Microsoft Research has developed an end-to-end machine learning pipeline that uses solar wind observations at L1, forecasts of the AE and Dst indices, and local geological conductivity data to estimate local space weather risks 30 to 60 minutes ahead for 66,935 substations in the contiguous United States.
CoreWeave announced that NVIDIA Vera Rubin NVL72 systems with Spectrum-X 102.4T Ethernet were available on CoreWeave Cloud, with Cognition becoming the first customer to run them in production.
NVIDIA released Kumo Tabular, an open-source tabular foundation model that predicts labels for new rows in a single forward pass given labelled rows, without training, tuning or feature engineering. It supports classification and regression, offers three sizes from 28M to 215M, and was pretrained solely on artificially generated tables. It uses the commercially usable OpenMDW-1.1 licence and ranks first on TabArena, BeyondArena, TALENT and ScoringBench.
Microsoft Research released Quine, an AI research system for biological complexity, comprising a biological world model trained on multimodal data spanning sequences, structures, functions, cell states and imaging, and an interactive harness connecting the model, scientific tools, literature and experimental researchers.
AWS released a solution for automating Amazon Textract adapter lifecycle management across accounts. By externalising adapter IDs to AWS Systems Manager Parameter Store, production adapter references can be updated without downtime or application redeployment, reducing coordination that previously took hours or days to seconds.
AWS presents an architecture combining EFA and DeepEP on Amazon EKS to scale MoE reinforcement learning training, increasing throughput by 40%.
Using the open-source SkyRL framework on Amazon SageMaker HyperPod, multimodal reinforcement learning post-training with GRPO increased the Qwen3-VL-8B vision-language model’s maze-solving success rate from 43.75% to over 95% on a fixed evaluation set of 64 mazes.
Amazon SageMaker HyperPod with Cloud Native Qumulo (CNQ) and Cloud Data Fabric (CDF) lets training clusters read remote datasets directly across AWS Regions without copying data or changing code.
NVIDIA joined a global research consortium including Google DeepMind and EMBL-EBI to release predicted 3D protein complex structures for over 2,800 viruses through the AlphaFold Database, free for any scientist to use.
NVIDIA validation engineer Sakeena Fiza validates new hardware before mass production in its data centre systems lab. One memorable moment was the first successful system-level enumeration of the Rubin GPU. She compares validation to solving crimes, finding and reproducing issues before customers do, across trays, racks, clusters and customers' AI factories.
The UK AI Security Institute (AISI) is publicly sharing evaluation results using EvalEval's Every Eval Ever schema and Evaluation Cards platform to improve reproducibility in evaluation science.
Microsoft published RetroChimera, a retrosynthesis prediction framework, in Nature and open-sourced its implementation and weights. It combines the Transformer model R-SMILES 2 with GNN-based NeuralLoc, using a learned ensemble to rerank predictions. Chemists preferred its single-step reaction predictions in blind testing.
NVIDIA highlighted five AI clean-energy companies at New York Climate Week. ThinkLabs AI's NVIDIA CUDA-based digital twins and agents help Southern California Edison cut grid connection application assessments from 30–45 days to two minutes.
Hex's data agent uses GPT-6 Astra to turn analytical answers into interactive visual reports that employees want to share.
Salesforce unveiled its first CRM reasoning model, Koa, at Dreamforce. Post-trained on NVIDIA Nemotron 3 Super, it uses a proprietary synthetic dataset derived from nearly three decades of enterprise CRM deployments across more than 14 industries, including manufacturing, financial services, healthcare and travel.
Google Research proposed Retrieve-for-Train, using offline reinforcement learning to find reward-aligned query fan-out and compile it into supervision, then distilling it into a 53.9M-parameter diffusion retriever. At inference, it performs one non-autoregressive query fan-out without generating CoT reasoning tokens.
NVIDIA announced several advances for Vera Rubin and the DSX platform at AI Infra Summit, focusing on energy-efficiency improvements in token throughput per megawatt.
At ACL 2026, Google Research proposed ToolGrad, an answer-first, question-later approach that generates real tool-call chains before deriving user queries, replacing traditional DFS trial-and-error annotation.
OpenAI announced Data agent in ChatGPT Work is available to everyone, connecting enterprise data, discovering insights and building interactive dashboards with AI through natural language.
TRL v1.14's AsyncGRPOTrainer now supports training and synchronising only LoRA adapters to vLLM. A rank-1 adapter is just a few MB and can transfer through a Storage Bucket mounted to each job, without NCCL.
Google DeepMind launched AlphaGenome Atlas, a platform containing predicted effects for 9 billion single-nucleotide variants across the human genome. At 1 PB, it is over 30 times the size of the AlphaFold Database.
