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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.

    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.

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  2. Story16 reports · 3 facts

    Nvidia releases Nemotron 3.5 Super VL and Bolt models

    On October 15, 2026, Nvidia announced the launch of Nemotron 3.5 Super VL and the edge-oriented 3.5 Bolt models. According to Nvidia's official introduction, Super VL is a 120B-parameter open model aimed at always-on agent tasks, and the company says it has the highest throughput among comparable open models, 5 times faster than the next-best model; it also launched two edge models, Bolt 74B-A9B 24GB and Bolt E4B 4GB, with the former able to run agent tasks locally on RTX/DGX Spark and the latter aimed at voice and conversational agents on GeForce RTX and Jetson. Super VL and Bolt 74B-A9B are already available on Hugging Face, and Bolt E4B is scheduled for release on October 23. On the same day, SGLang announced Day-0 support for Super VL, reaching 243.9 tok/s on a single RTX 6000 PRO with the help of MTP; Prime Intellect and Nvidia used GRPO to post-train it for about 11 hours on 128 GB200 GPUs, raising its score on the public AutomationBench-AA set from 34.0% to 57.7%. That evening OpenRouter listed the model, and the next day Perplexity announced that Pro and Max subscribers and the Agent API can use it.

    the launch of Nemotron 3.5 Super VL and the edge-oriented 3.5 Bolt models. According to Nvidia's official introduction, Super VL is a 120B-parameter open model aimed at always-on agent tasks, and the company says it has the highest throughput among comparable open models, 5 times faster than the next-best model; it also launched two edge models, Bolt 74B-A9B 24GB and Bolt E4B 4GB, with the former able to run agent tasks locally on RTX/DGX Spark and the latter aimed at voice and conversational a

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  3. Story2 reports · 1 facts

    StepFun updates Step Code to v0.1.4

    On October 15, 2026, StepFun updated Step Code, the open-source agent harness built around Step 5 Preview, to v0.1.4, helping agents track goals in long and complex tasks, keep making progress, and check against verifiable results whether tasks are truly completed. The company says that since Step Code was open-sourced, four versions have been released, roughly once every 3 working days, driven mainly by community feedback. On the same day, StepFun officially opened the Step 5 Preview model weights, with BF16 and FP8 versions launched simultaneously on Hugging Face and ModelScope; the model uses a sparse MoE architecture, with 600B total parameters and 27B activated parameters, and supports a 1 million Token context window.

    On October 15, 2026, StepFun updated Step Code, the open-source agent harness built around Step 5 Preview, to v0.1.4, helping agents track goals in long and complex tasks, keep making progress, and check against verifiable results whether tasks are truly completed. The company says that since Step Code was open-sourced, four versions have been released, roughly once every 3 working days, driven mainly by community feedback. On the same day, StepFun officially opened the Step 5 Preview model weig

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Thursday

  1. FactRelated story

    Nvidia releases Nemotron 3.5 Super VL and Bolt models

    On October 15, 2026, Nvidia announced the launch of Nemotron 3.5 Super VL and the edge-oriented 3.5 Bolt models. According to Nvidia's official introduction, Super VL is a 120B-parameter open model aimed at always-on agent tasks, and the company says it has the highest throughput among comparable open models, 5 times faster than the next-best model; it also launched two edge models, Bolt 74B-A9B 24GB and Bolt E4B 4GB, with the former able to run agent tasks locally on RTX/DGX Spark and the latter aimed at voice and conversational agents on GeForce RTX and Jetson. Super VL and Bolt 74B-A9B are already available on Hugging Face, and Bolt E4B is scheduled for release on October 23. On the same day, SGLang announced Day-0 support for Super VL, reaching 243.9 tok/s on a single RTX 6000 PRO with the help of MTP; Prime Intellect and Nvidia used GRPO to post-train it for about 11 hours on 128 GB200 GPUs, raising its score on the public AutomationBench-AA set from 34.0% to 57.7%. That evening OpenRouter listed the model, and the next day Perplexity announced that Pro and Max subscribers and the Agent API can use it.

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    View story:Nvidia releases Nemotron 3.5 Super VL and Bolt models

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

    eased 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 mod

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  2. Story9 reports · 2 facts

    Gemini 4 Argon appears on the GCP quota page

    On October 13, 2026, Gemini 4 Argon appeared on the GCP quota page. Testing Catalog, citing AiBattle, said the model was expected to launch within a few hours and called it a precursor to an imminent API model release; earlier that day, a Google employee had also teased an upcoming release. That evening, Testing Catalog reported that the model had begun rolling out and was already live in GCP's Agent Studio, where users could test it, with Google AI Studio and the Gemini app expected to follow. On October 14, Google officially released Gemini 4 Argon, open to developers and enterprises worldwide, usable via AI Studio and the Gemini platform API, and supported in Google Antigravity as well as the Gemini app and Search AI Mode for Ultra and Pro subscribers; reports said it supports long decoding output of up to 1,048,576 tokens and announced limited-time introductory pricing.

