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Ai2 Releases OlmoEarth v1.3, Offering Global 10-Meter Resolution Satellite Embeddings

The Allen Institute for AI released OlmoEarth v1.3 on October 22, 2026, including the OlmoEarth v1.3 Base model, 128-dimensional and 64-dimensional global precomputed annual embeddings, the login-free browser-based OlmoEarth Embeddings Explorer, and a technical report, all under the OlmoEarth Artifact License. The model is a 128-million-parameter ViT-Base foundation model that processes Sentinel-1, Sentinel-2, and Landsat image time series, outputting a 128-dimensional vector for every 10-meter pixel worldwide. Ai2 states that across 27 evaluations covering six continents, the 128-dimensional embeddings scored highest on 20 of them, while the 64-dimensional version led on 18; it also introduced PLANTEUR, a benchmark for crop types in tropical farmland, where the 128-dimensional embeddings achieved 24.9 mIoU in the benchmark's leave-one-island-out test, higher than Tessera v2's 19.1 and AlphaEarth Foundations' 15.8.

Hugging Face Releases Open-Source DNA Model Carbon-X, Runs Locally on a Laptop

Hugging Face on Thursday released the open-source DNA understanding model Carbon-X, which can run locally on a laptop, letting users ask in natural language how DNA changes affect gene activity and health. The model was developed in collaboration with researchers at Zhongguancun Academy and the University of Naples Federico II; it extends the vocabulary of an existing language model to read DNA in six-letter units and was further trained on 300 billion tokens of DNA sequences, English text, and samples pairing DNA with biological descriptions. The model has 14B parameters and can process sequences of about 50,000 base pairs at a time; it cannot upload a complete human genome. The developers also note that the prediction accuracy has not yet been validated through wet-lab experiments.

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.

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.

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.

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Memory Tensor Open-Sources MemT 1.0 Series Memory-Native Models

Memory Tensor has open-sourced and released the MemT 1.0 series of memory-native models, including MemT-1.0-Nano with 36B parameters and 3B activated (based on Qwen3.6-35B-A3B) and MemT-1.0-Flash with 288B parameters and 17B activated (based on DeepSeek-V4-Flash), adding a memory computation pathway parallel to attention in selected Transformer modules. The team built a 34-billion-token memory training corpus, of which 20 billion tokens were used for continued pre-training and 14 billion for post-training. In a comprehensive evaluation across 11 memory benchmarks, Flash and Nano achieved average scores of 50.6 and 47.0, respectively, ranking in the top two among the parametric memory methods evaluated; on 500 SWE-bench Verified tasks, the proportion of tasks that Nano succeeded on all three attempts rose from 54.00% for the no-experience baseline to 62.80%.

OpenAI Releases GPT-6 Astra, a Coding Model for Legacy Languages Such as COBOL

OpenAI has released GPT-6 Astra, a coding model for legacy programming languages such as COBOL, Fortran, and Visual Basic. OpenAI built two evaluations for this purpose, MultiLangBench and COBOL migration. GPT-6 Astra scored 87.4% on the 20 languages in MultiLangBench, compared with 26.8% for GPT-5.6 Sol; it scored 86.8% on COBOL migration, or 97.9% at high reasoning effort, while GPT-5.6 Sol scored only 5.4%. The model is now available to developers through the OpenAI API for coding tasks in COBOL, Fortran, OCam, and Visual Basic.

Memory Tensor Open-Sources MemT 1.0 Series of Memory-Native Models

Memory Tensor has open-sourced the MemT 1.0 series of memory-native models, comprising two variants: Nano with 36B parameters and 3B activated, and Flash with 288B parameters and 17B activated, built on the Qwen3.6-35B-A3B and DeepSeek-V4-Flash bases, respectively. The models add a memory branch parallel to attention within selected Transformer modules, encoding experience into an updatable matrix state that participates in subsequent generation. Across 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus a no-experience baseline of 66.07%; under a 128K long history, end-to-end inference speed is approximately 1.86 times that of the full-context approach, and the persistent state footprint per trajectory is one sixty-sixth that of the full-context KV Cache.

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Elorian Releases Its First Model Glimpse, Using a Visual Draft Space for Image Reasoning

Elorian has released its first model, Glimpse, opening the API as a research preview, with a waitlist for the chat interface. During reasoning, the model can crop image regions, extract video frames, or run code to measure and compare what it sees, which Elorian calls a visual draft space. Glimpse is priced at $0.60 per million input tokens and $3 per million output tokens, and according to Elorian's evaluations it surpasses GPT-6 Astra on scientific chart reasoning and short video description. The company says the model was trained on several hundred GPUs, a scale notably smaller than the frontier systems it benchmarks against, and it initially targets enterprises with large amounts of video, such as robotics, research laboratories, and media and creative firms.

Meta Releases 816B Open-Source Model Muse Spark Open, Partnering with Apple for Local Execution

Meta has released the open-weight model Muse Spark Open, which uses a MoE architecture with 256 experts, activates about 42B parameters per token, has a context window of 1 million tokens, and supports text, image, video, and audio input but outputs text only. Meta has partnered with Apple to run the approximately 530 GByte four-bit quantized version on two interconnected Mac Studio M5 Ultra machines, distributing computation via the MLX framework and Thunderbolt 5, achieving about 25 output tokens/second at an 8192-token context. Artificial Analysis tests show that it scores 39 on the Intelligence Index, making it the strongest American open-weight model currently available, but it trails MiMo-V2.6-Pro's 46 points and GLM-5.3's 45 points, and ranks last in four Agent tests. The model weights are available on Hugging Face in two versions: 16-bit at approximately 1.6 TByte and four-bit at approximately 530 GByte, with API pricing at $1.25 per million input tokens and $4.25 per million output tokens.

NVIDIA Expands Nemotron Model Family with New Super VL and Two Bolt Models

NVIDIA has added three models to the Nemotron open-source model family, covering enterprise workflows, local AI agents, and conversational applications across data centers, desktops, and edge devices. These include the 120-billion-parameter Nemotron 3.5 Super VL, which NVIDIA claims delivers up to 5x the throughput of the next-best comparable model, with accuracy approaching that of its largest reasoning model at roughly one-fifth the parameter count; there are also two small models in the Nemotron 3.5 Bolt series: 74B-A9B 24GB for everyday agent tasks on RTX and DGX Spark, and E4B 4GB for conversational and physical AI agents. Super VL and Bolt 74B-A9B are already available, and Bolt E4B is scheduled for release on October 23.

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Swiss AI releases the Apertus 1.5 multimodal model, expanding context to 262k tokens

Swiss AI has released Apertus 1.5, which builds on 1.0 by adding native image and audio understanding, a thinking mode, and a 262,144-token context window—4 times larger than 1.0. The model uses early fusion to convert images and audio into discrete tokens and feed them into the same sequence as text; the architecture is unchanged, and it only understands multimodal input while outputting text only. The team says the 8B version outperforms open-source models of the same scale on instruction following and multilingual tasks, its multimodal performance is close to Gemma 4 12B, and it has also released the training data mixture, preference data, and intermediate checkpoints from each stage.