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

Research papers, technical reports, benchmarks and datasets.

Today10/17Sat
  1. Google Research76

    Grounding AI agents in biological reality: Building blocks for autonomous wet labs

    Google Research presents three projects connecting AI agents with wet-lab experimentation through VERID laboratory automation, closed-loop aptamer discovery, and de novo protein binder design. Across three rounds, aptamer on-target hit rates reached 33.5% for myoglobin and 42.1% for α-thrombin; the protein design pipeline accelerated computational cycles more than eightfold and produced binders with single-digit nanomolar affinity. Integrating these projects remains a next step, with safeguards including sequence screening, simulation before robot execution, and human authorization and supervision.

    Why it matters: The aptamer experiments illustrate how experimental feedback can expose misleading optimization targets, offering a transferable approach to distinguishing genuine biological improvements from artifacts in automated discovery workflows.

  2. Google DeepMind · X72

    Four Gemini 4 Argon agents beat Minecraft from scratch in 27 hours

    Google DeepMind reports that four Gemini 4 Argon agents beat Minecraft from scratch in 27 hours. Operating in a sandbox using only code and text, they wrote 2,783 programs, divided up jobs over chat, shared notes, and debugged errors together, all with no human help. https://t.co/uyIhDOQXGT

    Why it matters: The account offers a concrete example of agent coordination through code, chat, shared notes, and collaborative debugging during a task lasting 27 hours.

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.

  2. The Decoder62

    Google says self-improving AI isn't anywhere close

    Google engineer Haydn Belfield says Google DeepMind has seen no measurable AI-driven acceleration of AI research despite substantial automation. AI writes more than 75 percent of production code, but every change receives human review before submission; Argon completes about half of RE-Bench's 74 ML engineering tasks. Google's General Capability Index shows steady Gemini capability growth without statistically significant acceleration, contrasting with researchers' warnings of a possible intelligence explosion.

  3. Google Research78

    Google evaluates Earth AI geospatial agents and expands experimental access

    Google released a technical report evaluating its experimental Geospatial Reasoning agent across four benchmarks and is expanding early access through Ask Google Earth and an Agent-to-Agent API. Evaluations using Gemini 3.8 Flash and Gemini 3.1 Pro found that specialized Earth AI tools improved spatial reasoning, while upfront planning helped with open-ended workflows. Google is releasing development splits for GeoKTools and GeoExpertQueries, but cautions that GeoExpertQueries evaluates adherence to expert workflows rather than final-answer correctness, making agent outputs drafts for expert review.

10/14Wed
  1. Ars Technica · AI74

    Researchers report jailbreak vulnerabilities in OpenAI and Google image models

    White Circle researchers report that their RISE attack method bypassed safeguards in DALL·E 3, Nano Banana 2, and GPT-Image-2 to generate explicit images about 13 percent of the time. The method uses widely available models to iteratively develop and evaluate jailbreak strategies. White Circle told Ars Technica that subsequent testing found ChatGPT Images 2.5 and Nano Banana 2.1 significantly more vulnerable than their predecessors; Google and OpenAI did not respond to requests for comment.

10/13Tue
  1. Demis Hassabis · X76

    Demis Hassabis welcomes AMIE’s clinical study published in The Lancet

    Amazing to see research move from the lab to real-world clinical utility—congratulations to the team on this milestone and The Lancet paper! https://twitter.com/sundarpichai/status/2108329198955958339 In the quoted post, Sundar Pichai describes Google’s prospective AMIE study with @BIDMC_Medicine involving real urgent care patients. Clinicians reported that its medical-history summaries helped prepare for visits in 75% of cases and influenced care approaches in more than half, while its differential diagnoses matched doctors’ final diagnoses 90% of the time.

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.

  2. 量子位75

    Huawei and UCL’s Memento 3 Clears 25 ARC-AGI-3 Games Without Updating Model Weights

    Huawei’s Noah’s Ark Lab, UCL, and collaborators report that Memento 3 cleared all 25 public ARC-AGI-3 games and 183 levels using 7518 actions, about 56% fewer than humans, achieving a 100 RHAE score. Built on Claude Opus 5 without weight updates or game-specific fine-tuning, the agent iteratively revises a Markdown rulebook and executable Python world model. In Atari Pong, its learned code-based strategy won three games 21:0 without further LLM calls; the experiments primarily cover game environments with relatively clear rules.

