#Google

AIIn an urgent care study, physicians rated advice from the older Gemini 2.5 Pro and Gemini 2.5 Flash, which lacked access to patient medical records, as similar in quality to doctors' advice. No safety issues were identified. The author notes that models have improved significantly since.

AIGoogle published a prospective study of AMIE, a research conversational system that patients chat with before doctor appointments, in The Lancet with Beth Israel Deaconess Medical Center. Clinicians reported the summaries helped them prepare for visits in 75% of cases and influenced their approach to care in more than half. AMIE's differential diagnoses matched the doctors' final diagnoses 90% of the time.

Why it matters: The study tests a patient-facing diagnostic chat system in a real urgent care clinic, a setting that goes beyond lab evaluation and is useful for judging clinical readiness.

AIA clinical trial found that nearly 100 patients discussed their symptoms with Google's AMIE chatbot before meeting physicians. The source text provides no further details on the trial's results or outcomes.

AIGoogle Research reports results from evaluating its research medical system AMIE in a real clinic with BIDMC, published in The Lancet. Across 100 patient interactions, AMIE recorded zero safety stops and matched doctor diagnoses in 90% of cases.

AIGoogle, in partnership with BIDMC Medicine, conducted a study demonstrating technology's potential to strengthen the relationship between doctors and patients. The post provides a link for more details but includes no further findings or figures.

AIGoogle reports that its medical research system AMIE, described as the first patient-facing conversational diagnostic tool of its kind studied prospectively in a real-world clinical setting, was evaluated in a study published in The Lancet. Patients who chatted with AMIE before in-person appointments reported stronger confidence and better organized thoughts. Physicians reviewing the pre-visit conversation information gained more time for collaborative care and shared decision-making instead of digging through data.

AIGoogle Research is presenting ContinuousBench, a standardized benchmark for measuring knowledge transfer and data contamination in differentially private (DP) synthetic data generation. Alex Bie is giving an encore presentation at the Google booth #107 at COLM 2026 today, at 2:00 PM PT.

AIGoogle proposed FlowAgent, a ReAct-style agent that generates and validates fixes for pre-submit test failures and shows them in its code review tools. Two abstention filters, before and after execution, suppress weak suggestions; in a manual review of 195 real failures, 67.18% of fixes were correct. After the Google-wide launch, it suggested fixes on 295,508 changes, with developers previewing 65,069 and applying 28,554.

AIArtificial Analysis compared six hallucination checkers on the same deliverables from 20 tasks across eight models. GPT-6 Sol and GPT-6 Luna generally flagged the most material hallucinations, while Claude Sonnet 5.5 and Gemini 3.8 Flash flagged far fewer, with Claude Opus 5.5 falling between Grok 4.7 and Sonnet. The counts reflect checker behavior rather than establishing accuracy or ruling out self-preference.

AIGoogle Research is presenting EnvHarness, a flexible framework that enables co-evolution between LLM agents and their training environments, at the #COLM2026 Google booth #107 today at 11:00 AM PT. The post notes that static environments limit agent growth, and EnvHarness is described as a plug-in architecture that dynamically reshapes environment behaviors to improve reinforcement learning and adaptability.

AIGoogle shared research on what its work could mean for the future of global maternal healthcare. The post links to a fuller write-up but gives no specific findings, models, or figures.

AIIn a prospective clinical study, Google's models pinpointed gestational age within 4 days of accuracy. The company says that precision could meaningfully affect clinical care, and that extending such tools to low-resource settings could help reduce maternal deaths and close care gaps worldwide.

AILarge-scale AI training is spreading across multiple datacenters, with Google, Microsoft, AWS, Meta, and CoreWeave cited as examples. Because synchronized GPU clusters must exchange data in bursts, inter-site links can become a bottleneck, which Cisco estimates may require aggregate bandwidth about 14x a conventional DCI baseline.

AIHarvey LAB-AA v1.1 adds hallucination checks that audit every model deliverable against task source documents, with material hallucinations zeroing a task's score. GPT-6 Astra averaged 0.03 material hallucinations per task across 120 tasks, while Gemini 3.8 Flash averaged 13.96. Harvey uses GPT-6 Sol (high) as the hallucination checker, separate from its three-judge rubric panel.

