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.
Microsoft Research systematically measured mobile manipulation robot workloads and found that offloading physical AI inference from onboard GPUs to edge or cloud GPUs can improve task success, support larger models and extend battery life.
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.
Hugging Face proposed three tests to quantify benchmark optimisation in speech recognition: a consensus-disagreement probe, masked-entity retrieval and spelling switches. Evaluating 11 open-source ASR models, it found some high-scoring models reproduce errors in VoxPopuli and LibriSpeech reference transcripts even when contradicted by audio, when relevant words are muted or when both spellings fit the audio.
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.
Microsoft Research introduced MindTopo to evaluate multimodal models' reasoning and planning over connectivity, enclosure, order, separation and knotting. Current models perform much better at static-image recognition than interactive planning, with failures mainly in planning rather than perception and overall performance far below humans. Image and video generation helps only when preserving structural relations in a single frame; multi-step operations often change topology or violate constraints.
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.
Microsoft Research proposed Echoverse, constructing twelve training worlds for computer-use agents: ten deep domain worlds and two capability worlds. Code, data and scorers for four worlds are open-sourced.