Google Research evaluated cross-population transfer of polygenic risk scores (PRS) on eight clinical measures using European UK Biobank data and nearly 200,000 Japanese participants from Biobank Japan. At target-population samples of 15,000 or more, target-specific models outperformed mixed European-data training, while highly genetically correlated traits continued to benefit from European data until target samples reached 25,000–40,000 or more.
Google Research partnered with HHMI Janelia, the University of Cambridge and others to publish in Cell a complete connectome of a male fruit fly’s brain and central nervous system. It contains over 166,000 neurons and 125 million synaptic connections, making it the largest brain map to date by neuron count.
A public low-cost approach fine-tunes LFM2.5-350M with TRL GRPO using around 500 samples and 100 steps, raising IFStruct from 22.6% to 29.7%. Training fits free Colab or Kaggle GPUs, with local llama.cpp evaluation on a MacBook and code open on GitHub.
Google Research proposed MAPL-EMIT, a Swin-S Transformer-based deep-learning framework automatically detecting methane plumes, predicting enhancements and locating them in NASA EMIT hyperspectral satellite data, with 84% recall on expert-labelled plumes.
Google Research released TimesFM-3, its first time-series foundation model pretrained natively for multivariate forecasting, with 330 million parameters and over 1 trillion pretraining time points.
Microsoft Research introduced GigaPath-Flash and GigaTIME-Flash, greatly reducing pathology compute requirements with a 22M-parameter ViT-S backbone distilled from the billion-parameter GigaPath encoder, open-source under Apache 2.0.
Google Research introduced the experimental Planetary Prediction Engine (PPE) under Google Earth AI. From a natural language query, it autonomously discovers geospatial data, engineers features, trains and evaluates models and produces reports, compressing weeks of manual data engineering into minutes.
Google released self-supervised GlucoFM, separating slow glucose trends from short-term fluctuations. Pretrained on 109,066 hours of unlabelled CGM data, it averaged 5.8 percentage points higher PR-AUC than the best GluFormer variant across 14 evaluations on seven clinical tasks in four cohorts, and achieved the lowest post-meal response prediction MAE.
Sentence Transformers v6.0 adds a fourth model type, MultiVectorEncoder, for ColBERT-style late-interaction retrieval, with a complete training approach.
Multiverse Computing published a paper proposing Quantization-Aware Healing (QAH). After compressing GPT-OSS 120B to 60B parameters and quantising it to MXFP4, the method distils directly from the original uncompressed model rather than a reconstructed bfloat16 checkpoint.
Google introduced Biomarker Discovery Framework, a human-supervised multi-agent system organising candidate biomarker prioritisation into iterative research cycles. Across 9,279 participant observations in three cohorts, it automatically identified 41 candidate digital mental health biomarkers and 25 metabolic candidates, including an association between sleep-duration variability and PHQ-8 severity (ρ = 0.252).
Google Research proposed Mobility-Embedded POIs (ME-POIs), combining anonymised mobility patterns with text descriptions to generate place embeddings encoding both identity and dynamic function.
Microsoft Research released deep-learning DFT functional Skala 1.1, trained on 2.5 times more data than the previous public version. It ranks first in 32 of GMTKN55's 55 categories with 2.8 kcal/mol weighted average error. Available in CP2K and being integrated into Psi4, FHI-aims, ORCA and VASP, it is tracked by a new living performance benchmark.
IBM Research published ALTK-Evolve research on the Hugging Face blog. Tests of eight models on 585 multi-step AppWorld tasks found agent memory is not an on/off switch but a dosage requiring model-specific calibration.
A Hugging Face blog describes a constraint-aware GPU allocator compared with FIFO under identical hardware and workloads. Across five real contention scenarios, GPU utilisation rose from 52–85% to 72–88%, while priority-weighted output improved 24.6–105.1%, averaging 52%.
Hugging Face published its open-source model observatory report for January–August 2026. Hub data shows Chinese labs released the largest open-source models by parameter count in most months, with Chinese monthly peaks between 754 billion and 2.78 trillion parameters, while US models stayed below 130 billion in five of seven months.
Hugging Face's OlmoEarth Studio now computes and exports embeddings. Users choose an area, period, encoder variant and resolution through UI or API and receive Cloud-Optimized GeoTIFF (COG) results.
Google Research's knowledge profiling framework evaluated 13 LLMs on WikiProfile's 2,150 Wikipedia facts. Gemini-3-Pro and GPT-5 encoded 95–98% but failed direct recall of 26–34%, and still failed 11–12% with thinking enabled. It argues frontier factual errors arise more from knowledge accessibility than absence, shifting the bottleneck from acquisition to use.
Microsoft Research released CARE-X, a unified chest X-ray vision-language research model for report generation and structured prediction. It rewards clinical correctness using multitask reinforcement learning (DAPO). Generation and dual-inference modes cover lesion presence and negation, localisation, multilabel classification, catheter and tube malposition detection, and localisation of 29 anatomical regions.