Skip to content
K = 0.731
2026.10.17 · Sat

#DeepMind

#DeepMind

10/15Thu
  1. The Decoder81

    GPT-6-Astra solves 49 of 50 physics research problems at $2.40 apiece

    GPT-6-Astra, running in OpenAI's Codex environment without human hints, solved 49 of 50 TheoPhysEval problems across eleven theoretical physics subfields, costing just under $116 overall. Anthropic's Opus 5.5 scored 92 percent, while both models reached 92 percent without internet access. The researchers emphasize that the benchmark tests calculations using known methods, while choosing promising research questions remains a separate challenge for current agents.

    Why it matters: The benchmark distinguishes proficiency in concrete physics calculations from the ability to choose fruitful research questions, clarifying which parts of scientific work its high scores actually measure.

10/12Mon
  1. The Decoder81

    Google DeepMind’s AlphaProof Nexus solves dozens of open math problems in peer-reviewed study

    Google DeepMind’s AlphaProof Nexus solved dozens of open math problems, according to a peer-reviewed study in Science, including nine of 353 open Erdős problems, two unsolved for 56 years. It also proved 44 OEIS conjectures, solved an open algebraic geometry question, and improved a min-max optimization bound. Built around Gemini 3.1 Pro, it drafts and revises proofs in Lean using checker feedback and can call AlphaProof for harder sub-problems, though DeepMind reports it solved the nine Erdős problems without that tool.

    Why it matters: DeepMind’s use of peer review provides a concrete comparison with OpenAI’s publication of AI proofs on GitHub amid a debate over how AI mathematics should be evaluated.

9/30Wed
  1. Google DeepMind62

    Google DeepMind launches SynthID Bio to embed watermarks in AI-generated proteins

    Google DeepMind launched SynthID Bio, bringing watermarking to synthetic biology. It embeds imperceptible signatures directly into biological code so that generated content can be verified both in digital models and in synthesized physical proteins, while preserving their biological function.

    Why it matters: SynthID watermarking has expanded from images to synthetic biology, showing how AI-generated proteins can be marked and verified.

9/24Thu
  1. NVIDIA Blog60

    NVIDIA, Google DeepMind, and partners open predicted protein-complex structures for more than 2,800 viruses

    NVIDIA, Google DeepMind, EMBL-EBI, and other organizations have made predicted 3D structures of protein complexes from more than 2,800 viruses freely available to any scientist through the AlphaFold Database.

    Why it matters: NVIDIA, Google DeepMind, and collaborators have opened predicted protein-complex structures for more than 2,800 viruses, showing a concrete approach to open science for pandemic preparedness.

9/8Tue
  1. Demis Hassabis · X72

    DeepMind releases AlphaGenome Atlas to predict the effects of 9 billion single-letter DNA variants

    Google DeepMind has released AlphaGenome Atlas, an AI-powered searchable database that predicts the effects of all 9 billion possible single-letter DNA variants. Demis Hassabis says that after AlphaFold mapped the protein universe, this tool begins mapping the human genome to help scientists better understand disease. It is free for academic research at http://alphagenome.google/atlas.

    Why it matters: Following AlphaFold, DeepMind is extending predictive capabilities from proteins to the human genome; the article describes the database's coverage and open access.

7/9Thu
  1. Google Research62

    Google releases SensorFM, a large sensor foundation model for wearable-health data

    Google Research and Google DeepMind have introduced SensorFM, a large sensor foundation model that learns directly from unlabeled wearable data. Its pretraining corpus contains more than one trillion minutes of multimodal sensor signals from five million consenting participants across more than 100 countries, all 50 U.S. states, and over 20 Fitbit and Pixel Watch devices.

    Why it matters: The article reports pretraining-data scale, scaling experiments, and downstream-task comparisons, helping readers assess how generalizable wearable foundation models are.

5/16Sat
  1. Google DeepMind66

    How WeatherNext helped the U.S. National Hurricane Center forecast Hurricane Melissa's landfall in Jamaica

    WeatherNext, developed by Google DeepMind and Google Research, helped the U.S. National Hurricane Center forecast five days in advance that Hurricane Melissa would make landfall in Jamaica as a Category 5 storm, with 80% confidence. Three days before landfall, confidence rose to nearly 100%.

    Why it matters: The article reports WeatherNext's real-world performance on both hurricane track and intensity forecasts, showing how AI weather models are used for extreme events.

5/12Tue
  1. Google DeepMind71

    Google DeepMind releases Co-Scientist multi-agent research system

    Google DeepMind has published research on Co-Scientist in Nature. Built on Gemini, it is a multi-agent AI system that iteratively generates, debates, and evolves scientific hypotheses.

    Why it matters: The article describes the multi-agent system's three-stage architecture and deployments in several labs, explaining how AI contributes to scientific hypothesis generation.

You've reached the end