10/9Fri
  1. 量子位62

    Google and BIDMC study appears in The Lancet: AI chatbot AMIE used for pre-visit assessment

    A study led by researchers from Google and Beth Israel Deaconess Medical Center (BIDMC) appeared in The Lancet’s flagship journal, marking the first time Google research has been published there. In a real-world clinical study at BIDMC’s outpatient primary-care clinic, 98 patients used Google’s research diagnostic AI chatbot AMIE for pre-visit assessment before their emergency visits. Doctors monitored every interaction in real time, and none had to be interrupted under the predefined safety criteria.

  2. Sundar Pichai · X66

    Google and BIDMC publish AMIE conversational diagnostics study in The Lancet

    Google and Beth Israel Deaconess Medical Center published an AMIE study in The Lancet, described by Sundar Pichai as the first prospective study of patient-facing conversational diagnostics in a real clinic. In urgent care testing, clinicians said AMIE's summaries helped them prepare for visits in 75% of cases and influenced their approach to care in more than half; its differential diagnoses matched doctors' final diagnoses 90% of the time.

    Why it matters: A prospective clinic study of a patient-facing conversational diagnostic system, with clinician-reported preparation and diagnosis-agreement figures from real urgent care visits.

10/8Thu
  1. Google Research62

    Google Research's three-month patent-writing experiment found AI boosted output but did not accelerate junior lawyers' independent judgment

    Google Research published a three-month field experiment on patent drafting through NBER. It randomly gave 133 lawyers at 11 intellectual-property law firms access to an unpublished Google Labs AI patent-writing assistant, now part of Gemini Notebook; two-thirds received early access.

    Why it matters: The three-month patent-writing experiment separates AI's immediate productivity gains from changes in unaided judgment, providing comparative evidence on AI's effect on professional skills.

10/7Wed
  1. Latent Space88

    OpenAI's internal model releases 722 math papers, claiming to solve 90 of 500 open problems

    OpenAI published mathematical results from an unreleased internal frontier model through a blog, GitHub repository, and posts. The release includes 722 manuscripts grouped into 372 related result families, drawn from an evaluation of about 4,000 research questions. Each result used an average of about three hours of ChatGPT Pro reasoning compute.

    Why it matters: OpenAI is producing mathematical results in bulk with an internal model, giving readers a view of the scale, compute cost, and outside criticism surrounding the work.

10/6Tue
  2. The Decoder62

    Researchers extend LeCun's JEPA into JEPA-Anything, a world model spanning physical and biological domains

    Researchers at PhAI Labs, Stanford, Oxford, and Princeton proposed JEPA-Anything. It splits JEPA's predicted state into four orthogonal factors, assigns a prediction module to each, and recombines them. It outperformed standard JEPA with the same architecture and data on ten test tasks in physics, robotics, weather, and other areas.

  3. Google Research31

    Google publishes workshop report on open questions in agent privacy and security

    Google published the workshop report Open and Emerging Issues in Agentic Privacy and Security: A Contextual Perspective, compiled by more than 50 academics and industry participants at a Google CAPS workshop in New York in late 2025. Based on Contextual Integrity (CI) theory, the report proposes a context-policy engine as an oversight layer to assess whether information flows are appropriate before data leaves a user's workspace. It addresses prompt injection, probabilistic control flow, and autonomous delegation at three levels: system-level sandboxing, model-level reasoning, and user control.

  4. Google AI · X62

    Gemma 4 and BOTANIC-1 rank plant mutations in minutes

    Google AI highlights work by AI research lab Living Models, which paired Gemma 4 with BOTANIC-1, a model trained on plant DNA from 320 species, to identify mutations that alter crop traits. In a melon yield gene test, the pair ranked the target mutation first out of 2,494 possibilities in under four minutes, versus years of physical breeding trials. A demo and paper are linked.

10/4Sun
10/2Fri
  1. Google Research62

    Google releases new TEE-based federated learning system, now used by Gboard

    Google Research released a new federated learning system that uses trusted execution environments (TEEs) to provide verifiable, auditable guarantees for data anonymization. Gboard has deployed it for next-word prediction in English and Japanese, with stronger privacy guarantees and improved accuracy.

