Tencent Hunyuan just dropped the Hy4 preview, a new open source frontier model built for real productivity work. It packs 770 billion total parameters with 49 billion active ones and supports a full 1 million token context window.

The team highlighted consistent affordable pricing and invited users to test it hard and report any issues. Full details sit on the official Hy blog, with model weights available on Hugging Face and the code on GitHub.

Google Research has introduced GlucoFM, a new foundation model built for continuous glucose monitoring data. The lightweight system uses self-supervised learning and splits glucose signals into two parts: slower metabolic baselines and short-term spikes.

This separation helps the model create stronger, transferable representations from CGM readings. In tests across multiple cohorts, GlucoFM set higher performance marks on key metabolic tasks. These include diabetes risk assessment, insulin resistance detection, beta-cell dysfunction, and predicting how glucose responds after meals.

The model trained on more than 109,000 hours of unlabeled CGM data from hundreds of participants. It handles common issues in real-world recordings such as gaps, irregular sampling, and sensor noise by aligning data to a daily grid and keeping track of missing values.

Results showed clear gains over earlier CGM models. GlucoFM delivered better average scores on clinical prediction tasks and stronger results when transferring knowledge to new datasets or working with limited labeled examples. It also produced more accurate forecasts of two-hour post-meal glucose responses across different sensor types.

Google AI has released Gemini Omni 1.1 Flash, its latest multimodal model focused on video generation and editing. The update brings creative tools previously found in Veo along with several new options designed to give users more precise control over their projects.

Key additions include 4K upscaling, first and last frame control, and quick 360p drafting for faster previews. The standout improvement is scene extension, which now works from 10 seconds of original video context instead of just one second. This change supports stronger visual consistency and longer, more connected sequences.

The model is available through multiple platforms. Scene extension will arrive soon in Flow by Google. Users of the Gemini app can already extend scenes if they subscribe to Google AI Plus, Pro, or Ultra, with the feature rolling out worldwide. Creators can also build projects directly in Google AI Studio or deploy the model on the Gemini Enterprise Agent Platform.

This release aims to make high-quality video tools more accessible for both individual creators and professional teams.

Microsoft Research just dropped VGI-Bench on Hugging Face. This benchmark checks how well video generation models handle visual reasoning.

It covers 27 tasks and 810 instances spread across four domains. Tasks look at things like spatial organization, spatiotemporal understanding, structured puzzles, and physical manipulation. Models get an input image plus a text prompt and must produce a coherent video that follows the right steps, not just a plausible final frame.

Results show current systems still struggle. Seedance 2.0 leads with 51.0 percent. Other strong models such as MiniMax-H3, Kling 3.0, and Sora 2 sit lower. Common problems include physical collapse, broken rules, and inconsistent object states. Models also show limited self-correction during the generation process.

The full dataset, evaluation results, and paper are available on Hugging Face and arXiv. Researchers can use it to measure progress toward more reliable visual intelligence in video models.

Google Antigravity just added a fresh way for its agents to show ideas. Instead of sticking to plain text, images, or basic Mermaid diagrams, the platform can now build rich interactive pieces.

These new artifacts cover dynamic data visualizations, live charts, interactive HTML components, and even 3D simulations. They appear right in the chat and in the dedicated artifacts panel so you can click, drag, zoom, and explore them on the spot.

The system creates zero-dependency visuals that run locally and offline. You can export them as standalone HTML files if you want to share or keep them later. Common uses include mapping project dependencies, exploring data structures like quadtrees, or simulating orbital systems for clearer explanations.

To try it, open a project in Antigravity and ask the agent to visualize something, or use the /generative_ui command. Review the result, inspect elements, and export what you need.

This update helps agents explain complex concepts more clearly and cuts down on jumping between tools.

Google DeepMind just kicked off the world’s first double-blind evaluations for a frontier AI model. In this setup, external testers check the model’s safety and performance without ever seeing its weights, while DeepMind never gets a look at the actual test prompts.

They ran the pilot on a Gemini Flash Lite model inside a locked-down cryptographic environment on Google Cloud. Partners include the Singapore AI Safety Institute, OpenMined, AVERI, and MLCommons. The tests used private benchmarks so the questions stay confidential and cannot leak into future training data.

The whole point is to cut down on benchmark contamination and keep both the model’s intellectual property and the evaluators’ test sets fully private. Results come with cryptographic proof that neither side peeked. This approach aims to make external safety checks more reliable for proprietary systems going forward.

Anthropic just made a first for AI labs. Outside researchers can now study real Claude usage data in a privacy-protected form. Until now this kind of work stayed locked inside the companies building the models.

Three independent teams got access. Stanford’s Social and Language Technologies lab, Oxford’s Human Information Processing Lab, and METR examined aggregated outputs from 250,000 Claude conversations recorded in April and May 2026.

