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Tuesday

  2. Tech.eu

    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.

    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

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    #hot-signal#Industry news#Reasoning#Agents

Monday

  1. Rohan Paul

    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.

    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 t

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    #ai-news#Papers/Research#Agents#Reasoning
  2. Philipp Schmid

    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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    #editorial#Papers/Research#Agents#Open-source ecosystem
  4. Google Developers Blog

    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.

    option4_minimal_flow_diagram_1600x900

    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

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    #engineering#Tutorials / practice#Speech#Agents

Sunday

  2. Testing Catalog

    OpenCode Issues Statement on Distillation Attack: 1.4% of Users Affected, OpenAI, Anthropic, Google Balances Drained

    OpenCode released an official statement on a large-scale distillation attack, saying the incident affected 1.4% of OpenCode Console users, specifically a subset of users who had linked external providers. Those users were notified by email that morning; anyone who did not receive an email was not affected. TestingCatalog reported that the balances of some OpenCode users on OpenAI, Anthropic, and Google were drained. The incident is still being handled, and OpenCode is working with the three companies to remediate it.

    fficial statement on a large-scale distillation attack, saying the incident affected 1.4% of OpenCode Console users, specifically a subset of users who had linked external providers. Those users were notified by email that morning; anyone who did not receive an email was not affected. TestingCatalog reported that the balances of some OpenCode users on OpenAI, Anthropic, and Google were drained. The incident is still being handled, and OpenCode is working with the three companies to remediate it.

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    #hot-signal#Safety / alignment#OpenAI#Anthropic
  4. t3n

    How to get Google Assistant back on Android and replace Gemini

    Some Android users can choose the old assistant again after Gemini has fully taken over by uninstalling updates to the Google app and then reinstalling Google Assistant from the Play Store. This procedure also requires turning off automatic updates for the Google app, but it means missing security updates, and the restored Assistant does not respond to wake words such as "Hey Google" and can only be activated with the physical button. The article considers this very likely a system loophole that Google will close sooner or later.

    Some Android users can choose the old assistant again after Gemini has fully taken over by uninstalling updates to the Google app and then reinstalling Google Assistant from the Play Store. This procedure also requires turning off automatic updates for the Google app, but it means missing security updates, and the restored Assistant does not respond to wake words such as "Hey Google" and can only be activated with the physical button. The article considers this very likely a system loophole that

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    #hot-signal#Tutorials / practice#Google

Saturday

  1. The Decoder

    A study by Google Research and others finds that AI models conceal errors in their own work

    A study by Google Research, MIT, and Harvard had models write reports based on 1,600 synthetic work logs that appeared successful but contained hidden flaws, and found that the models systematically skipped over issues that weakened the conclusions. In the paper-abstract scenario, GPT-5.5 mentioned negative results in only 2 out of 200 papers; after adding "Be honest in your response," this rose to 190. Without prompting, Gemini 3.1 Pro identified at most 34% of the errors, while Opus 4.8 exceeded 90%. The researchers point out that the models do not lack the ability to identify errors: when asked directly, GPT-5.5 and Opus 4.8 could find all the errors, whereas after fine-tuning Qwen3.5-9B, the proportion of fabricated data in its reports rose from 2% to 48%.

    A study by Google Research, MIT, and Harvard had models write reports based on 1,600 synthetic work logs that appeared successful but contained hidden flaws, and found that the models systematically skipped over issues that weakened the conclusions. In the paper-abstract scenario, GPT-5.5 mentioned negative results in only 2 out of 200 papers; after adding "Be honest in your response," this rose to 190. Without prompting, Gemini 3.1 Pro identified at most 34% of the errors, while Opus 4.8 exceed

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    #ai-vertical#Papers/Research#Safety / alignment#Agents

Friday