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Thursday

Wednesday

  2. Sundar Pichai

    Pichai thanks Big Technology podcast for having him

    Sundar Pichai posted his thanks to Alex Kantrowitz for inviting him to record the Big Technology Podcast, calling it a great conversation. The episode discussed topics including Google's investment in the AI frontier, recursive self-improvement (RSI), and the balance between DeepMind's compute needs and Google Cloud's profits.

    Matched fieldsSummary

    #editorial#English text is being prepared.
  3. Josh Woodward

    Google executive relays Sundar's remarks on frontier AI commitment

    Josh Woodward quoted Sundar Pichai as saying that Google is "so committed to being at the frontier, it's in our DNA," and called on everyone to join in building it. The post also cited Alex Kantrowitz's interview with Pichai, covering topics including Google's frontier AI strategy, recursive self-improvement, and balancing DeepMind's compute needs with Google Cloud profits.

    Matched fieldsSummary

    #hot-signal#English text is being prepared.#Google

Tuesday

  1. Fortune AI

    Gray Swan relaunches Arena red-teaming competition, targeting the AI agent era

    AI security company Gray Swan on Tuesday launched a redesigned Arena platform, where contestants compete for prize money by tricking AI models into breaking through their guardrails, adding a sandboxed testing environment for indirect prompt injection. The new platform offers beginner-friendly challenges, training certificates, and a pathway for top performers to enter the Gray Swan red-team community and take part in real-world testing. Gray Swan conducts pre-release stress testing for frontier labs such as Anthropic, OpenAI, and Google DeepMind, and its Arena community has about 15,000 members.

    e for prize money by tricking AI models into breaking through their guardrails, adding a sandboxed testing environment for indirect prompt injection. The new platform offers beginner-friendly challenges, training certificates, and a pathway for top performers to enter the Gray Swan red-team community and take part in real-world testing. Gray Swan conducts pre-release stress testing for frontier labs such as Anthropic, OpenAI, and Google DeepMind, and its Arena community has about 15,000 members.

    Matched fieldsSummary、Body

    #hot-signal#Safety / alignment#Agents#Evaluations / benchmarks
  2. TechCrunch AISelected

    Eka Robotics Completes $150 Million Series A Funding at a $1.45 Billion Valuation

    Eka Robotics has completed a $150 million Series A funding round led by a16z and F-Prime, at a post-money valuation of $1.45 billion. The company was founded by MIT professor Pulkit Agrawal and former Google DeepMind researcher Tuomas Haarnoja. Its vision-force-action model, paired with force-sensing grippers, can grasp about 1,500 objects weighing under 1 kg per hour, with a success rate exceeding 95%; the gripper costs less than $1,000 and is rated for 5 million cycles. The team uses a physics simulator to build reinforcement learning environments, and says it can generate thousands of hours of robot experience within minutes. It is currently running pilots with industrial customers for pick-and-place and cable manipulation.

    $150 million Series A funding round led by a16z and F-Prime, at a post-money valuation of $1.45 billion. The company was founded by MIT professor Pulkit Agrawal and former Google DeepMind researcher Tuomas Haarnoja. Its vision-force-action model, paired with force-sensing grippers, can grasp about 1,500 objects weighing under 1 kg per hour, with a success rate exceeding 95%; the gripper costs less than $1,000 and is rated for 5 million cycles. The team uses a physics simulator to build reinforc

    Matched fieldsSummary、Body

    #tech-media#Industry news#Embodied AI#English text is being prepared.

Monday

Sunday

  1. The Decoder

    Four Gemini 4 Argon agents complete Minecraft without human assistance

    The Google DeepMind team had four AI agents running on Gemini 4 Argon autonomously complete Minecraft in a randomly generated world, defeating the Ender Dragon with no human input throughout. The team did not assign them roles, strategies, or game-specific tools; the four agents divided the work among themselves, warned each other of threats, recovered items from fallen teammates, and recorded mistakes in a shared memory. The entire run took 27 hours and 14 minutes, during which they wrote 2,783 JavaScript programs, exchanged 229 chat messages, and died 605 times, costing $1,620 at the standard Gemini 4 Argon prices effective in 2027.

    The Google DeepMind team had four AI agents running on Gemini 4 Argon autonomously complete Minecraft in a randomly generated world, defeating the Ender Dragon with no human input throughout. The team did not assign them roles, strategies, or game-specific tools; the four agents divided the work among themselves, warned each other of threats, recovered items from fallen teammates, and recorded mistakes in a shared memory. The entire run took 27 hours and 14 minutes, during which they wrote 2,783

    Matched fieldsSummary、Body

    #ai-vertical#English text is being prepared.#Agents#Google

Saturday

  1. Arena

    Arena tracks four signals for agents from six major AI labs

    Arena tracked four Agent Arena signals for the best agents from Anthropic, OpenAI, Google DeepMind, Kimi, xAI, and DeepSeek from late June to early October, finding that the largest gap between labs lies in whether users enjoy the experience of using them. On Praise vs Complaint, OpenAI and Anthropic scored +35 points above the leaderboard average, while Moonshot, xAI, and DeepSeek ranged from +5 to +13; on Confirmed Success, the average lab's best model rose from -4 to +14 points within three months, with Anthropic leading at +20 with Claude Opus 5.5 (Max). The Bash Recovery gap narrowed from 34 to 16 points, and Steerability from 19 to 11 points.