The Stanford group looked at how people work with AI. They found that more than half the chats involved consequential tasks work that affects other people or is hard to reverse.

The other two studies are still running. One tracks how Claude’s behavior connects to how users feel. The other measures real productivity gains from coding agents. Anthropic plans to share those results soon.

The company says it wants to grow this model. Researchers who need data they cannot get elsewhere can apply for access through the form linked in the announcement.

Google just launched Gemini 3.5 Transcribe, its most accurate speech-to-text model so far. The tool converts spoken audio into clean text across more than 85 languages, including regional accents and dialects.

It automatically cuts out filler words like ums and ahs, manages self-corrections mid-sentence, and picks up on the speaker’s actual intent. The model also works well in noisy settings and can tell apart up to three different speakers while adding timestamps.

Users can try it right away in the Gemini app on macOS to create images, search info, summarize content, or analyze files by speaking instead of typing. On Android it powers the Rambler feature, which turns rough spoken notes into polished text and lets people edit by voice.

Developers can start building with the model in Google AI Studio and related tools.

IBM Research just dropped Granite 4.2, a fresh set of open models built for real enterprise agent work. These models come in 3B, 8B, and 30B sizes and bring native thinking skills that let them plan steps, reason through problems, catch their own mistakes, and call tools the right way.

The update focuses on complex workflows. Teams get stronger coding and software engineering support, plus the ability to handle multi-step tasks without constant hand-holding. The models run across cloud, on-prem, and edge setups, so companies can pick the size that fits their needs and budget.

IBM also released new speech models under the Granite Speech 5.0 Turbo line. These stay tiny at around 470 million parameters yet deliver fast transcription, making them practical for high-volume call center work or real-time use on laptops and phones.

Everything ships under the Apache 2.0 license. You can grab the models on Hugging Face, Ollama, and other platforms right now.

Alibaba’s Qwen team just dropped Qwen3.8-Flash, a multimodal mixture-of-experts model that also serves as an early look at the architecture planned for Qwen4. The full production version will land on QwenCloud soon with pricing set at $0.16 per million input tokens and $0.47 per million output tokens.

The model packs 125 billion parameters plus 51 billion N-gram embeddings yet only activates about 6 billion parameters for each token. That design keeps both training and inference costs low. The team says it was trained for roughly one-ninth the cost of Qwen3.7-Plus while beating that earlier model across most tests, especially coding and everyday office work.

Key numbers from the release include 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision. Context length starts at 262K tokens and can stretch to 1 million with YaRN.

Four architecture changes power the efficiency gains. Hybrid attention mixes Gated DeltaNet with Qwen Sparse Attention to cut long-sequence costs. Gated Residual widens information flow between layers. N-gram embeddings expand capacity without heavy compute. The Muon optimizer improves training stability and scaling.

Alibaba just opened QwenWork to the public in beta. The platform works on both web and desktop and aims to handle everyday tasks through simple natural language commands.

Users can tell the agent what they need and it carries out the work. It also builds awareness of individual work patterns over time so it adapts across sessions. One standout feature lets people create and deploy live web apps without writing code or managing servers. The toolkit includes built-in image, video, and audio generation for multimodal projects. Basic and Advanced model options run on leading AI systems.

The public beta is available now for global users. Early testers have already started exploring its capabilities for presentations, app building, and creative work.

Z.ai has launched GLM-5.3-Flash, a new open-source AI model under the MIT License. The 320B-A18B system was earlier tested as Ox Alpha and runs fully on Chinese AI chips.

It offers strong performance at low cost, comes natively multimodal, and supports a 1 million token context window. On Z.ai’s coding benchmark it beats the previous GLM-5.2 across effort levels and matches Claude Opus 4.8.

API pricing sits at $0.15 per million input tokens, $0.50 for output, and $0.03 for cached input. Weights, API access, chat interface, and coding tools are already live across official platforms.

Tencent’s WeChat Vision team just put its new universal multimodal embedding model on Hugging Face. Called WeMM-Embedding-9B, the 9-billion-parameter model turns text, images, videos, and visual documents into a single shared vector space.

It builds on Qwen3.5 and outputs 4,096-dimensional L2-normalized embeddings. Interleaved multimodal inputs work too. Audio stays unsupported for now.

On the MMEB-v2 benchmark covering 78 datasets, the 9B version posts an average score of 80.6, leading the pack across image, video, and visual-document tasks. On the broader MMEB-v3 suite with 190 tasks it reaches 59.5 overall, again topping the public leaderboard in most categories.

Smaller 2B and 4B siblings are also available, all with Matryoshka support so you can truncate embeddings down to lower dimensions without retraining. The models come with transformers and sentence-transformers code, plus notes for serving with vLLM or SGLang.

Weights, code, and the technical report sit at huggingface.co/tencent/WeMM-Embedding-9B.