    Why it matters: Google described a new TEE-based federated learning system and its deployment in Gboard, showing an engineering path for privacy-preserving on-device training.

10/1Thu
  2. 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.

9/30Wed
  1. Google Research42

    Google Research introduces Diffusion Controller to unify and simplify AI image-generation control

    Google Research introduced Diffusion Controller, a framework that recasts the denoising process in diffusion models as a continuous control problem. A lightweight “steering damper” network dynamically adjusts the generation trajectory while keeping the base model frozen. On Stable Diffusion v1.4, its fully unlocked version achieved a 90% win rate against the baseline on the HPS-v2 evaluation. It also supports custom control of closed-source models whose internal weights are unavailable.

9/25Fri
  1. Google Research65

    Google Research introduces AI video co-director framework for coherent long-form video generation

    Google Research has introduced an AI video co-director multi-agent framework, an orchestration layer on top of Gemini and Veo. It models long-form video generation as a problem of global optimization and world-state tracking to mitigate semantic drift and cascading failures.

    Why it matters: Google breaks long-video generation into four composable frameworks and reports continuity improvements across several benchmarks, illustrating how multi-agent orchestration can reduce semantic drift.

9/19Sat
9/18Fri
  1. Google Research60

    Google Research uses generative UI to help teachers create interactive learning simulations

    Google Research has published a research experiment in which teachers use generative UI (GenUI) to dynamically create interactive, level-based learning simulations for STEM classes, complete with scaffolding such as hints, feedback, and solution steps.

    Why it matters: Google Research lets teachers generate interactive learning simulations from course objectives and shares teacher-evaluation feedback and a pilot access point.

9/16Wed
  1. Google Research36

    Google Research proposes Retrieve-for-Train: compile diffusion models with RL to bypass inference bottlenecks in complex AI search

    Google Research proposes Retrieve-for-Train, a framework that uses offline RL to discover reward-aligned query fan-outs and compile them into supervision, then distills them into a 53.9M-parameter diffusion retriever. At inference, it generates a complete result set in one non-autoregressive pass without chain-of-thought tokens. The method fine-tunes fan-out language models based on Gemma3-4B and Qwen3-4B, using set-level attribute rewards to address paraphrase collapse in zero-shot LLMs and autoregressive latency.

9/11Fri
9/8Tue
  1. Google DeepMind71

    Google DeepMind's AlphaGenome Atlas covers predictions for 9 billion single-letter variants in the human genome

    Google DeepMind has released AlphaGenome Atlas, a platform containing predicted effects for 9 billion single-nucleotide variants in the human genome. At 1 PB, it is more than 30 times the size of the AlphaFold Database. From today, academic researchers can access it free through the web portal, AlphaGenome API, and a skill in Google Antigravity.

    Why it matters: The article gives the scale of predicted effects for 900 million single-nucleotide variants and free academic access points, helping readers assess the availability of genome-interpretation resources.

9/7Mon
9/4Fri
  1. Google Research62

    Google, HHMI Janelia, and partners publish a complete male fruit-fly brain connectome

    In a paper published in Cell, Google, HHMI Janelia, Cambridge, and collaborators have published a connectome of the complete male fruit-fly brain and central nervous system. It contains more than 166,000 neurons and 125 million synaptic connections, making it the largest brain map to date by neuron count.

    Why it matters: Google, HHMI Janelia, and partners have released the largest brain map by neuron count, showing a concrete application of AI in connectomics.

9/2Wed
8/28Fri
  1. Google Research62

    Google releases Planetary Prediction Engine to automate the full geospatial modeling workflow

    Google Research has introduced the experimental Planetary Prediction Engine (PPE) under Google Earth AI. From a natural-language query, it can autonomously discover geospatial data, engineer features, train and evaluate models, and produce a report—compressing weeks of manual data engineering into minutes.

    Why it matters: The article reports quantified improvements for PPE in public health, food security, and epidemic forecasting, giving a view of the practical limits of geospatial modeling automation.

8/27Thu
  1. 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.

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
  2. 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.