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Thursday

  1. Shao Meng

    NVIDIA Releases OpenShell Reference Architecture, Governing Agent Security with a Three-Layer Approach

    NVIDIA has released the OpenShell reference architecture, arguing that Agents cannot govern themselves and that security and governance should be placed at the infrastructure layer, which Agents cannot reach. The approach has three layers: the open-source runtime OpenShell defines permission boundaries through Gateway, Supervisor, Sandbox, and a formally verified Policy Prover; Vera CPU provides 88 Olympus cores and 1.2 TB/s memory bandwidth, with CoreWeave measuring sandbox startup more than 3x faster and Agent tasks on Terminal Bench 1.8x faster; BlueField-4 DPU plus Sentry provides optional hardware-level monitoring that can isolate violating Agents within a few milliseconds.

    NVIDIA has released the OpenShell reference architecture, arguing that Agents cannot govern themselves and that security and governance should be placed at the infrastructure layer, which Agents cannot reach. The approach has three layers: the open-source runtime OpenShell defines permission boundaries through Gateway, Supervisor, Sandbox, and a formally verified Policy Prover; Vera CPU provides 88 Olympus cores and 1.2 TB/s memory bandwidth, with CoreWeave measuring sandbox startup more than 3x

    Matched fieldsTitle、Summary、Body

    #hot-signal#Product updates#Agents#Safety / alignment
  3. OpenClaw

    OpenClaw Podcast Discusses NVIDIA RTX Spark and Local AI

    Episode 14 of OpenClaw's The ClawCast features NVIDIA's Apoorv Chaudhary, who joins hrudolph and Pat_Erichsen to discuss local AI, privacy, hardware limitations, and scenarios where cloud models still apply.

    Episode 14 of OpenClaw's The ClawCast features NVIDIA's Apoorv Chaudhary, who joins hrudolph and Pat Erichsen to discuss local AI, privacy, hardware limitations, and scenarios where cloud models still apply.

    Matched fieldsTitle、Summary、Body

    #company#Industry news#English text is being prepared.#OpenAI
  4. Aravind Srinivas

    Perplexity and NVIDIA propose JOLT self-distillation training method

    Perplexity and NVIDIA collaboratively propose a post-training method, JOLT: it lets the same model serve as both teacher and student, and trains the teacher to learn from the rewards of its own attempts while penalizing drift away from the student's behavior. On the MATH benchmark, JOLT achieves accuracy comparable to GRPO with 6.5 times fewer training tokens; JOLT+ additionally provides the student model with outcome-based rewards, clearly surpassing GRPO on math, coding, terminal tasks, and tool-use benchmarks.

    Perplexity and NVIDIA collaboratively propose a post-training method, JOLT: it lets the same model serve as both teacher and student, and trains the teacher to learn from the rewards of its own attempts while penalizing drift away from the student's behavior. On the MATH benchmark, JOLT achieves accuracy comparable to GRPO with 6.5 times fewer training tokens; JOLT+ additionally provides the student model with outcome-based rewards, clearly surpassing GRPO on math, coding, terminal tasks, and to

    Matched fieldsTitle、Summary、Body

    #editorial#Papers/Research#Reasoning#English text is being prepared.
  5. Hugging FaceSelected

    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.

    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 S

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

Wednesday

  1. CNBC Technology

    Nvidia unveils truck-transportable Vera Rubin containerized AI factory DSX Pod

    Nvidia has unveiled the DSX Pod AI Factory, which packs 7 Vera Rubin AI racks into a container roughly the size of a standard shipping container and weighing about 28,000 kilograms. Cabling and liquid cooling are assembled at the factory, and the unit can be transported by truck over ordinary roads. Nvidia says the solution can cut the time for a new AI data center to start outputting tokens from 6 months to 14 days. The product is a reference design, shipped by manufacturers including Foxconn, Gigabyte, Lenovo, HPE, Quanta, and Pegatron, with deliveries expected to begin in early 2027; no price has been announced.

