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Sunday

  1. Story2 reports · 1 facts

    OpenRouter launches RespanAI decision model Span-02

    OpenRouter announced the launch of RespanAI's open-weight decision model Span-02. The model accepts text and image inputs and returns options, yes/no/unknown, or scores with probabilities in response to custom questions; pricing is $0.01 per million input tokens with free output, and the context length is 131K. OpenRouter later clarified that Span-02 runs on the OpenRouter Decisions API, uses the same request format as other decision models, and switching requires only changing one line with the model name; images and text are placed in the same state, with a maximum of 4 images per request. Respan previously introduced Span-02 as supporting anytime inference, returning the current best decision once a deadline is given, allowing configurable reasoning duration, and supporting multimodal viewing of images and screens. The model is now live on OpenRouter.

    OpenRouter announced the launch of RespanAI's open-weight decision model Span-02. The model accepts text and image inputs and returns options, yes/no/unknown, or scores with probabilities in response to custom questions; pricing is $0.01 per million input tokens with free output, and the context length is 131K. OpenRouter later clarified that Span-02 runs on the OpenRouter Decisions API, uses the same request format as other decision models, and switching requires only changing one line with the

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  2. Story1 reports · 1 facts

    Databricks has been collecting npm malware samples since November 2025

    Databricks has been collecting npm malware samples since November 2025. Its blog discloses that, among the 11,413 package-versions scanned and verified, 96.2% hid their payloads in source code or on external servers, while only 3.8% (432) bundled binaries; however, the proportion of bundled-binary packages from which no clear malicious conclusion could be drawn was more than twice that of pure source-code packages (14.4% versus 6.2%). The team evaluated 9 models, including Claude Haiku 4.5, Gemini 3.5 Flash, GPT-5.4-nano, Qwen3-Next 80B, and others, and injected precomputed static evidence as capability profiles during the inference stage to improve detection quality for both budget-tier and frontier models. Currently, public information is still based on what this blog discloses, and there have been no subsequent developments.

    Databricks has been collecting npm malware samples since November 2025. Its blog discloses that, among the 11,413 package-versions scanned and verified, 96.2% hid their payloads in source code or on external servers, while only 3.8% (432) bundled binaries; however, the proportion of bundled-binary packages from which no clear malicious conclusion could be drawn was more than twice that of pure source-code packages (14.4% versus 6.2%). The team evaluated 9 models, including Claude Haiku 4.5, Gemi

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  3. 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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  4. 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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Saturday

  1. Story3 reports · 1 facts

    Elorian releases its first AI model, Glimpse

    On October 17, 2026, Elorian released its first AI model, Glimpse. According to Testing Catalog, Glimpse can, during its thinking process, return to a specific point in a video, zoom in on details, or replay half a second of video at a higher frame rate before answering. Elorian's own testing shows that Glimpse outperforms GPT-6 Astra in reading scientific charts and describing short videos; in a simulated task of planning for a robotic arm to place cream cheese into a bowl, Glimpse cost $0.035, compared with $0.249 for GPT-6 Astra; in a demonstration, Glimpse solved 5 mazes of increasing difficulty, while Gemini 3.8 Flash solved 1/5. Another report the same day said that Elorian positions Glimpse as the first visual reasoning model, used to compare scene changes at different points in a video, and calls this frame-based reasoning visual thinking; during its reasoning process it can return to the source video, crop regions, extract specified frames, or run code to measure and compare what it sees, a mechanism called visual scratch space; co-founder Andrew M. Dai said it has frontier performance competitive with Astra on key public visual reasoning benchmarks. At present, there have been no further evidence-supported developments since the model's release.

    On October 17, 2026, Elorian released its first AI model, Glimpse. According to Testing Catalog, Glimpse can, during its thinking process, return to a specific point in a video, zoom in on details, or replay half a second of video at a higher frame rate before answering. Elorian's own testing shows that Glimpse outperforms GPT-6 Astra in reading scientific charts and describing short videos; in a simulated task of planning for a robotic arm to place cream cheese into a bowl, Glimpse cost $0.035,

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Friday

  1. Story4 reports · 2 facts

    Artificial Analysis publishes evaluation results for Reflection's first model, Beam

    On October 16, 2026, Artificial Analysis published the evaluation results for Beam, the first model from Reflection: Beam scored 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). The model is an open-weight model with 501B total parameters and 23B activated parameters, consuming about 26k output tokens per intelligence index task, of which about 19k are used for reasoning and 7k for answering; it scored 48.5% on AutomationBench-AA and only 3.0% on Terminal-Bench 4.0; AA-Omniscience accuracy was 33% with a hallucination rate of 47%. On October 17, Arena.ai announced that Beam-1.0-501B-A23B entered Agent Arena, ranking 12th among open-weight models with a Net Improvement of -3.67% and a margin of error of ±3.84%; on the Code Arena: WebDev open-source leaderboard it ranked 22nd with a score of 1438.

    On October 16, 2026, Artificial Analysis published the evaluation results for Beam, the first model from Reflection: Beam scored 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). The model is an open-weight model with 501B total parameters and 23B activated parameters, consuming about 26k output tokens per intelligence index task, of which about 19k are used for r

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  4. 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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