provides pretraining recipes for Llama 3 70B/405B, Qwen 3 8B/32B/235B MoE, and FLUX.1 12B, and replaces standard implementations with Pallas kernels such as Splash Attention. On FLUX, SimpleFSDP brings TorchTitan to 99% performance parity with JAX MaxDiffusion.

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Former Google DeepMind researcher founds London AI company Metis Reasoning, focusing on compute-efficient reasoning

Metis Reasoning, a London AI company co-founded by former Google DeepMind research lead and AlphaGo co-creator Thore Graepel, has officially launched, seeking a compute-efficient path distinct from the frontier models of Anthropic and OpenAI, and is believed to have raised tens of millions of dollars in initial funding. The company has a team of 12 people, including researchers, mathematicians, robotics experts, and technology entrepreneurs. It believes that LLMs have fundamental limitations in reasoning, that results are difficult to verify, and that there is a lack of audit trails; its approach is more verifiable, more robust, and safer, and it says it has already achieved some positive results on frontier reasoning problems at a cost far below that of frontier models. Metis has not yet announced a release date for its first AI model.

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Google and Meta jointly launch TorchTPU, enabling PyTorch to run natively on TPUs

Google and Meta's PyTorch teams have collaborated to launch the open-source project TorchTPU, providing a native PyTorch backend for Google TPUs; migration only requires changing model.to("cuda") to model.to("tpu"). The project supports both eager and torch.compile modes, adds a new DeferAndFuse eager mode, covers about 450 Aten operators, and has been verified to run out of the box on 4,917 Hugging Face models, with a Tier 1 pass rate of 98% and Tier 2 numerical consistency relative to GPUs of 97%. According to the official announcement, it has already been used in the inference and training production workloads of some major Google Cloud customers, and is integrated with vLLM, SGLang, TorchTitan, Ray, and TorchRec.

Princeton and Google Paper: 4B Decision Model Shares Agent Tool Selection, Cutting Costs by 81.1% to 89.0%

A new paper from Princeton University and Google finds that the most accurate decision model on isolated problems solved only 69.5% of agent tasks, while a 4B model trained on agent states solved 91.5%. Its harness delegates each step's tool selection to a fast decision model, adopting its answer only when confidence exceeds a threshold fitted on the agent's own runs, and it trained AgentDecision-4B on the states the agent actually reached (including states after mistakes). On tasks similar to the training data, such 4B models cut agent costs by 81.1% to 89.0% and boost speed by up to 2.5x, with a success-rate gap within 3 percentage points; without a threshold, delegating all tool selection drops the success rate by 19.0 percentage points. The paper is available at arxiv.org/abs/2610.21412.

Google Research releases Harness²: agents modify their own harness

Google Research has released the Harness² framework, which lets agents improve their own harness while the model remains frozen. Gemini 3.5 Flash gains 8.4 points on WorkBuddy-Bench and 7.5 points on JobBench, and improves on all 3 benchmarks. Skills and scripts account for about 50% of the edits; removing skills costs 5.3 points.

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Google Developers Blog publishes ADK voice agent tutorial: using streaming tools to eliminate silence

Google Developers Blog published a step-by-step tutorial on how to build a voice agent with ADK and gemini-3.8-live that does not stay silent for long periods. The core approach is to change tool functions from return to yield to make them streaming tools, or to use FunctionTool's response_scheduling to deliver results when idle; in the sales report benchmark flow, the agent's time to first speech dropped from 14.4 seconds to 1.2 seconds. The tutorial also covers the stop_streaming cancellation mechanism, preventing duplicate calls, and the trade-offs among the three scheduling modes WHEN_IDLE, INTERRUPT, and SILENT. It requires google-adk >= 2.10.0 and uses run_live bidirectional sessions.

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