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#Google

All collected and published items, ordered by time. Picks are a selected subset.
First-party

Direct announcements from model vendors, companies and research institutions on their official sites, blogs and accounts.

8/27Thu
  1. Google DeepMind58

    Google DeepMind pilots the first global double-blind AI evaluation

    Google DeepMind says it has launched the first double-blind evaluation of proprietary frontier AI models, restricting external evaluators to an encrypted environment so models cannot encounter test questions in advance. The pilot, conducted with Singapore's AI Safety Institute, OpenMined, AVERI, and MLCommons, uses confidential benchmarks to test a Gemini Flash Lite model in a privacy-preserving environment and improve evaluation credibility.

  2. Google Research34

    Google introduces GlucoFM, a dual-stream foundation model for continuous glucose monitoring

    Google has released GlucoFM, a self-supervised foundation model with a dual-stream design that separates slow glucose trends from short-term fluctuations while preserving timing and missingness information. Across 14 evaluations spanning four cohorts and seven clinical tasks, it improved average PR-AUC by 5.8 percentage points over the best GluFormer variant and achieved the lowest MAE for predicting post-meal glucose response. It was pretrained on 109,066 hours of unlabeled CGM data and performed best at cross-dataset transfer and few-shot adaptation.

  3. Google DeepMind71

    Google DeepMind releases Gemini 3.5 Transcribe speech-to-text model

    Google DeepMind has released Gemini 3.5 Transcribe, which it calls its most accurate speech-to-text model yet. It converts raw audio directly into accurate, formatted text.

    Why it matters: The official post reports WER for streaming and non-streaming APIs and compares the model with the previous-generation Chirp 3, helping readers assess speech transcription in practical workflows.

8/26Wed
  1. Google Research26

    Google introduces AgentHands to generate spatial gestures for XR agent conversations

    Google has released AgentHands, a research prototype described in a paper at CHI 2026, that maps LLM reasoning output to gestures synchronized with speech in XR. This lets an agent point things out and demonstrate objects or steps in the user's physical space. The system comprises four modules: object registration, a gesture-event library, gesture-embedding inference, and synchronized execution in a headset. Gestures are categorized by dimensions including left or right hand, spatial anchoring, and VFX. In a controlled study with N=12, AgentHands significantly improved spatial reference over a voice-only baseline.

8/22Sat
  1. Google Research43

    Google introduces Biomarker Discovery Framework to identify candidate biomarkers from wearable-sensor data

    Google has released Biomarker Discovery Framework, a multi-agent system that organizes candidate biomarker prioritization into an iterative, human-supervised research loop combining hypothesis generation, parallel statistical analysis, model training, adversarial validation, and literature reasoning. Across 9,279 participant observations in three cohorts, it identified 41 candidate digital biomarkers for mental health and 25 for metabolic health. One example: variability in sleep duration correlated with PHQ-8 severity (ρ = 0.252).

8/21Fri
8/17Mon
  1. Google Research58

    Google Research introduces PhotoScan to estimate body composition from phone photos and predict insulin resistance

    Google Research has proposed PhotoScan, a deep-learning framework that estimates body-fat percentage, the android-to-gynoid fat ratio (A/G), and the visceral-to-subcutaneous fat area ratio (V/S) from ordinary 2D phone photos. It was pretrained on more than 35,000 UK Biobank records, fine-tuned on a cohort of 677 adults, and validated on an independent cohort of 132 people.

8/14Fri
8/12Wed
  1. Google DeepMind71

    Google DeepMind releases SL2T sign-language-to-text model, first rolling out in Gboard and Live Transcribe on Pixel 11

    Google DeepMind has released SL2T, a multilingual sign-language-to-text model, bringing sign-language AI to consumer products for the first time. On Pixel 11, Gboard and Live Transcribe support dictation from American Sign Language (ASL) to English; more devices and languages are planned.

    Why it matters: The source describes the capabilities and limits of a multilingual sign-to-text model and where it is available, helping readers assess sign-language translation in real products.

  2. Google Research60

    Google Research proposes knowledge profiling: retrieval, not encoding, is the bottleneck for frontier LLM factuality

    Google Research has proposed a knowledge-profiling framework that distinguishes factual errors caused by encoding failures from those caused by retrieval failures, and released WikiProfile, a benchmark containing 2,150 Wikipedia facts.

    Why it matters: By separating encoding failures from retrieval failures, knowledge profiling offers a new way to diagnose the sources of factual errors in frontier models.

