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Friday

  1. Story5 reports · 4 facts

    Chollet says Argon performs similarly to Astra on ARC-AGI-3

    François Chollet commented that Gemini 4 Argon performs similarly to Astra on ARC-AGI-3, with Google returning to the frontier. On the same day, ARC Prize published the verified results for Gemini 4 Argon on the ARC-AGI series: ARC-AGI-3 scored 97.5% under the provider adapter harness at a cost of $4.8K; under the standard harness it scored 49.8% at a cost of $5.8K. ARC-AGI-2 scored 91.3% at $0.32 per task; ARC-AGI-1 scored 98.5% at $0.16 per task. ARC Prize said 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 could not complete all 55 games due to API issues; the high-reasoning setting completed only 19 games, so no score was published.

    François Chollet commented that Gemini 4 Argon performs similarly to Astra on ARC-AGI-3, with Google returning to the frontier. On the same day, ARC Prize published the verified results for Gemini 4 Argon on the ARC-AGI series: ARC-AGI-3 scored 97.5% under the provider adapter harness at a cost of $4.8K; under the standard harness it scored 49.8% at a cost of $5.8K. ARC-AGI-2 scored 91.3% at $0.32 per task; ARC-AGI-1 scored 98.5% at $0.16 per task. ARC Prize said Google DeepMind is the second fr

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Wednesday

  1. Story44 reports · 5 facts

    Meta launches Muse Wonder and Muse Wonder Full models

    On October 13, 2026, Meta released the Muse Wonder series, comprising two models, Muse Wonder and Muse Wonder Full, which it called its most capable models to date, achieving SOTA on personal and professional agent tasks, covering professional domains such as law, finance, and health as well as frontier coding capabilities. Both models feature Active Memory and Active Focus, and were trained, evaluated, and released according to the Meta Superintelligence Scaling Framework. According to Artificial Analysis data, the two models scored 52 and 56 respectively on Intelligence Index v4.3 at maximum reasoning effort, higher than the 48 of Muse Spark 1.3 (max); Muse Wonder is already available on Meta's first-party API and Muse Code, while Muse Wonder Full is available only to Muse Code users. On the same day, Arena added the two models to Agent Arena testing, and Simon Willison released the llm-meta-ai 0.2 plugin supporting the new models. On October 14, Arena announced Muse W

    On October 13, 2026, Meta released the Muse Wonder series, comprising two models, Muse Wonder and Muse Wonder Full, which it called its most capable models to date, achieving SOTA on personal and professional agent tasks, covering professional domains such as law, finance, and health as well as frontier coding capabilities. Both models feature Active Memory and Active Focus, and were trained, evaluated, and released according to the Meta Superintelligence Scaling Framework. According to Artifici

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Sunday

  1. Story1 reports · 1 facts

    Aleph Alpha releases the Kolibri-1 open-weight model

    On October 16, 2026, Aleph Alpha released the open-weight model Kolibri-1. According to heise online, the model has 78B total parameters and uses a MoE architecture with 384 experts and 6 routed experts, activating only 3.46B parameters during inference, with a context of 256K tokens that can be extrapolated to about 1 million tokens. The model focuses on German and English, and its tokenizer segments German words more token-efficiently; for example, the German Bundestag glossary requires only 1,137 tokens, whereas the compared DeepSeek-V4.1-Flash requires 1,910 tokens. Pretraining used 20 trillion tokens, with data up to June 18, 2026. The report's author tested it and said its answers were correct and its German reasoning process evident, and said the Merlin-Arthur protocol makes the model hallucinate less in benchmarks, but its actual performance still needs to be tested in real-world use. There are currently no subsequent developments.

    On October 16, 2026, Aleph Alpha released the open-weight model Kolibri-1. According to heise online, the model has 78B total parameters and uses a MoE architecture with 384 experts and 6 routed experts, activating only 3.46B parameters during inference, with a context of 256K tokens that can be extrapolated to about 1 million tokens. The model focuses on German and English, and its tokenizer segments German words more token-efficiently; for example, the German Bundestag glossary requires only 1

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  2. Story1 reports · 1 facts

    Reflection announces a $10,000 Hub credit for the Beam project

    Reflection announces a $10,000 Hub credit for the Beam project. The news was released on October 16, 2026. At the same time, Hugging Face released the Beam model: a 501B-parameter sparse MoE open-source model aimed at long-context reasoning and agent tasks with a 1M context, activating only about 23B parameters per token. The model uses 52 layers of hybrid attention, of which 43 layers use a 512-token sliding window and 9 layers use global attention; the KV cache grows by 36 KiB per token, about one-sixth that of a full-attention scheme. Official checkpoints are provided in BF16, FP8, and NVFP4; NVFP4 can compress the weights from about 1 TB to about 280 GB, and day-0 support is already available in transformers, vLLM, and SGLang. There are currently no subsequent developments.

    Reflection announces a $10,000 Hub credit for the Beam project. The news was released on October 16, 2026. At the same time, Hugging Face released the Beam model: a 501B-parameter sparse MoE open-source model aimed at long-context reasoning and agent tasks with a 1M context, activating only about 23B parameters per token. The model uses 52 layers of hybrid attention, of which 43 layers use a 512-token sliding window and 9 layers use global attention; the KV cache grows by 36 KiB per token, about

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Thursday