Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler
Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler
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Google launches zero-trust, aggregated private analytics technology, integrating cryptography with a trusted execution environment.
Google’s research team has unveiled a new private analytics solution that leverages an innovative single-round lattice-based cryptographic aggregation protocol, working in tandem with a Trusted Execution Environment (TEE), to enable data aggregation without relying on any single trusted entity. This approach ensures that user data remains securely stored on the device while delivering group-level anonymized insights.
Physics AI research that’s shaping the industry.
Published breakthroughs pushing the state of the art.
Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRL
Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRL
Reachy Mini goes fully local
Reachy Mini goes fully local
Harness, Scaffold, and the AI Agent Terms Worth Getting Right
Harness, Scaffold, and the AI Agent Terms Worth Getting Right
Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery
General Science
OlmoEarth v1.1: A more efficient family of Earth observation models
OlmoEarth v1.1: A more efficient family of Earth observation models
Introducing the Ettin Reranker Family
Introducing the Ettin Reranker Family
PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend
PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend
Opening new paths in aging research
Calico Life Sciences uses Co-Scientist to connect scattered findings and generate new leads in aging research.
Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality
Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality
Unlocking asynchronicity in continuous batching
Unlocking asynchronicity in continuous batching
Catalyzing scientific impact through global partnerships and open resources
Data Mining & Modeling
Four ways Google Research scientists have been using Empirical Research Assistance
Data Mining & Modeling
Gradient-based Planning for World Models at Longer Horizons
GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across time, (2) adding stochasticity directly to the state iterates for exploration, and (3) reshaping gradients so actions get clean signals while we avoid brittle “state-input” gradients through high-dimensional vision models. Large, learned world models are becoming increasingly capable. They can predict long sequences of future observations in high-dimensional visual spaces and generalize across tasks in ways that were difficult to imagine a few years ago. As these models scale, they start to look less like task-specific predictors and more like general-purpose simulators. But having a powe