AIA NVIDIA study accepted at NeurIPS 2026 reports that multimodal models refuse harmful requests less reliably when they call tools. Refusal failures rise by up to 68.7% relative and by 17.7% on average across the models tested, including Claude Opus 4.6 and 4.7 and Gemini Agentic Vision. The authors attribute this to tool outputs crowding out the original harmful intent and to attention shifting toward describing tool results. Re-inserting the original request and image before the final response restores part of the lost refusals.

AIEpoch gave six models 11 real work tasks from its own operations, including graphic design, data insights, and research design, and graded outputs against employee standards. Fable 5.1 and GPT-6 Astra led on average task performance, reliably handling well-defined work such as coding and computational analysis. The report finds that all models still fail on open-ended judgment, including matching Epoch's standards, designing informative experiments, and generating diverse ideas, so the authors conclude AI cannot yet replace workers at Epoch.

Why it matters: The report separates well-defined task reliability from open-ended judgment failures, which benchmark scores on easily verifiable tasks would miss.

AIGoogle Research is hosting a walkthrough at its COLM booth #107 today at 5:00 PM of ContinuousBench, a standardized benchmark for measuring knowledge transfer in differentially private synthetic data. The session, led by Alex Bie, asks whether DP synthetic data preserve actual information or only style. A paper is linked on arXiv.

AIGoogle Research announced findings from a three-month randomized controlled trial with practicing patent attorneys, led by Visiting Fellow David Autor. The study examines both short-term productivity gains and longer-term skill building resulting from AI usage.

AIA Google Research field experiment with 133 patent lawyers found AI tool access raised drafting scores by 0.34 to 0.38 standard deviations over three months. When the tool was removed for a redlining task, only senior lawyers kept an advantage of 0.45 SD, while junior lawyers showed no discernible improvement. The authors argue that tools which boost current output must not stop junior professionals from building the judgment that senior experts rely on.

Why it matters: The field experiment separates AI's short-term productivity gains from skill retained after the tool is removed, which matters for training junior professionals.

AIGoogle Research is presenting EnvHarness at the #COLM2026 Google booth (#107) at 2:00 PM today, with Zifeng Wang leading the session. EnvHarness is a plug-in architecture that dynamically reshapes environment behaviors to improve reinforcement learning and agent adaptability, addressing limits of static training setups for LLM agents.

AIGoogle describes new research intended to help researchers and public health officials complement existing health information and bridge reporting gaps. The stated goal is to address health challenges proactively. The post provides no further details on the specific research, methods, or results.

AIGoogle Earth AI, according to new research, can help communities respond to public health crises more quickly and proactively. The post says it combines behavioral trends, geospatial AI models, and other insights beyond simple statistics to help public health teams understand complex issues and bridge reporting gaps. The aim is to shift emergency response from reactive management toward proactive prevention.

AIGoogle Research will demo ScientistTwo, an autonomous multi-agent framework, at the COLM booth (#107) today at 2:00 PM PT. The framework analyzes research papers, identifies their limitations, and produces verified codebases. The accompanying paper is available on arXiv (2609.19644).

AIGoogle researchers, working with Northwestern Medicine and Jacaranda Health, trained healthcare workers to perform "blind sweep" ultrasounds analyzed by machine learning models. The models estimated gestational age and fetal presentation as accurately as a trained sonographer in a study of 1,000 mothers each in Nairobi and Chicago. The AI processes results on the device, so it needs no electricity supply or Wi-Fi.

AIGoogle Research shares results from five partner-driven case studies showing how Google Earth AI's Population Dynamics Foundation Model addresses public health data gaps. The post frames the work as empowering global health research, with details available in the linked blog.

AIGoogle Research reports that Population Dynamics Foundation Model (PDFM) embeddings, built from search trends, mobility, built environment, and weather signals, were tested by partners across five public health tasks. The embeddings improved results in cross-border MMR vaccination coverage, dengue forecasting, postpartum depression screening, and cholera outbreak prediction, and matched census inputs for cardiovascular mortality nowcasting.

AIGoogle Research released a workshop report, "Open and Emergent Problems in Agentic Privacy and Security: A Contextual Angle," compiled by more than 50 academic and industry leaders from the Google Contextual Agent Privacy and Security (CAPS) Workshop held in late 2025 in New York City.

