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Thursday37 items

  1. Hugging FaceFirst-partyModel releases
    AI score73

    NVIDIA Releases Kumo Relational Relational Foundation Model, Predicts Without Training

    NVIDIA has released Kumo Relational, a pretrained relational foundation model that can make predictions directly on enterprise databases in a single forward pass, without task-specific training, feature engineering, or table flattening. Across 41 tasks on 15 databases—RelBench V1/V2, SAP SALT, and 4DBInfer—it outperforms supervised relational deep learning models and gradient-boosted tree pipelines built with expert feature engineering, achieving an 8% higher MRR than the AutoGluon ensemble on SAP SALT. The model is available through NVIDIA's open-source GPU-native structured-data-models library or NVIDIA NIM, and supports databases with 500B+ rows.

    #Model releases#English text is being prepared.#Agents

  2. ZhidongxiModel releases
    AI score70

    NVIDIA Open-Sources Surgical World Model Cosmos3-H-Dreams, Enabling Real-Time Surgical Scene Generation on a Single GPU

    NVIDIA has released and open-sourced the real-time generative surgical world model Cosmos3-H-Dreams, which can generate surgical scenes in real time based on robot operation instructions, simulating instrument movement, tissue retraction, and deformation. The model is built on the 16-billion-parameter Cosmos3-H-Embodied-Nano, with training data comprising approximately 526 minutes of real surgical video selected from 4,792 surgical recordings, and it supports 28-dimensional action input. Using teacher-student distillation and the FlashDreams streaming inference framework, the model requires only a single NVIDIA RTX PRO 6000 GPU for real-time interaction, and two student model versions with 4-step and 2-step denoising have been open-sourced.

    Same story · 4 reports

    #Model releases#English text is being prepared.#Embodied AI

  3. Baseten Base Labs: Model ResearchFirst-partyModel releases
    AI score68

    Baseten Base Labs releases decision models mammoth-1 and elephant-1

    Baseten's Base Labs releases two decision models, elephant-1 (9B) and mammoth-1 (27B), both fully fine-tuned from Qwen3.5, with model weights publicly released. mammoth-1 ranks first among 208 models on the Jev Decision Index 0.4 with a score of 71.24, leading the second-place Perplexity Decider v1.2 by 1.8 points; elephant-1 scores 62.65 on that leaderboard and ties for first with d1-ultra on the Artificial Analysis Decision Index with a score of 65. Training used the eighth version of the data mixture with 6.21M samples, and replaced the full-attention layers with a block bidirectional attention mask; under fixed data, this change raised the public 10k-sample score from 44.8 to 48.6.

    Max Kirkby

    Same story · 2 reports

    #Model releases#Reasoning#English text is being prepared.

  5. kling_aiFirst-partyModel releases
    AI score60

    English translation is being prepared.

    Quoted postMorphic · @morphic

    Introducing Kling 4.0 on Morphic. Get 10% discount on Kling 4.0 generations through November 20 on Morphic. Native 30-second generations, up to 10 keyframes, 15 references, consistent on-screen text, multilingual support. 4K and 10-bit HDR. https://t.co/KEJsRjqn5m https://t.co/ltkDtrfGCv

    #Model releases#Video#Multimodal

  6. Wired AIModel releases
    AI score74

    Recursive Releases Open-Source Coding Model Flint, Says Most of Development Process Was Completed by AI

    Recursive released Flint, its first open-weight coding model, with 40B parameters. CEO Richard Socher said that nearly every stage of the model pipeline was completed by its Eureka Machine system, including a new base training method, the model architecture, and code optimized for specific chips. A WIRED author tested it on a Mac with 48GB of memory, using it to build a command-line to-do app, a web research scraping program, and an RSI educational website, and found its speed and accuracy superior to models of the same size. Recursive has raised $650 million from Google Ventures, Greycroft, Nvidia, and AMD Ventures, at a valuation of $4.65 billion.

    Same story · 9 reports

    #Model releases#Open-source ecosystem#English text is being prepared.

Wednesday42 items

  1. The DecoderModel releases
    AI score73

    Inception Releases Diffusion Language Model Mercury 3, Claiming Speeds Four to Eight Times Faster Than GPT-6 Luna

    Inception has released the diffusion language model Mercury 3, claiming it wins 8 of 12 benchmarks and runs four to eight times faster than GPT-6 Luna and Claude Haiku 5.5. In Inception's own testing, Mercury 3 scored 76.84 on IFBench, 15 points higher than GPT-6 Luna; it scored 51.08 on SWE-Bench Atlas, versus 33.33 and 32.26 for its competitors. Claude Haiku 5.5 led on the DeepSWE long-code test and long-text retrieval. The model generates 1,068 tokens per second on Nvidia GPUs, is priced at $0.20 per million input tokens and $0.70 per million output tokens, with a 50% discount during the launch period, and is now available on the Inception platform as well as OpenRouter, Baseten, and Vercel; there are currently no independent tests.

    #Model releases#Reasoning#English text is being prepared.

  8. Unite.AIModel releases
    AI score64

    NVIDIA Releases Nemotron 3.5 Series of Open Models, Covering Embedding, Reranking, and Document Parsing

    On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, launching Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and the 631-million-parameter Nemotron 3.5 Parse, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100; the open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM as well as Baseten, DeepInfra, and FriendliAI.

    #Model releases#RAG#Multimodal

  12. LeiphoneModel releases
    AI score83

    MiniMax M3.1 Flash released, tops Agent task evaluation

    MiniMax released M3.1 Flash and claimed the previously anonymous model Space Bunny; it ranked first in the Automation-Bench complex Agent task comparison, and tied for first with Opus 5 on BankerToolBench at 76.2%. Leiphone used the same 3D campus building task for a real-world test: MiniMax Code plus M3.1 Flash took 55.4 minutes, while OpenCode plus DeepSeek V4.1 Flash took about 121 minutes, with model costs for both around 6.5 yuan. Pricing is 1 yuan per million input Tokens, 4 yuan for output, and 0.1 yuan for cache reads, roughly half the input and output prices of the previous generation M3, with no peak/off-peak distinction all day.

    Same story · 19 reports

    #Model releases#Agents#English text is being prepared.