    Nvidia has unveiled the DSX Pod AI Factory, which packs 7 Vera Rubin AI racks into a container roughly the size of a standard shipping container and weighing about 28,000 kilograms. Cabling and liquid cooling are assembled at the factory, and the unit can be transported by truck over ordinary roads. Nvidia says the solution can cut the time for a new AI data center to start outputting tokens from 6 months to 14 days. The product is a reference design, shipped by manufacturers including Foxconn,

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    #tech-media#Product updates#English text is being prepared.#English text is being prepared.
  2. heise online

    Nvidia launches Open Agent Safety Platform, using BlueField-4 DPU to monitor agents

    At GTC Berlin, Nvidia launched the Open Agent Safety Platform, adding hardware-level controls around the open-source runtime OpenShell; a BlueField-4 DPU works with Sentry software to independently monitor agent behavior and, when it crosses boundaries, isolate and stop it within milliseconds. OpenShell supports third-party agent frameworks such as Anthropic's Claude Code and OpenAI's Codex, and Nvidia says more than 100 organizations already use the platform's technology. Nvidia also introduced the Agent Knowledge Foundation and the Auto-Ontology library, designed to let agents access structured and unstructured data without enterprise data leaving local premises, and expanded its Nemotron 3.5 Super VL, Nemotron 3.5 Bolt 74B-A9B and Kumo series models.

    At GTC Berlin, Nvidia launched the Open Agent Safety Platform, adding hardware-level controls around the open-source runtime OpenShell; a BlueField-4 DPU works with Sentry software to independently monitor agent behavior and, when it crosses boundaries, isolate and stop it within milliseconds. OpenShell supports third-party agent frameworks such as Anthropic's Claude Code and OpenAI's Codex, and Nvidia says more than 100 organizations already use the platform's technology. Nvidia also introduced

    Matched fieldsTitle、Summary、Body

    #hot-signal#Product updates#Agents#Safety / alignment
  3. Hugging Face

    NVIDIA Kumo Forecast: An Interpretable Forecasting Model Combining Pretrained Time Series Representations and Historical Pattern Retrieval

    NVIDIA Kumo Forecast (formerly NV-Tesseract Forecasting) is a multivariate time series forecasting model built on the MOMENT-1-large backbone, with an approximately 340-million-parameter encoder plus a trainable forecasting head, and the encoder weights are frozen. Its DARR mode retrieves patterns from historical data via k-nearest neighbors and fuses them with direct forecasts, and it also provides an interpretability framework that outputs lag and feature attributions by forecast step. The model is released under the Apache 2.0 license and supports input sequence lengths from 256 to 2048.

    NVIDIA Kumo Forecast (formerly NV-Tesseract Forecasting) is a multivariate time series forecasting model built on the MOMENT-1-large backbone, with an approximately 340-million-parameter encoder plus a trainable forecasting head, and the encoder weights are frozen. Its DARR mode retrieves patterns from historical data via k-nearest neighbors and fuses them with direct forecasts, and it also provides an interpretability framework that outputs lag and feature attributions by forecast step. The mod

    Matched fieldsTitle、Summary、Body

    #lab#Model releases#Open-source ecosystem#English text is being prepared.
  4. Testing Catalog

    Mistral AI and NVIDIA Partner to Advance AI for Industrial Engineering

    Mistral AI announced a partnership with NVIDIA to bring AI to industrial engineering, including faster simulation, open benchmarks and supporting datasets for physical AI, and evaluations of agentic AI in CAE and EDA scenarios. Mistral's models will also be integrated into the NVIDIA Omniverse libraries for GPU-accelerated 3D scene generation.

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    #hot-signal#Industry news#English text is being prepared.#Multimodal
  5. Databricks Blog

    Databricks AI Runtime is now GA: Fine-tune open models on serverless NVIDIA GPUs

    Databricks AI Runtime (AIR) is now generally available. Developers can train and fine-tune open models on serverless NVIDIA GPUs without managing GPU infrastructure themselves. AIR supports NVIDIA NeMo, NGC containers, and Nemotron cookbooks, and supports multi-node distributed training, with clusters and networking managed by Databricks. Trained models can be deployed for real-time inference through Databricks Model Serving, and access to training data is governed by Unity Catalog.

    Matched fieldsTitle、Summary、Body

    #company#Product updates#English text is being prepared.#English text is being prepared.
  13. Unite.AI

    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.

    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 retri

    Matched fieldsTitle、Summary、Body

    #hot-signal#Model releases#RAG#Multimodal