Google has released TurboQuant, a compression algorithm that quantizes KV cache to 3 bits without training or fine-tuning and without loss of model accuracy, reducing KV-memory use by at least 6×.
Why it matters: Google reports TurboQuant's algorithm details and benchmark results, helping readers assess the usefulness of KV-cache compression for long-context workloads.
Google Research has released S2Vec, a self-supervised framework that converts the built environment into layered images through S2 Geometry tiling and feature rasterization, then learns general-purpose embeddings with a masked autoencoder (MAE). On zero-shot geographic extrapolation for U.S.-wide socioeconomic predictions such as population density and median income, S2Vec is usually the best single model. Multimodal fusion with image embeddings generally performs better than either modality alone, though tasks such as tree canopy and elevation remain challenging.
Two companion studies in Nature Cancer evaluate an AI breast-screening system in the AIMS research collaboration between Google Research and several NHS organizations.
Why it matters: The two Nature Cancer studies report detection-rate and workload results for AI in the NHS double-reading workflow, showing the practical limits of human–AI collaboration.
Google and Cornell University tested GPT-4o, Perplexity, Claude 3.5, Gemini Advanced Pro 1.5, NotebookLM, and a custom RAG system on 67 expert-level questions about high-temperature superconductivity. An international panel of experts scored the answers blind across six dimensions.
Berkeley AI Research has proposed SPEX and ProxySPEX, algorithms that identify important interactions driving LLM outputs across feature, data, and model-component attribution. SPEX turns interaction search into a sparse-recovery problem by exploiting sparsity and low-order structure; ProxySPEX uses additional hierarchy to match SPEX performance with about ten times fewer ablation experiments.
Google Research and Beth Israel Deaconess Medical Center conducted a prospective, single-center feasibility study in which AMIE interviewed patients by text before outpatient primary-care visits, under video supervision by a physician throughout.
Why it matters: Google and BIDMC have deployed conversational diagnostic AI in a real outpatient workflow for the first time, showing its safety-supervision model and diagnostic performance.
A Berkeley AI Research team proposes a mutual-information-based framework for evaluating and optimizing imaging systems. It estimates information directly from noisy measurements to predict system performance. Validated in color photography, radio astronomy, lensless imaging, and microscopy, optimized designs reach end-to-end-method performance with lower memory and compute needs and no task-specific decoder. The paper appeared at NeurIPS 2025.
Berkeley AI Research has proposed a divide-and-conquer off-policy reinforcement-learning algorithm that does not rely on temporal-difference (TD) learning. It reduces the number of Bellman-recursion steps from linear to logarithmic, enabling scaling to long-horizon tasks. The method achieves scaling for the first time in goal-conditioned RL, using the triangle inequality for shortest-path distances between states to construct transitive Bellman updates. Selecting the optimal subgoal w remains an open problem.
Berkeley AI Research proves that under a fourth-order approximate objective, specific hyperparameter constraints, tiny initial embedding weights, and tiny gradient-descent steps, word2vec's learning problem reduces to an unweighted least-squares matrix factorization. Its gradient-flow dynamics have a closed-form solution, and the resulting representations are equivalent to applying PCA to matrix M*.
