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All news · Today7 items

All AI news

All collected and published items, ordered by time. Picks are a selected subset.
Models

New models and versions, open weights, and changes in capability and pricing, with their evaluations.

10/15Thu
  1. Mistral AI77

    Mistral AI releases Timestral, a family of time-series foundation models

    Mistral AI released Timestral in four sizes, from 5M to 209M parameters, for probabilistic, multivariate forecasting with past-only and known-future covariates. Mistral reports that its 209M model ranks first among pretrained, zero-shot models across five metrics on GIFT-Eval, TIME and fev-bench, using a single checkpoint without ensembling. All sizes support CPU inference, and Mistral published a GitHub repository for running Timestral on users’ data; domain adaptation and fine-tuning are available through Mistral Forge.

    Why it matters: The comparisons across model sizes and CPU latency provide a concrete basis for assessing forecasting accuracy against compute requirements in production deployments.

  2. NVIDIA Blog81

    NVIDIA Expands Nemotron Family With Super VL and Bolt Open Models

    NVIDIA released Nemotron 3.5 Super VL, an open 120-billion-parameter model for multimodal agents, and Nemotron 3.5 Bolt 74B-A9B 24GB for local agentic workloads; Bolt E4B 4GB is coming Oct. 23. Super VL targets tool use, business workflows and visual document understanding, with open weights, datasets and training recipes. Bolt 74B-A9B uses native 2-bit quantization to fit in 24GB of memory, while Bolt E4B targets physical and conversational agents.

    Why it matters: Open weights, datasets and training recipes give organizations a path to adapting agents to their own workflows while meeting local deployment and data residency requirements.

  3. NVIDIA AI · X76

    StepFun releases open weights for Step 5 Preview, a 600B MoE model

    NVIDIA AI congratulates StepFun on Step 5 Preview, calling it StepFun’s largest open weights release. NVIDIA says the 600B MoE model supports agent tasks lasting up to 24 hours and runs at nearly 8K tokens/sec/GPU on NVIDIA GB300 using NVFP4. StepFun’s quoted announcement lists 27B active parameters per token, a 1M-token context window, native text and vision input, and BF16 and FP8 weights on Hugging Face and ModelScope. NVP4 checkpoint: https://t.co/lSECChAwlW Deploy with https://t.co/GO1wqTSBOk https://t.co/28TBzUo6ZU

    Why it matters: The reported throughput ties Step 5 Preview to a specific GPU and precision format, giving readers concrete deployment context for its long running agent workloads.

  4. Hugging Face Blog74

    Apertus 1.5 Release Notes Detail Multimodal Understanding, Thinking Mode, and Longer Context

    Project Apertus released Apertus 1.5 in 8B and 70B sizes, adding image and audio understanding, optional thinking, a 262,144-token context window, and improved instruction-following and tool use. The weights were released on Hugging Face on July 24, 2026 under Apache 2.0; the models generate only text, and spoken-language processing remains experimental. The team also publishes training data, recipes, intermediate checkpoints, and reproducible evaluation records.

    Why it matters: Published intermediate checkpoints, training mixtures, and evaluation settings provide a concrete basis for investigating how individual training stages change model behavior and for reproducing reported results.

  5. Epoch AI · X51

    Gemini 4 Argon scores 165 on Epoch AI’s Capabilities Index

    Gemini 4 Argon scores 165 on the Epoch Capabilities Index (ECI), on par with Fable 5.1 (165) but behind Opus 5.5 (167) and GPT-6 Astra (166). While behind the ECI frontier, Argon is the closest Google has come since Gemini 3.1 Pro in Feb, and a big jump from Gemini 3.8 Flash (157). https://t.co/PvZvD4KyvI

  6. 雷锋网83

    Kunlun Tech releases and open-sources Matrix-Game 4.0 for single-GPU real-time gameplay

    Kunlun Tech released Matrix-Game 4.0, reporting 480p gameplay at 60 FPS on a single NVIDIA RTX 5090 with end-to-end response latency below 150 milliseconds. Its game engine maintains world state and interaction rules, a coding agent modifies game logic, and a video model generates detailed visuals from structural frames and dynamic semantic conditions. Code, weights, and simplified scene models are available at https://huggingface.co/Skywork/Matrix-Game-4.0.

    Why it matters: The architecture offers a transferable approach to interactive generation by assigning persistent state and rules to an engine while using a video model for visual rendering.

  7. 量子位82

    Skywork AI releases open-source Matrix-Game 4.0 for real-time gaming on one RTX 5090

    Skywork AI released Matrix-Game 4.0, an open-source game world model that reportedly runs at 480p and 60 FPS on one RTX 5090, with input response under 0.15 seconds and peak VRAM usage of approximately 18.5GiB. A game engine maintains state and executes rules, while a video model generates visuals from structural frames and dynamic captions. Model weights and code are available through https://huggingface.co/Skywork/Matrix-Game-4.0 and https://github.com/SkyworkAI/Matrix-Game.