  3. Google Research72

    Google researchers advance AMIE to expert-level clinical interviews with audio and video

    Google Research has released AMIE (Video), which uses Gemini and Project Astra for real-time video clinical interviews. It can perceive nonverbal cues, guide virtual physical examinations, and reason about diagnoses in real time.

    Why it matters: AMIE extends from text to live video consultations, showing how an asynchronous multi-agent architecture can balance conversational latency with clinical reasoning.

8/6Thu
  1. Google DeepMind76

    Google DeepMind open-sources WeatherNext cyclone forecasting model

    Google DeepMind has released WeatherNext, an AI model that achieves state-of-the-art forecasts of cyclone tracks, intensity, and wind-field structure, providing about one additional day of lead time on average—roughly a decade of progress in meteorology.

    Why it matters: The source reports measured gains of about one additional day of warning for cyclone tracks and intensity and opens the model weights and code.

7/31Fri
  1. Google Research62

    Google Research proposes Science One Framework for verifiable autonomous research with Chain-of-Evidence

    Google Research has proposed Chain-of-Evidence (CoE), a framework for verifiability implemented in the autonomous-research prototype Science One Framework and the automated auditing tool CoE Audit.

    Why it matters: The article reports a verifiability framework and audit metrics, helping readers compare autonomous research systems on citation and code consistency.

7/30Thu
  1. Google DeepMind71

    Google DeepMind releases Gemini Robotics ER 2 embodied-reasoning model

    Google DeepMind has released Gemini Robotics ER 2, positioned as the robot's high-level brain: it converses with people, understands the physical world, and plans multi-step tasks before handing execution to a lower-level VLA model. It can also call tools such as Google Search natively.

    Why it matters: The official release gives concrete figures for ER 2's progress classification and temporal localization, along with access details, helping readers assess its limits on long tasks.

  2. Google DeepMind58

    Google DeepMind releases Lyria 3.5 music-generation model in Flow Music

    Google DeepMind has released Lyria 3.5, its next-generation music-generation model, in Google Flow Music. The release improves musicianship, lyrics, vocals, and creative control: it produces richer, more natural melodic structures; improves lyric quality, prompt adherence, and structure awareness; offers more expressive vocals with finer emotional detail and better pronunciation; and makes tempo and duration easier to control.

7/28Tue
  1. Google DeepMind80

    Google DeepMind releases Gemini Robotics 2 for full-body robot intelligence

    Google DeepMind has released Gemini Robotics 2 with three models: Gemini Robotics 2, a vision-language-action model for translating visual and language input into motion; Gemini Robotics ER 2 for embodied reasoning; and Gemini Robotics On-Device 2, which runs locally on devices.

    Why it matters: The article explains the three models' roles and access points, helping readers assess pathways to full-body robot control and multi-robot collaboration.

7/23Thu
  1. Google Research62

    Google introduces SymptomAI, a conversational AI agent for everyday symptom assessment

    Google Research has published SymptomAI, a study in which five Gemini Flash 2.0-based conversational agents interviewed 13,917 participants and evaluated differential diagnoses (DDx).

    Why it matters: Google's randomized study compares five questioning strategies across 13,917 people and, for the first time, compares diagnoses with Fitbit physiological signals, offering evidence on real-world conversational triage.

  2. Google Research62

    Google Research proposes quantum computers that learn from their mistakes

    In a paper published in Nature, Google Research proposes using reinforcement learning to let autonomous agents learn from quantum error-correction detection events, continuously adjusting thousands of control parameters during computation to counter drift.

    Why it matters: Google trains a reinforcement-learning agent on quantum error-correction data to continuously calibrate control parameters without interrupting computation.

7/22Wed
7/21Tue
7/17Fri
  1. Google DeepMind71

    Google DeepMind releases Gemini 3.5 Flash Cyber security model

    Google DeepMind has released Gemini 3.5 Flash Cyber, a lightweight cybersecurity model fine-tuned from 3.5 Flash to quickly find, validate, and patch vulnerabilities. Across benchmarks such as CyberGym, it approaches the results of larger models with multiple calls.

    Why it matters: The source reports the lightweight security model's performance on benchmarks such as CyberGym and the terms of its limited pilot, helping readers assess its positioning and availability.

7/16Thu
7/13Mon
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.

7/8Wed
  1. Google Research36

    Google Research uses Google Maps routing interventions to ease urban congestion

    In the first large-scale real-world study of its kind, published in Nature Cities, Google Research modified the Google Maps algorithm to direct about 2% of trips to alternative routes across 10 U.S. cities for six months. Median travel speeds on target roads rose by about 2%, median fuel-consumption rates fell by 0.5% to 1.0%, and each city could cut thousands of tonnes of CO2e emissions annually.