AIGoogle announces a next-generation federated learning system that uses Trusted Execution Environments to provide verifiable, auditable data anonymization. The system publishes access policies to a public transparency log and is deployed in Gboard, which has launched English and Japanese next-word prediction models with stronger privacy guarantees and improved accuracy. Training time has also sped up significantly because computation moved to the server and is parallelized across many machines.

Why it matters: The post shows how Trusted Execution Environments make federated learning's privacy claims externally verifiable, rather than relying on trust in the server operator.

AIGoogle DeepMind reports that AI-designed proteins can be synthesized and watermarked using its new SynthID Bio method, published in Nature. The team says the work is a step toward biosecurity in AI-driven biology and is open-sourcing the SynthID Bio tools for the research community.

AIGoogle Developers Blog describes how Sparse VideoGen (SVG) routes video diffusion attention heads into spatial or temporal sparse masks and implements them as custom JAX and Pallas Splash Attention kernels on TPU v6e. In isolated single-chip tests with 75.6K tokens and 10 heads, the sparse variants retain about 38.87% of query-key pairs. The article argues that theoretical sparsity must be converted into hardware tile skipping to yield real speedups.

AIGoogle Research has introduced Diffusion Controller, a lightweight steering damper that precisely guides image generation to improve prompt alignment. The post says it improves image quality while preserving baseline stability, with details available in the linked blog.

AIGoogle Research introduced Diffusion Controller, a framework that treats image generation as a continuous control problem rather than separate inference-time guidance and fine-tuning fixes. Its lightweight add-on "steering damper" network keeps the base model frozen and works on black-box or gray-box models, and it outperformed the industry standard on human preference matching. In a Stable Diffusion v1.4 test, the fully unlocked version achieved a 90% win rate over the baseline.

AIGoogle Research announced a new unified multi-agent framework that helps creators generate temporally consistent long-form video narratives. It is designed to reduce visual drift and the propagation of errors across the production pipeline.

AIGoogle Research introduces four multi-agent frameworks for generating minutes-long videos with consistent characters and environments across shots. The frameworks include AI video co-director, CANVAS, A²RD, and VQQA, which are built as orchestration layers on Gemini and Veo and use SynthID watermarking. The post reports measured gains on benchmarks such as GenAD-Bench, HardContinuityBench, and LVBench-C, with the full architectures described in the linked papers.

Why it matters: The post links four frameworks to specific failure modes in long video generation, such as semantic drift and cascading errors, making the design choices easier to compare.

AIGoogle Developers reproduced Ai2's Olmo 3 7B from scratch in MaxText on Google Cloud TPUs, covering both the stage-1 pre-training run and the stage-2 mid-training anneal. The match was checked on held-out C4 loss, an 8-task accuracy suite, multi-domain perplexity, and token-level KL, not just the training loss curve. The post also describes a data-loader bug that made training loss look better than the reference while held-out metrics did not move.

Why it matters: The post documents how a faithful reproduction was verified on held-out metrics, including a data bug that training loss alone would have hidden.

AIGoogle Research has introduced MilleMiglia, a standardized benchmark for optimizing middle-mile logistics, the segment that moves goods across hundreds of miles overnight. The benchmark uses spatial clustering and gravity models to simulate realistic middle-mile delivery scenarios. It addresses the difficulty of optimizing these networks without public data.

AIGoogle has released Android Bench 2.0, an updated benchmark that evaluates AI models on long-horizon tasks such as building apps from scratch, migrating cross-platform codebases to Android, and making complex architectural transitions. The benchmark uses continuous completion scoring to show which tasks each model performs well on.

AIGoogle Research is sharing an experiment that lets educators generate custom, guided STEM simulations tailored to their curriculum using generative UI. It is releasing a sample library of over 30 English interactives for physics, chemistry, biology, and math, all AI-generated and reviewed by teachers. Schools using Google Workspace for Education can sign up through the Google for Education Pilot Program to give feedback.

AIGoogle DeepMind, working with Science Stowers, built Atlas, which maps over 2,500 regulatory patterns in DNA. These sequences act as biological switches and volume dials that turn gene activity up or down across hundreds of cell types.

AIResearchers at the Broad Institute used Atlas to identify a critical mutation in the DNM1 gene, behind unexplained rare disease cases. The mutation was confirmed and validated in the lab.