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
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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
Improving the academic workflow: Introducing two AI agents for better figures and peer review
Generative AI
Building better AI benchmarks: How many raters are enough?
Algorithms & Theory
TurboQuant: Redefining AI efficiency with extreme compression
Algorithms & Theory
Mapping the modern world: How S2Vec learns the language of our cities
Algorithms & Theory
Improving breast cancer screening workflows with machine learning
Health & Bioscience
Identifying Interactions at Scale for LLMs
Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems through different lenses: feature attribution, which isolates the specific input features driving a prediction (Lundberg & Lee, 2017; Ribeiro et al., 2022); data attribution, which links model behaviors to influential training examples (Koh & Liang, 2017; Ilyas et al., 2022); and mechanistic interpretability, which dissects the functions of internal components (Conmy et al., 2023; Sharkey et al.
Protecting cities with AI-driven flash flood forecasting
Climate & Sustainability
Exploring the feasibility of conversational diagnostic AI in a real-world clinical study
Generative AI
WAXAL: A large-scale open resource for African language speech technology
Natural Language Processing