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#DeepMind

All collected and published items, ordered by time. Picks are a selected subset.
Research

Research papers, technical reports, benchmarks and datasets.

10/16Fri
  1. 雷锋网72

    Nick Cammarata Challenges Superposition Explanations in Neural Network Interpretability

    Nick Cammarata’s Neural Networks Are Already Clean argues that improved observation tools reveal coherent neuron functions, reporting clear interpretations for 99%–100% of final-layer neurons in tested modern vision models. He reinterprets classic examples of apparently unrelated features as geometric detection or recursive generalization. Neel Nanda counters that these findings do not resolve representational superposition in language models and defends SAE applications. Cammarata describes the proposal as a working hypothesis, acknowledging gaps in language-model training evidence, multimodal coverage, and causal validation.

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.

10/1Thu
  1. Demis Hassabis · X71

    DeepMind publishes SynthID Bio in Nature, watermarking AI-designed proteins and open-sourcing the tool

    Google DeepMind published research in Nature proposing SynthID Bio, a protein-watermarking method, and successfully synthesized AI-designed proteins that both function and carry watermarks. Demis Hassabis said bringing SynthID into biology is a key step in addressing biosecurity challenges in the AI era. The team open-sourced SynthID Bio tools for the research community to build on.

    Why it matters: SynthID has expanded from image watermarking to protein design, with a paper and open-source tools released together, offering a technical path for biosecurity governance.

9/7Mon
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.

6/22Mon
  1. Import AI62

    Oxford and partner study finds AI's persuasive writing consistently beats human experts, with 10.8 percentage points more real donations

    Researchers at Oxford, the UK's AI Safety Institute, Stanford, and the London School of Economics ran four experiments with 6,923 participants and 18,978 conversations. They found that AI systems consistently outperformed human experts at text-based persuasion, even when experts chose their own topics, researched in advance, and were offered a £1,000 prize incentive.