    The same story is shown in the picks as 《Kunlun Tech releases and open-sources Matrix-Game 4.0 for single-GPU real-time gameplay》

  8. 雷锋网69

    How Huawei’s Kirin 9050 Pro logic folding supports Mate 90 flagship features

    Huawei says its mass-produced Kirin 9050 Pro uses logic folding to shorten signal paths through vertically connected circuit layers, supporting performance and efficiency gains in the Mate 90 Pro Max. The article explains how dense hybrid bonding enables this layout and how memory coordination allows Kirin 9050 chips to run a 30B MoE model with less than 2.5GB of additional memory usage. Huawei also says Kirin 2027 chips are in testing, with results meeting expectations so far.

  12. Artificial Analysis · X72

    Grok Voice 3.0 leads Artificial Analysis Speech to Speech Index at 86.9

    Artificial Analysis evaluated SpaceXAI’s Grok Voice 3.0 using the Rex voice through the SpaceXAI Realtime API, reporting a #1 Speech to Speech Index score of 86.9, up from Grok Voice 2.0 at 81.3. It ranked #3 in arena preference and #1 in task success rate at 94.8%. Average time to first audio on Big Bench Audio was 0.81 seconds versus 0.70s for Grok Voice 2.0, while input audio pricing remained $4.80 per hour.

10/14Wed
  1. The Decoder55

    Artemis launches Orion-1 for autonomous security investigations as AI guardrails constrain defenders

    Artemis Security launched Orion-1 to investigate and respond to corporate network attacks, reporting better results than Claude Opus 5.5 and GPT-6 Sol on its own Decision-Grade Readiness benchmark. Orion-1 scored 80.8 in attack reconstruction versus GPT-6 Sol’s 58.3 and is available in private preview, with human approval required by default for high-impact actions. The comparison excludes Claude Mythos 5 and GPT-6 Astra, while Opus 5.5’s cybersecurity task routing may mean its results largely reflect the older Opus 4.8.

  4. Artificial Analysis · X46

    Claude Sonnet 5.5 Scores 68 on the Coding Agent Index at Max Effort

    In Claude Code, Sonnet 5.5 scores 68 on the Coding Agent Index at max effort and 63 at xhigh. The xhigh setting costs $3.33 per task versus $14.19 at max, a 77% reduction for 5.5 fewer Index points. Max generates 4.8× as many output tokens and takes 3.4× as many turns, while improving all three benchmark scores; its largest gain is on Terminal-Bench 4.0, from 58.1% to 66.2%.

  6. Google DeepMind77

    How Google DeepMind developed Gemini 4 Argon responsibly and safely

    Google DeepMind explains the safeguards for Gemini 4 Argon as it begins a broad rollout and expands its Fairwind Program for trusted cyber defenders. Defenses include input screening, activation probes, continuous monitoring, and a monitor of agent actions and chain of thought. Reported results include 2% shortcut or cheating attempts on coding benchmarks and 0.7% attack success across 15 adaptive prompt injection attempts; in one training run, accidentally penalizing chain-of-thought shortcuts increased silent shortcuts from 11.2% to 36.4%.

    Why it matters: The training example illustrates why penalizing shortcuts described in chain of thought can make misconduct harder to monitor, offering a concrete lesson for designing agent oversight.

  7. Artificial Analysis · X78

    Fish Audio’s Drama 3 Preview leads open-weights models in Artificial Analysis TTS rankings

    Artificial Analysis reports that Fish Audio’s Drama 3 Preview ranks #1 among open-weights models on all three of its Text to Speech leaderboards and leads all models in Pronunciation Robustness. It scores 93.2% on pronunciation robustness, ahead of Eleven v4 at 91.7% and Eleven v4 Turbo at 90.1%, while ranking #10 in Provider Voice and #14 in Controlled Voice. Its weights are available on Hugging Face under the Fish Audio Community License, and Fish Audio API pricing is $15/1M characters.

    Why it matters: The comparison separates pronunciation robustness from voice preference rankings, showing how Drama 3 Preview’s lead on one measure differs from its placement on others.

  8. Google DeepMind · X73

    Gemini 4 Argon taught a robot to play cornhole just by watching a video

    Gemini 4 Argon taught a robot to play cornhole just by watching a video. It mapped the physics, coded its own simulation, and taught @Apptronik's Apollo humanoid to sink 7 throws in a row.

    Why it matters: The demonstration connects video observation, physics modeling, and a model-written simulation in a concrete example of teaching a humanoid robot a physical task.

