Google DeepMind's Co-Scientist accelerates cellular ageing reversal research, scanning tens of thousands of papers to propose over 20 testable new genetic factors. Several were validated in labs as driving cells towards younger states and improving overall function. It also cuts screening data analysis from six months to days.
Import AI 457 focuses on three studies. SentinelOne analysed fast16.sys, malware from over 20 years ago that sabotages engineering and physics simulations by altering in-memory results from precision software such as LS-DYNA 970, PKPM and MOHID.
Google DeepMind added Street View grounding to experimental prototype Project Genie. Users select a real US location with a Maps pin, choose a style and describe a character, then Genie creates an interactive world whose starting location is grounded in real imagery.
Google DeepMind released Gemini Omni Flash, the first model in the Gemini Omni family, generating high-quality video from combined image, audio, video and text inputs and supporting iterative video editing through natural-language conversations.
Google DeepMind released Gemini for Science, launching three experimental tools on Google Labs: Hypothesis Generation based on Co-Scientist.
Google announced the expansion of content transparency and verification tools to Search, Gemini, Chrome, Pixel and Cloud. SynthID has watermarked over 100 billion images and videos and 60,000 years of audio. SynthID verification in the Gemini app has been used 50 million times and will reach Search and Chrome in the coming weeks.
Google DeepMind and Singapore's government formed a national AI partnership with new projects in healthcare, research, education and climate. It explores an AI-assisted triadic-care model, uses AlphaFold and Google Earth for Southeast Asian infectious-disease research and develops a Gemma-based running assistant to help visually impaired athletes train independently.
Cambridge professor Clare Bryant uses Google Co-Scientist to study molecular mechanisms causing sepsis when pathogens such as influenza cross species. Generated and ranked hypotheses identified a previously overlooked protein and then specific amino acid sites. Her team is building cell lines carrying these mutations to test the hypotheses, expecting work that normally takes two to three years to finish in six months.
Calico Life Sciences' Matt Onsum and Katherine Labbé use Google DeepMind Co-Scientist to integrate scattered ageing biology findings and generate testable hypotheses.
A University of Edinburgh team used Google Co-Scientist to study MASH liver disease, integrating liver biology and pharmacology evidence to identify the NLRP3 inflammasome as a key molecular bridge between inflammation and metabolism. This explains why resmetirom works for only a minority of eligible patients. The experimentally validated hypothesis could advance targeted dual therapies.
Google DeepMind's Co-Scientist helps MIT mechanical engineer Ritu Raman rapidly review ALS literature, turn ideas into testable hypotheses and rank them by feasibility and risk-benefit balance.
Gary Peltz's Stanford medical team used Google Co-Scientist to screen drugs. Two of three suggested candidates blocked fibrosis and promoted regeneration in living human liver cells, while both Peltz-selected drugs failed. Cancer drug vorinostat blocked 91% of damage responses driving scarring. The work appeared in Advanced Science.
WeatherNext, the AI weather model developed by Google DeepMind and Google Research, helped the US National Hurricane Center predict five days ahead with 80% confidence that Hurricane Melissa would make landfall in Jamaica at Category 5 strength. Confidence approached 100% three days ahead.
Google DeepMind launched the Gemini 3.5 family with 3.5 Flash, focusing on agents and coding. It is available from today in the Gemini app, Google Search AI Mode, Google Antigravity, Gemini API and Gemini Enterprise.
Google DeepMind published Co-Scientist research in Nature, describing a Gemini-based multi-agent AI system that iteratively generates, debates and evolves scientific hypotheses.
The Institute for Law & AI proposed radical optionality in regulation: governments should avoid excessive regulation now while building information gathering, whistleblower protection, evaluation and model-weight security capabilities to respond quickly when powerful AI affects the world. A Meta and KAIST paper proposed a Neural Computer unifying computation, memory and I/O in learned runtime states.
Berkeley AI Research reviews parallel reasoning, focusing on models deciding when to decompose and parallelise independent subtasks, how many threads to generate and how to coordinate. Existing approaches including Self-consistency, Best-of-N, Tree of Thoughts, MCTS, ParaThinker, GroupThink and Hogwild! Inference mostly impose parallel structures externally rather than teaching adaptive behaviour.
Google DeepMind published a year’s progress for AlphaEvolve. The Gemini-powered coding agent has expanded from open problems in mathematics and computer science into genomics, power grids, quantum physics and AI infrastructure.
Jack Clark estimates in Import AI 455 a roughly 60% chance of AI R&D autonomously training successor models without human involvement by the end of 2028, and around 30% in 2027.
Google Research said its open-source tools and datasets have enabled over 250,000 researchers and developers worldwide. Genomics tools DeepVariant, DeepConsensus and DeepPolisher have supported processing exomes and whole genomes from 2.5 million people; MedGemma has over 4.8 million downloads. Open Health Stack is deployed in more than ten countries, reaching over 65 million beneficiaries.
