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Friday

  1. 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.

    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

    Matched fieldsSummary、Body

    #company#Evaluations / benchmarks#Reasoning#Google
  7. Unite.AISelected

    Meta open-sources 816B-parameter multimodal model Muse Spark Open

    On October 15, Meta released the weights of Muse Spark Open, a natively multimodal open-source model with 816B total parameters, 42B activated parameters, and a 1M token context window, targeting coding and long-horizon agent tasks. The model uses a MoE Transformer plus Perception Encoder architecture, can be invoked through API platforms such as Baseten, Fireworks AI, and Groq, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and elsewhere; running the NVFP4 version locally requires at least 700GB of VRAM. Meta claims it is the first model of its class that can run on desktop, carried by two Mac Studios equipped with M5 Ultra paired with the MLX framework; the model card shows its Artificial Analysis Intelligence Index at 39, lower than GLM 5.3's 45 and Kimi K3's 44 in the table.

    us Perception Encoder architecture, can be invoked through API platforms such as Baseten, Fireworks AI, and Groq, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and elsewhere; running the NVFP4 version locally requires at least 700GB of VRAM. Meta claims it is the first model of its class that can run on desktop, carried by two Mac Studios equipped with M5 Ultra paired with the MLX framework; the model card shows its Artificial Analysis Intelligence

    Matched fieldsSummary、Body

    #hot-signal#Model releases#Multimodal#Open-source ecosystem
  8. The New StackSelected

    Meta releases 816B open-source model Muse Spark Open

    Meta releases Muse Spark Open, an open-weight model with 816B parameters and 42B activated parameters, using a 1 million token context window, callable via APIs such as Baseten, Fireworks AI, Groq, and OpenRouter, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and others. Meta says it targets coding and agent tasks and leads Kimi K3, GLM 5.3, and Qwen3.8 on agent and tool-calling benchmarks such as JobBench and MCP-Atlas, but trails on coding benchmarks such as Terminal-Bench 2.1 and DeepSWE 1.1. The model uses the Muse Model License; companies with annual revenue over $20 million must first notify Meta and sign a separate agreement to offer model services, and users must prominently label Built with Muse Spark Open in product interfaces.

    s, using a 1 million token context window, callable via APIs such as Baseten, Fireworks AI, Groq, and OpenRouter, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and others. Meta says it targets coding and agent tasks and leads Kimi K3, GLM 5.3, and Qwen3.8 on agent and tool-calling benchmarks such as JobBench and MCP-Atlas, but trails on coding benchmarks such as Terminal-Bench 2.1 and DeepSWE 1.1. The model uses the Muse Model License; companies wit

    Matched fieldsSummary、Body

    #hot-signal#Model releases#Open-source ecosystem#Reasoning

Thursday