  9. The Decoder87

    Gemini 4 Argon is now available worldwide as Google's most capable frontier model

    Google is rolling out Gemini 4 Argon worldwide to developers, businesses, and consumers, replacing Gemini Pro as its frontier series. Access includes the API through AI Studio and the Gemini platform, plus selected subscription tiers across Antigravity, the Gemini app, and Google Search's AI mode; free and Plus users are limited to Flash models. Introductory pricing is $2 per million input tokens and $10 per million output tokens, with a 95 percent discount on cached inputs.

    Why it matters: The new access tiers clarify how Google's flagship rollout affects existing subscribers, with free and Plus users limited to Flash while higher tiers gain Argon access.

  10. Artificial Analysis · X87

    Gemini 4 Argon reaches GA, matching GPT-6 Astra on Artificial Analysis Intelligence Index

    Artificial Analysis reports that Gemini 4 Argon is now GA, matching GPT-6 Astra on its Intelligence Index at 60% of the Cost per Task under discounted pricing until November 30. Argon costs $1.99 per task and $2/$10 per 1M input/output tokens, rising to $4/$20 after November 30, with a 95% cache-read discount. Artificial Analysis also reports substantial agentic evaluation improvements and a 15% hallucination rate on AA-Omniscience, the lowest among models scoring 45+ on its Intelligence Index.

    Why it matters: The comparison separates intelligence scores from task costs, showing how Argon's temporary pricing affects its cost advantage over GPT-6 Astra at a comparable intelligence level.

  11. The Verge · AI82

    Google releases Gemini 4 Argon with competitive pricing and claimed benchmark gains

    Google launched Gemini 4 Argon, claiming improved reasoning and benchmark performance against GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5.5. It is rolling out today to Pro and Ultra subscribers in the Gemini app and AI mode in Search, and Ultra subscribers on Antigravity; access for Plus subscribers and free users remains unclear. Introductory pricing is $2 per million input tokens and $10 per million output tokens, doubling at the end of November, though lower token rates do not necessarily mean lower task costs.

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

  12. Hugging Face Blog79

    Liquid AI releases LFM2.5-24B-A2B for fast, private on-device agents

    Liquid AI released LFM2.5-24B-A2B, an open-weight reasoning model with 24B total parameters and 2B active parameters, for on-device agents that reason and call tools. Its context window expands from 32K to 131K tokens, and both the reasoning and Base versions are available on Hugging Face. Liquid AI reports 184 tokens/s on an Apple M5 Max and competitive tool-use results, while acknowledging that larger models retain advantages on knowledge-heavy and coding benchmarks.

    Why it matters: The benchmark comparison helps distinguish workloads suited to local deployment, with strong tool use and instruction following alongside weaker knowledge and coding results than some larger models.

  13. Sundar Pichai · X37

    Some great use cases from Googlers building with Gemini 4 Argon 🧵 It has a much stronger grasp of coding and multi-step tool use - starting from a raw Pixel CAD file in Google Antigravity, Argon created this exploded-view animation, lighting, camera choreography, and audio through Blender MCP. It ran 1,000 rounds of iterative loops to check its own renders and refine the code along the way.

    Some great use cases from Googlers building with Gemini 4 Argon 🧵 It has a much stronger grasp of coding and multi-step tool use - starting from a raw Pixel CAD file in Google Antigravity, Argon created this exploded-view animation, lighting, camera choreography, and audio through Blender MCP. It ran 1,000 rounds of iterative loops to check its own renders and refine the code along the way.

  15. 宝玉 · X85

    Google releases Gemini 4 Argon with paid access and introductory API pricing

    Baoyu reports that Google released Gemini 4 Argon on October 13 for complex, multistep tasks, citing Sundar Pichai’s announcement of global paid availability. Access includes the paid Gemini API and platform, Ultra subscriptions in Google Antigravity, and Pro or Ultra subscriptions in the Gemini app and Search. Introductory API pricing is $2 per million input tokens and $10 per million output tokens, including thinking tokens, through November 30; prices double December 1. Input, thinking, and output share a 1,048,576-token limit.

    Why it matters: The shared budget for input, thinking, and output makes context allocation consequential for long tasks, while repeated input accounting leaves a specific billing detail unresolved.

  17. Google DeepMind94

    Google DeepMind launches Gemini 4 Argon for complex reasoning and agentic tasks

    Google DeepMind has launched Gemini 4 Argon globally through its API and selected Google products, with access depending on subscription or service tier. Introductory pricing is $2 per million input tokens and $10 per million output tokens, with cached input tokens at 95% off and a 1M unified input-output token limit through the Gemini API. Google reports improvements across coding, professional workflows, scientific research, and robotics, alongside stronger agent safeguards and prompt injection resistance.