    earlier that day, a Google employee had also teased an upcoming release. That evening, Testing Catalog reported that the model had begun rolling out and was already live in GCP's Agent Studio, where users could test it, with Google AI Studio and the Gemini app expected to follow. On October 14, Google officially released Gemini 4 Argon, open to developers and enterprises worldwide, usable via AI Studio and the Gemini platform API, and supported in Google Antigravity as well as the Gemini app an

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Sunday

  6. Story2 reports · 1 facts

    Google releases Gemini 3.8 Live audio model

    Google released two real-time conversational audio models, Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, available starting today through the Gemini API, Google Workspace, and the Gemini app. According to Google DeepMind, Gemini 3.8 Live Extended Thinking ranks first on the Artificial Analysis Speech-to-Speech Quality Index with a score of 82.6, achieves a 68.6% task completion rate on τ-Voice agent tasks, and scores 97.7% on Big Bench Audio; Gemini 3.8 Live ranks second on the Speech Agent Arena. Both models support near-real-time visual input, automatic switching between 97 languages during a conversation, and can execute tool and API calls in the background without interrupting the conversation. On September 18, Google demonstrated a workshop assistant built with Gemini 3.8 Live Extended Thinking that can analyze the workspace, reason aloud, and guide the building process. On September 21, Google also demonstrated a programming tutoring Agent built with this model that can view the user's screen, reference the p5.js library through function calling, and explain code logic.

    ind, Gemini 3.8 Live Extended Thinking ranks first on the Artificial Analysis Speech-to-Speech Quality Index with a score of 82.6, achieves a 68.6% task completion rate on τ-Voice agent tasks, and scores 97.7% on Big Bench Audio; Gemini 3.8 Live ranks second on the Speech Agent Arena. Both models support near-real-time visual input, automatic switching between 97 languages during a conversation, and can execute tool and API calls in the background without interrupting the conversation. On Septem

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Tuesday

  1. Story1 reports · 0 facts

    Fireworks AI launches the Specialized Intelligence Index

    On September 22, 2026, Fireworks AI released the Specialized Intelligence Index (SII), which brings together the performance of open, closed-source, and specialized models on domain-specific benchmarks, initially covering seven domains: healthcare, legal, cybersecurity, finance, customer service, productivity, and software. According to the official description, each benchmark was designed by practitioners in the field, such as Harvey's Legal Agent Benchmark, Sierra's τ-Banking and τ-Voice, and Datacurve's DeepSWE v1.1. SII does not weight or average results across domains; it retains only benchmark- and domain-level scores and provides comparison views of score versus cost and score versus duration. Each benchmark is annotated with its reproducible scope, and new frontier models must pass a production deployability screening before being included. Fireworks also accepts partner submissions, with partners providing tasks and data in a Harbor-compatible format; Fireworks runs the evaluations free of charge and publishes scores with the partner's consent. There have been no further developments so far.

    ty, finance, customer service, productivity, and software. According to the official description, each benchmark was designed by practitioners in the field, such as Harvey's Legal Agent Benchmark, Sierra's τ-Banking and τ-Voice, and Datacurve's DeepSWE v1.1. SII does not weight or average results across domains; it retains only benchmark- and domain-level scores and provides comparison views of score versus cost and score versus duration. Each benchmark is annotated with its reproducible scope,

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Sunday

  1. Story1 reports · 1 facts

    Bailing releases and open-sources native multimodal model Ling-3.0-flash-VL

    On September 9, 2026, Ant Bailing released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, the model has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K-token context window. According to the company, native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, and it introduces a visual feedback loop of observe, act, verify, and correct, used for front-end replication, GUI agents, and medical report interpretation. The model is currently available for free trial on Ling Studio, and the BF16 and FP8 versions have been open-sourced on Hugging Face and ModelScope.

    ontext window. According to the company, native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, and it introduces a visual feedback loop of observe, act, verify, and correct, used for front-end replication, GUI agents, and medical report interpretation. The model is currently available for free trial on Ling Studio, and the BF16 and FP8 versions have been open-sourced on Hugging Face and ModelScope.

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  2. Story1 reports · 1 facts

    Ant Ling releases finance-enhanced model Ling-3.0-flash-Fin

    On August 28, 2026, Ant Ling released its first finance-enhanced model, Ling-3.0-flash-Fin. The model continues the Ling-3.0-flash architecture, maintaining 124B total parameters and 5.1B activated parameters, and strengthens its handling of complex financial content such as annual reports and financial workbooks through continued pre-training on financial corpora, domain post-training, and tool-use optimization. The model was tested on benchmarks including FinFIRST, FinSearchComp Verified, Finance Agent, APEX-Agents, SpreadsheetBench, and τ3-Banking, with its AA Intelligence Index v4.1.1 score rising from 38 to 41. Ant Ling said the model will be available for a one-month limited-time free API access on OpenRouter, and its weights will be open-sourced next week. No further progress has been disclosed at this time.

    ontinued pre-training on financial corpora, domain post-training, and tool-use optimization. The model was tested on benchmarks including FinFIRST, FinSearchComp Verified, Finance Agent, APEX-Agents, SpreadsheetBench, and τ3-Banking, with its AA Intelligence Index v4.1.1 score rising from 38 to 41. Ant Ling said the model will be available for a one-month limited-time free API access on OpenRouter, and its weights will be open-sourced next week. No further progress has been disclosed at this tim

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