    Arena tracked four Agent Arena signals for the best agents from Anthropic, OpenAI, Google DeepMind, Kimi, xAI, and DeepSeek from late June to early October, finding that the largest gap between labs lies in whether users enjoy the experience of using them. On Praise vs Complaint, OpenAI and Anthropic scored +35 points above the leaderboard average, while Moonshot, xAI, and DeepSeek ranged from +5 to +13; on Confirmed Success, the average lab's best model rose from -4 to +14 points within three m

    Matched fieldsSummary、Body

    #editorial#English text is being prepared.#Agents#Evaluations / benchmarks

Friday

  1. The Decoder

    Google Gemini 4 Argon scores 97.5% on ARC-AGI-3, but drops to 49.8% when the test environment is changed

    Google DeepMind's new frontier model Gemini 4 Argon scored 97.5% on ARC-AGI-3 in the Provider Adapter Harness test environment at a cost of $4,800, becoming the second lab after OpenAI to cross 95%, second only to GPT-6 Astra's 99.9%. Under the Standard Harness, which allows only simple notes, the low-reasoning setting's score dropped to 49.8%, and due to API issues it was unable to complete all 55 games. On Epoch AI's ECI index, Argon scored 165, on par with Fable 5.1, but ranked eighth on the math ECI and sixth on the software engineering index.

    Google DeepMind's new frontier model Gemini 4 Argon scored 97.5% on ARC-AGI-3 in the Provider Adapter Harness test environment at a cost of $4,800, becoming the second lab after OpenAI to cross 95%, second only to GPT-6 Astra's 99.9%. Under the Standard Harness, which allows only simple notes, the low-reasoning setting's score dropped to 49.8%, and due to API issues it was unable to complete all 55 games. On Epoch AI's ECI index, Argon scored 165, on par with Fable 5.1, but ranked eighth on the

    Matched fieldsSummary、Body

    #ai-vertical#Evaluations / benchmarks#Google#DeepMind
  3. ARC PrizeSelected

    ARC Prize announces evaluation results of Gemini 4 Argon on the ARC-AGI series

    ARC Prize announces the validated results of Google Gemini 4 Argon on ARC-AGI: ARC-AGI-3 reaches 97.5% (provider adapter harness, cost $4.8K), and 49.8% under the standard harness (cost $5.8K); ARC-AGI-2 is 91.3%, at $0.32 per task; ARC-AGI-1 is 98.5%, at $0.16 per task. ARC Prize states that Google DeepMind is the second frontier AI lab to exceed 95% on ARC-AGI-3, and Argon's 97.5% is second only to GPT-6 Astra's 99.9%. The standard harness test failed to complete all 55 games due to API issues, and the high reasoning tier completed only 19 games so no score was published; costs are calculated at the current discounted price of $2 per million input tokens and $10 per million output tokens.

    harness, cost $4.8K), and 49.8% under the standard harness (cost $5.8K); ARC-AGI-2 is 91.3%, at $0.32 per task; ARC-AGI-1 is 98.5%, at $0.16 per task. ARC Prize states that Google DeepMind is the second frontier AI lab to exceed 95% on ARC-AGI-3, and Argon's 97.5% is second only to GPT-6 Astra's 99.9%. The standard harness test failed to complete all 55 games due to API issues, and the high reasoning tier completed only 19 games so no score was published; costs are calculated at the current disc

    Matched fieldsSummary

    #company#Evaluations / benchmarks#Reasoning#Google
  5. François CholletSelected

    Chollet: Gemini 4 Argon performs comparably to Astra on ARC-AGI-3, Google returns to the frontier

    François Chollet stated that Gemini 4 Argon's performance on ARC-AGI-3 is close to Astra's, and Google has returned to the frontier. Data cited by ARC Prize shows that Google DeepMind's Gemini 4 Argon achieved 97.5% (provider adapter harness, $4.8K) and 49.8% (standard harness, $5.8K) on ARC-AGI-3, 91.3% at $0.32/task on ARC-AGI-2, and 98.5% at $0.16/task on ARC-AGI-1, making it the second frontier AI lab to exceed 95% on ARC-AGI-3.

    Matched fieldsSummary

    #hot-signal#Evaluations / benchmarks#Google#DeepMind