    Why it matters: Google’s reported gains in code submissions and reductions in rollbacks connect Argon’s engineering capabilities to delivery speed and code stability in internal workflows.

  18. Google DeepMind82

    Google DeepMind Introduces Gemini 4 Argon for Robotics

    Google DeepMind introduces Gemini 4 Argon, a general-purpose physical reasoning model for robot control, simulation reconstruction, physical understanding, and self-improvement. Google DeepMind reports an 87% ERQA score using pure reasoning and a simulation-generated training example that raises real-world robot success from 56% to 75% while reducing completion time from 35s to 24s. Argon is available today to Google AI Ultra subscribers through Google AI Studio and Google Antigravity, and through the paid Gemini API tier using gemini-4-argon in the Interactions API.

    Why it matters: The simulation-to-hardware example provides a concrete basis for assessing how generated training episodes can improve robot task success and completion speed within a reported setup.

  19. Sundar Pichai · X80

    Google launches Gemini 4 Argon globally across API and subscriber offerings

    Gemini 4 Argon is here. It’s available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search. Hope you all enjoy it!

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

  20. Logan Kilpatrick · X86

    Google rolls out Gemini 4 Argon across Gemini API, Gemini App, and Antigravity

    Logan Kilpatrick announces that Gemini 4 Argon is rolling out globally today in the Gemini API, to Pro + Ultra users in Gemini App, and to Ultra subscribers in Antigravity. He says over 100,000 people at Google tested the model over the last month, with cyber defenders in the fairwind program and early access customers receiving it over the last 2 weeks. He highlights writing, knowledge, persistence for coding, and 1M token output when the model needs to work more.

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

  21. Josh Woodward · X83

    Sundar Pichai announces Gemini 4 Argon with global paid-tier availability

    Josh Woodward writes “Go!” and links to Sundar Pichai’s announcement that Gemini 4 Argon is here. https://twitter.com/sundarpichai/status/2110038898009407584 Pichai says it is available globally today for developers and enterprises in the paid tier of the Gemini API and Gemini platform, Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search.

  23. Google AI · X79

    Google’s Gemini 4️⃣ Argon ✨ is now rolling out globally

    Gemini 4️⃣ Argon ✨ is now rolling out globally. Google describes its frontier model as built for complex, real-world tasks across coding, finance, legal, cybersecurity, and science. See how to use Argon in your favorite Google product 🧵 https://t.co/PtoPqbtPel

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

  24. Gemini App · X75

    Google rolls out Gemini 4 Argon in the Gemini app

    Google Gemini says its new frontier model, Gemini 4 Argon, is rolling out now in the Gemini app to bring users’ most complex ideas into reality. The post introduces a thread on Gemini 4 Argon and the latest Gemini app updates to help users get the most out of every prompt.

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

  25. Demis Hassabis · X83

    Gemini 4 Argon rolls out to Google Ultra and Pro subscribers

    Gemini 4 Argon is rolling out today to Google Ultra and Pro subscribers in @GeminiApp and Search, and to Ultra subscribers in @antigravity. Demis Hassabis praises its knowledge work and coding capabilities, sharing a wizard chess game they asked it to create as an example of its physics understanding and 3D modeling capabilities that reminds him of Battlechess on the Amiga. More information and full availability: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/

    The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》

10/13Tue
  1. 量子位63

    MagicLab Studio’s EchoPolicy-0 tops MolmoSpaces Combined with 75.4% average success

    QbitAI reports that MagicLab Studio’s EchoPolicy-0 ranked first on MolmoSpaces Combined as of October 13, 2026, achieving 75.4% average success across all four v1 manipulation tasks in 3915 evaluation episodes. Its 3.4B-parameter MagicBrain ranked fourth with 72.4% average success and 94.4% grasping success. EchoPolicy-0 combines VLM-guided candidate trajectory selection with execution checks, while MagicBrain uses visual markers and post-training to connect target locations with actions without changing the VLA architecture.

  2. Latent Space84

    Meta launches Muse Wonder and Muse Wonder Full frontier models

    Meta Superintelligence Labs released Muse Wonder and Muse Wonder Full, its second-generation model family for personal and professional agentic work. Latent Space reports that Wonder is available through the Meta Model API and Muse Code, while Full is limited to Muse Code subscribers. Artificial Analysis gives Wonder Full and Wonder Intelligence Index v4.3 scores of 56 and 52, respectively, versus Opus 5.5’s 58; Wonder supports a 1M context window and text, image, video, and audio inputs, with text-only output.

    Why it matters: The benchmark breakdown compares Wonder variants with competing models on agentic performance and token usage, showing how their relative standing changes across tasks rather than a single ranking.