7/3Fri
  1. Google DeepMind22

    Google DeepMind and A24 announce their first research partnership

    Google DeepMind and A24 have announced their first research-focused partnership, a long-term collaboration across several projects that will involve A24 and its filmmakers in shaping new technology. Google has also invested in A24. Initial work will focus on bridging frontier technology and next-generation entertainment, with specific goals and technical outputs to evolve over time.

7/1Wed
  1. Hugging Face Blog62

    Hugging Face and Cerebras bring Gemma 4 to real-time voice AI

    Hugging Face and Cerebras demonstrated a real-time speech-to-speech pipeline using Cerebras to accelerate Gemma 4 31B inference, bringing voice-interaction latency closer to natural conversation.

    Why it matters: The source presents a replaceable open-source speech-to-speech pipeline, helping readers assess engineering combinations for low-latency voice interaction.

6/30Tue
  1. Google Research76

    Google releases TabFM, a zero-shot foundation model for tabular data

    Google has released TabFM, a zero-shot foundation model for classification and regression on tabular data. It recasts table prediction as an in-context-learning problem, producing predictions in a single forward pass without manual training, tuning, or feature engineering.

    Why it matters: The source details TabFM's architecture and TabArena results, helping readers assess whether zero-shot table prediction can replace traditional tree-model workflows.

6/27Sat
  1. Google Research60

    Google speeds up Gemini Nano on Pixel with frozen multi-token prediction

    Google Research has introduced an architecture that attaches a multi-token-prediction (MTP) head to the frozen Gemini Nano v3 model, speeding up on-device generation without changing the backbone weights.

    Why it matters: Google attaches an MTP head to frozen Gemini Nano v3 and reports concrete methods and gains under on-device memory and energy constraints.

6/25Thu
  1. Google Research31

    Google lowers cloud costs with linear elastic caching: Spanner memory use drops 15.5%

    Google Research has proposed linear elastic caching, modeling page eviction as a ski-rental problem and using lightweight machine learning to adjust cache size dynamically. After months of testing in Spanner production, memory use fell 15.5%, cache misses rose just 5.5%, total cost of ownership fell about 5%, and the impact on actual I/O costs was only 0.5%.

  2. Google Research37

    Google Research studies how reasoning unlocks LLMs' parametric knowledge

    A paper from Google Research at COLM 2026 finds that reasoning traces can unlock parametric knowledge in LLMs that they could not otherwise access, even for simple single-hop factual questions. Tests on Gemini-2.5 Flash and Pro and Qwen3-32B identify two mechanisms: extra tokens act as a “computation buffer,” and “fact priming” builds a retrieval bridge by generating related facts.

  3. Google DeepMind74

    Google DeepMind integrates computer-use tools into Gemini 3.5 Flash

    Google DeepMind has integrated computer use as a built-in tool in Gemini 3.5 Flash. Previously, the capability was offered only through a separate Gemini 2.5 computer-use model.

    Why it matters: The source describes the path from a standalone model to computer use built into Gemini 3.5 Flash, helping readers assess how to connect agents to cross-platform automation.

6/17Wed
  2. Google Research30

    Google Earth AI releases vectorized dataset mapping hedgerows and woodlands in the British countryside

    Google Research has released a vectorized dataset that converts the high-resolution raster maps in Farmscapes 2020 into actionable inventories of hedgerows, stone walls, and scrubland, covering more than 130,000 square kilometers of the UK. The framework fine-tunes an RSF Vision Transformer backbone pretrained on over 300 million global satellite images and uses Polsby–Popper compactness scores to distinguish woodland, woody patches, and linear woody features.

6/16Tue
  1. Google DeepMind60

    Google DeepMind publishes AI Control Roadmap for agent safety through defense in depth

    Google DeepMind has published an AI Control Roadmap that treats internal AI agents as potentially misaligned and adds system-level safety layers beyond model alignment. It proposes grading safeguards by detection capabilities D1–D4 and prevention-and-response capabilities R1–R3.

    Why it matters: DeepMind has published its internal AI control roadmap, with graded detection and response capabilities and analysis of one million coding-agent trajectories.

6/13Sat
  1. Google Research32

    Google studies how AI can help people understand skin concerns

    Google reports on consumer understanding of dermatology AI tools. Among 2,345 participants, more than 62% of those using AI tried to name a skin condition, with 23% accuracy—nearly three times the control group's 8%. The “Wizard of Oz” group reached 36% accuracy. But the AI group showed no statistically significant improvement in deciding what care to seek and was slightly more likely than dermatologists to recommend less urgent care (30% vs. 27%).