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.
Google Research released MilleMiglia, a C++ generator of realistic, privacy-preserving benchmark instances for middle-mile logistics delivery. Source and documentation are public on GitHub.
Google Research published the first large-scale real-world study in Nature Cities, redirecting under 2% of trips to alternative routes through Google Maps changes across ten US cities for six months. Median target-road speed rose around 2% and median fuel consumption rates fell 0.5–1.0%, potentially saving thousands of tonnes of CO2e per city annually.
Google Research proposed linear elastic caching, modelling page eviction as a ski-rental problem and using shallow decision trees to predict page TTLs, dynamically resizing caches to minimise total ownership cost. After months in Spanner production, memory fell 15.5%, cache misses rose only 5.5%, TCO dropped around 5% and actual I/O cost impact was just 0.5%. It was also validated on several public cache traces.
Google combined a new single-message encrypted aggregation protocol with TEEs, letting devices submit without multiple online rounds. Google receives only anonymised group insights; raw data is neither exposed nor reconstructed even within hardware protection. TEE attestation proves execution follows public code.
Google DeepMind published a paper proposing Decoupled DiLoCo, splitting large-scale training into decoupled compute islands that communicate through asynchronous data streams, isolating local hardware failures.
Berkeley AI Research proposed GRASP, a gradient-based planner for learned world models. It lifts trajectories into virtual states for parallel optimisation across time, injects randomness directly into state iterations for exploration and reshapes gradients to give actions clear signals. Avoiding fragile state-input gradients in high-dimensional visual models makes long-horizon planning more practical and robust.
Google Research released TurboQuant, compressing the KV cache to three bits without loss of model accuracy or training and fine-tuning, alongside the QJL and PolarQuant methods.
The AIMS collaboration between Google Research and several NHS organisations published two companion studies in Nature Cancer evaluating an AI breast cancer detection system within NHS screening workflows.