Google Research proposed MoGen (Neuronal Morphology Generation), using PointInfinity point-cloud flow matching to generate synthetic neuronal shapes and supplement PATHFINDER reconstruction training. It reduced reconstruction error by 4.4% on held-out mouse axons, mainly by reducing merge errors.
Google released Vantage, a research experiment using generative AI simulated conversations to assess future skills such as problem-solving and collaboration in school and university students, with English registration open on Google Labs. An Executive LLM dynamically guides dialogue and an AI Evaluator scores against rubrics. A study with New York University involving 188 US participants aged 18–25 found AI–expert scoring agreement close to agreement between two human experts.
Google Research released ConvApparel, with over 4,000 human–machine multi-turn clothing-shopping conversations, nearly 15,000 turns, to quantify realism gaps between LLM user simulators and real people.
Google released PaperVizAgent for paper illustrations and ScholarPeer for automated peer review. PaperVizAgent combines retrieval, planning, style, visualisation and review agents, scoring 60.2 on a 0–100 evaluation, ahead of GPT-Image-1.5, Nano-Banana-Pro and Paper2Any. It was the only framework above the 50.0 human baseline.
Google Research proposed situational judgement tests (SJT) adapted from psychological questionnaires such as IRI and ERQ to evaluate alignment between LLM behaviours and human consensus in real user–assistant scenarios.
Google Research studied trade-offs between evaluation item counts and annotators per item in Forest vs Tree: The (N, K) Trade-off in Reproducible ML Evaluation. Common counts of one, three or five annotators are often insufficient; reflecting human disagreement frequently requires more than ten.
Google Quantum AI published a white paper saying future quantum computers would require fewer qubits and gates than previously estimated to break the elliptic-curve cryptography protecting cryptocurrencies.
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.
S2Vec uses S2 Geometry partitions and rasterised features to turn buildings and roads into multilayer images, learning general embeddings with masked autoencoding (MAE). It beats SATCLIP and GEOCLIP on zero-shot geographic socioeconomic extrapolation such as US population density and median income, but needs improvement on tree cover and elevation.
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.
Google and Cornell published a PNAS study asking GPT-4o, Perplexity, Claude 3.5, Gemini Advanced Pro 1.5, NotebookLM and a custom RAG system 67 expert superconductivity questions. Twelve international experts blindly assessed balance, comprehensiveness, concision, evidence, image relevance and qualitative feedback.
Berkeley AI Research proposed SPEX and ProxySPEX to identify key interactions driving LLM outputs at scale in feature, data and model-component attribution. SPEX turns interaction search into sparse recovery using sparsity and low order; ProxySPEX exploits hierarchy to match SPEX with roughly ten times fewer ablations.
Google Research launched Groundsource, using Gemini to extract structured historical disaster data from global news. Its first open dataset contains 2.6 million urban flash-flood records across over 150 countries, spanning 2000 to the present.
Google Research and Beth Israel Deaconess Medical Center conducted a prospective, single-centre feasibility study in which AMIE held text consultations with patients before outpatient primary care visits, under live physician supervision by video.
Berkeley AI Research proposed a mutual-information framework to evaluate and optimise imaging from noisy measurements and noise models. A NeurIPS 2025 paper validates decoder performance predictions for colour photography, radio astronomy, lensless imaging and microscopy. Designs match end-to-end state-of-the-art approaches with less memory and compute, without task-specific decoder design.
Berkeley AI Research proposed a divide-and-conquer off-policy reinforcement learning algorithm without temporal-difference (TD) learning. It reduces Bellman recursions from linear to logarithmic counts, scaling to long-horizon tasks.
Berkeley AI researchers proposed the first theory quantitatively predicting word2vec learning. Under realistic training conditions, it reduces to unweighted least-squares matrix factorisation with a closed-form gradient-flow solution, and the resulting representation is equivalent to PCA of target matrix M*. From small initialisation, word2vec learns orthogonal linear subspaces in discrete steps, increasing embedding rank and reducing loss. These features are M*'s leading eigenvectors and can be calculated in advance from corpus statistics and hyperparameters.