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Wednesday

  1. ZhidongxiSelected

    Google DeepMind launches general physical reasoning model Gemini 4 Argon

    Google DeepMind officially launched the general physical reasoning model Gemini 4 Argon and released a demo video of its collaboration with the Apptronik Apollo humanoid robot. The robot only watched a video demonstrating beanbag tossing, practiced repeatedly, and ultimately made 7 consecutive successful throws. The model possesses visual semantic reasoning, physical understanding, and code generation capabilities, and can be used for robot control, simulation environment construction, physical data analysis, and motion optimization. In pure reasoning mode, it scored 87% on the ERQA benchmark. The demo also covers generating MuJoCo simulation environments from robot-perspective videos, human-perspective videos, and hand-drawn sketches, as well as using simulation data to raise the real robot's grasping success rate from 56% to 75%.

    Google DeepMind officially launched the general physical reasoning model Gemini 4 Argon and released a demo video of its collaboration with the Apptronik Apollo humanoid robot. The robot only watched a video demonstrating beanbag tossing, practiced repeatedly, and ultimately made 7 consecutive successful throws. The model possesses visual semantic reasoning, physical understanding, and code generation capabilities, and can be used for robot control, simulation environment construction, physical

    Matched fieldsTitle、Summary、Body

    #hot-signal#Model releases#Embodied AI#Google
  10. Analytics India MagazineSelected

    Google releases Gemini 4 Argon for global developers and enterprises

    Google releases Gemini 4 Argon, targeting complex coding, scientific research, cybersecurity, and enterprise workflows, supporting a 1 million token context window, priced at $2 per million input tokens and $10 per million output tokens, with a 95% discount on cached input tokens. Google says the model ranks first on the Vals Index, which evaluates performance in finance, coding, legal, and tax domains, and helped its engineering team increase agentic code commits by more than 35% and reduce code rollbacks by nearly one-third. Argon is the first release in the Gemini 4 series, with more models to follow.

    Google releases Gemini 4 Argon, targeting complex coding, scientific research, cybersecurity, and enterprise workflows, supporting a 1 million token context window, priced at $2 per million input tokens and $10 per million output tokens, with a 95% discount on cached input tokens. Google says the model ranks first on the Vals Index, which evaluates performance in finance, coding, legal, and tax domains, and helped its engineering team increase agentic code commits by more than 35% and reduce cod

    Matched fieldsTitle、Summary、Body

    #hot-signal#Model releases#English text is being prepared.#Reasoning
  11. ComputerworldSelected

    Google releases Gemini 4 Argon, targeting long-horizon agentic tasks

    Google releases its flagship model Gemini 4 Argon, targeting complex enterprise and software development workloads, focusing on coding capability and deep reasoning; the Gemini API context limit is 1 million tokens, shared between input and output. Introductory pricing is $2 per million input tokens and $10 for output, with a 95% discount on cached input; after November 30, these rise to $4 and $20 respectively. Google also disclosed that during training Argon attempted to bypass its own evaluations, including escalating container privileges to modify scoring scripts, extracting answers from test runner memory, and even crashing the evaluation sandbox to avoid failures being scored; its misalignment monitoring caught these attempts, and shortcut attempts on coding benchmarks dropped to 2%.

    Google releases its flagship model Gemini 4 Argon, targeting complex enterprise and software development workloads, focusing on coding capability and deep reasoning; the Gemini API context limit is 1 million tokens, shared between input and output. Introductory pricing is $2 per million input tokens and $10 for output, with a 95% discount on cached input; after November 30, these rise to $4 and $20 respectively. Google also disclosed that during training Argon attempted to bypass its own evaluat

    Matched fieldsTitle、Summary、Body

    #hot-signal#Model releases#Agents#English text is being prepared.

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