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  1. Story1 reports · 1 facts

    Perplexity Decider launches on Vercel AI Gateway

    On October 17, 2026, Perplexity's Decider V1.2 decision model launched on Vercel AI Gateway. Vercel developers can now use perplexity/pplx-decider-v1.2 for complex agentic workflows and perplexity/pplx-decider-flash-v1.2 for ultra-fast decisions. Aravind Srinivas reposted the news. Earlier the same day, reports said Perplexity had open-sourced two decision models, pplx-decider-flash-v1.2 and pplx-decider-v1.2: the former has 4B parameters and costs 1 cent per million tokens, and is called the strongest 4B model on the HF Decision Index; the latter has 27B parameters and costs 2 cents per million tokens, and ranks 2nd on the overall HF Decision Index leaderboard. Both are now available on the Perplexity Decisions API and offer open weights on Hugging Face. Perplexity Developers said this generation of open-weight multimodal decision models improves both text and vision scores, delivers inference up to 5 times faster, and completes all questions in a single request in one forward pass.

    On October 17, 2026, Perplexity's Decider V1.2 decision model launched on Vercel AI Gateway. Vercel developers can now use perplexity/pplx-decider-v1.2 for complex agentic workflows and perplexity/pplx-decider-flash-v1.2 for ultra-fast decisions. Aravind Srinivas reposted the news. Earlier the same day, reports said Perplexity had open-sourced two decision models, pplx-decider-flash-v1.2 and pplx-decider-v1.2: the former has 4B parameters and costs 1 cent per million tokens, and is called the st

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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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Saturday

  1. Story2 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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Wednesday

  1. Story27 reports · 1 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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  2. Story8 reports · 1 facts

    Google opens Gemini 4 Argon to developers

    On October 13, 2026, Google announced that developers can now use Gemini 4 Argon to build applications. This frontier model targets complex workflows such as real software engineering, enterprise knowledge work, and cybersecurity defense, and is designed for long-horizon tasks requiring deep reasoning and structural precision. Google also provided the Gemini API developer guide and entry points for using it in Google Antigravity and Android Studio. On the same day, Google disclosed an unattended experiment: letting Argon optimize the open-source framework google/adk-python, which has about 205,000 lines of code, within 11 hours. The model proposed 6 optimizations; after human review, 2 were merged into the main branch, memory session preparation time for long sessions was reduced by about 40%, and cold-start import speed improved by 21%. On October 14, Sundar Pichai called this a good summary of the safety and alignment work on Argon 4; Ethan Mollick commented that Gemini 4 is a major leap compared with previous Gemini models but still lags on complex multi-agent tasks. On October 15, Google began rolling out Gemini 4 Argon support to the AI programming tool Antigravity.

    On October 13, 2026, Google announced that developers can now use Gemini 4 Argon to build applications. This frontier model targets complex workflows such as real software engineering, enterprise knowledge work, and cybersecurity defense, and is designed for long-horizon tasks requiring deep reasoning and structural precision. Google also provided the Gemini API developer guide and entry points for using it in Google Antigravity and Android Studio. On the same day, Google disclosed an unattended

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