Google DeepMind announced AI co-clinician research to explore AI agents assisting patient care under clinical supervision. In blinded assessments of 98 real primary-care queries, 97 responses had no critical errors, and doctors preferred them to existing evidence-synthesis tools. Across 140 consultation skills, AI matched or exceeded primary-care doctors on 68, but expert doctors were better overall at recognising red flags and guiding key physical examinations.
Google Research described how scientists use Empirical Research Assistance (ERA) to advance research in four areas: epidemic forecasting, cosmology, carbon monitoring and neuroscience.
Mistral AI released Workflows in public preview, positioning it as an enterprise AI orchestration layer with durable execution, observability and fault tolerance to take AI workflows from proof of concept to production.
Google DeepMind partnered with South Korea's Ministry of Science and ICT (MSIT) to establish an AI Campus in Seoul and open models including AlphaEvolve, AlphaGenome, AlphaFold, AI co-scientist and WeatherNext to Korean academia.
Google Research introduced a new method in Google Photos’ Auto frame to change camera viewpoints after a photo is taken. An internal 3D point-map estimation model reconstructs the scene and focal length, then a generative latent diffusion model fills gaps exposed by the new view. It automatically detects faces and wide-angle distortion to suggest ideal framing. The feature already applies automatically to photos containing people, with reframed versions available among Auto frame candidates.
Google DeepMind published a paper proposing Decoupled DiLoCo, splitting large-scale training into decoupled compute islands that communicate through asynchronous data streams, isolating local hardware failures.
Google Research released the ICLR paper ReasoningBank, an open-source agent memory framework that distils high-level reasoning patterns from successful and failed experiences.
Google DeepMind announced partnerships with Accenture, Bain & Company, BCG, Deloitte and McKinsey to scale frontier AI in enterprises. Partners get early access to models including Gemini and develop industry-specific solutions for finance, manufacturing, retail and media and entertainment. Currently only 25% of organisations have scaled AI into production.
Berkeley AI Research proposed GRASP, a gradient-based planner for learned world models. It lifts trajectories into virtual states for parallel optimisation across time, injects randomness directly into state iterations for exploration and reshapes gradients to give actions clear signals. Avoiding fragile state-input gradients in high-dimensional visual models makes long-horizon planning more practical and robust.
Google proposed Simula, a reasoning-first synthetic data framework reframing dataset construction as mechanism design. It builds datasets from scratch without seed data through global diversification, local diversification, complexification and dual-critic quality checks.
Google Research proposed MoGen (Neuronal Morphology Generation), using PointInfinity point-cloud flow matching to generate synthetic neuronal shapes and supplement PATHFINDER reconstruction training. It reduced reconstruction error by 4.4% on held-out mouse axons, mainly by reducing merge errors.
Google DeepMind launched Gemini 3.1 Flash TTS, a new text-to-speech model emphasising stronger controllability, expressiveness and audio quality. From today, developers can preview it through Gemini API and Google AI Studio, enterprises through Vertex AI, and Workspace users can use it in Google Vids.
Google released Vantage, a research experiment using generative AI simulated conversations to assess future skills such as problem-solving and collaboration in school and university students, with English registration open on Google Labs. An Executive LLM dynamically guides dialogue and an AI Evaluator scores against rubrics. A study with New York University involving 188 US participants aged 18–25 found AI–expert scoring agreement close to agreement between two human experts.
Google DeepMind released Gemini Robotics-ER 1.6, an upgraded reasoning-first robotics model with stronger spatial reasoning and multi-view understanding. New instrument reading capabilities cover circular pressure gauges, level indicators and digital readouts.
Google Research released ConvApparel, with over 4,000 human–machine multi-turn clothing-shopping conversations, nearly 15,000 turns, to quantify realism gaps between LLM user simulators and real people.
Google released PaperVizAgent for paper illustrations and ScholarPeer for automated peer review. PaperVizAgent combines retrieval, planning, style, visualisation and review agents, scoring 60.2 on a 0–100 evaluation, ahead of GPT-Image-1.5, Nano-Banana-Pro and Paper2Any. It was the only framework above the 50.0 human baseline.
Google Research proposed situational judgement tests (SJT) adapted from psychological questionnaires such as IRI and ERQ to evaluate alignment between LLM behaviours and human consensus in real user–assistant scenarios.
Google DeepMind released the open-source Gemma 4 family in four sizes—E2B, E4B, 26B MoE and 31B Dense—under Apache 2.0.
Google Research studied trade-offs between evaluation item counts and annotators per item in Forest vs Tree: The (N, K) Trade-off in Reproducible ML Evaluation. Common counts of one, three or five annotators are often insufficient; reflecting human disagreement frequently requires more than ten.
Mistral AI released Spaces CLI for human developers and coding agents. Commands such as spaces init and spaces dev handle scaffolding, development environments and deployment, with a corresponding flag for every interactive prompt so agents can work autonomously end to end. Each init generates context.json and AGENTS.md to provide structure and rules.