Google DeepMind

This is Genie 4, our most realistic and interactive world model yet. 🌐 Explore dynamic environments in real time to see how the scene responds, sounds, and how the people in it move, react, and speak.

Gemini 4 Carbon is our next frontier model. It’s state-of-the-art across cybersecurity and long-horizon capabilities.

Carbon can work through complex tasks for days without getting stuck or taking shortcuts – delivering high-quality, reliable code. Feedback from an initial group of cyber defenders will help us iterate on guardrails before we expand access soon. Find out more ↓

RT @pushmeet: Delighted to see the launch of the UK’s new AI Biosecurity Lab, which @GoogleDeepMind will partner with and support. As life sciences enter a transformational era, it is essential for researchers, government, and industry to collectively strengthen our biological defences and guide these new capabilities responsibly. Congratulations to @KanishkaNarayan and the entire team that has worked on making this a reality.

2/7 We're launching a £165m National AI Biosecurity Initiative, including £100m for a world-first AI Biosecurity Lab. This will harness British scientific expertise and latest AI to detect biological threats and protect people from emerging risks. As with our @AISecurityInst, I want the new AIBL to lead the world in capability.

Here’s what’s new with Genie 4: 🔵 Photorealism: Built on Gemini Omni for lifelike detail across spaces and subjects 🔵 Advanced physics: Aerodynamics, collisions, and people who move naturally 🔵 Real-time controls: Prompt to change the scene, add elements, or interact with characters 🔵 Native audio: Ambient sound, music, background noise, and interactive dialogue 🔵 Gemini reasoning: Plans multi-step events to maintain coherence as scenes evolve These capabilities are a step toward developing safe virtual environments to see how agents and robots navigate their surroundings. We’re exploring early use cases with trusted testers first, before bringing the model to Google AI Ultra subscribers in Project Genie and early access partners in our API and @GoogleAIStudio soon. Find out more →

RT @SpringerNature: Springer Nature and @GoogleDeepMind are partnering to co-design an AI peer review assistant, exploring how AI can help reviewers navigate manuscripts more effectively and focus their time on specialist scientific judgement. Learn more:

Using a single Nano Banana image and Blender MCP, Gemini 4 Argon built a 3D Gothic cathedral in @Antigravity. It iterates on the geometry, textures, and lighting to create the finished scene.

Not all forests are equal, and now we can map the difference. 🌳 Meet Forest Typology (ForTy): a 10m map of natural, planted, and farmed forests to help identify deforestation and protect the forests that matter most. 🧵

Different forests can look identical from above, so ForTy tracks how they have changed over time. 🌲🛰️ Built on AlphaEarth Foundations, our foundational AI model for planetary data, ForTy reads years of satellite imagery across 1.7M sites to help governments, companies, conservation groups and scientists build deforestation-free supply chains.

Published in @ScientificData, the ForTy 2020 map and the 2017–2025 time series are freely available today on Google Earth Engine - helping organizations build deforestation-free supply chains and protect the natural forests that matter most. Find out more →

RT @pushmeet: SynthID timeline over the last few weeks has been momentous – from opening SynthID Detector to the public, to launching SynthID Bio to watermark functional, AI-designed proteins. Each moment brought us a step closer to our broader vision on provenance, or helping verify what is AI-generated and what isn’t. Recently, Dartmouth Professor Hany Farid and I discussed why solving provenance is vital to @GoogleDeepMind’s broader mission to develop AI to benefit humanity. In short: solving provenance is a highly complex, multi-disciplinary effort with watermarking technology at its core. It requires a collective mix of frontier research, interoperable open standards, and direct collaboration across platforms and developers. Read our full conversation on Google's Public Policy blog:

RT @ralucaadapopa: Wiz Research discovered PHP2Shell - CVSS 9.2 vulnerabilities in core PHP affecting 70% of the web. Pleased to see the important role that Gemini 4 Argon played here, finding the vulnerability, developing the complex exploit chain, creating attack detectors and validating the patch, giving defenders an advantage. Read more here:

Gemini 4 Argon combined three open-source datasets to build an interactive 3D visualization of human anatomy. Toggle different layers in motion, isolate muscles, or zoom inside a beating heart.

RT @TZahavy: Over the past year, the Discovery team at @GoogleDeepMind, in a project led by student researcher @dsantosmarco, has made new discoveries in one of the most beautiful open problems at the edge of computation: Finding the 6th Busy Beaver (BB) number. While most AI math systems solve human-generated problems, like theorems, this work tackles a different aspect of mathematical creativity: Discovering hidden problems. For years, the BB Community has pursued one of theoretical computer science’s most captivating collaborative quests: finding the simplest programs that run the longest before halting. Because determining whether these programs ever stop is uncomputable, no universal algorithm can solve it, meaning new mathematical techniques need to be found and invented to make progress. In this way, the BB challenge serves as an open ended domain for mathematical creativity and, in particular, a generator of hard math problems called Cryptids. These are tiny programs whose halting behavior is governed by and equivalent to deep, unsolved math problems in number theory, similar to the Collatz or Goldbach conjectures. Historically, the BB community had recognized ~20 Cryptids in BB(6), and the best problems were selected for the Busy Math Olympiad (BMO). Building on their work, Marco built a pipeline powered by Gemini 4 Argon that: 1️⃣ Simulates raw Turing machine traces to compress recurring patterns. 2️⃣ Conjectures mathematical transition rules & runs synthetic experiments to test edge cases. 3️⃣ Proves the equivalence formally in Lean 4.

Using this pipeline, Marco and the team discovered 20 new potential Cryptids and verified 33 more machines previously studied in the BB community. All the proofs were open sourced and shared in the community discord channel and we hope that they will help the community to make progress towards resolving BB(6). We also contributed related halting/non-halting proofs directly to the community GitHub repository. Finding these Cryptids shows hidden links between simple programs and number theory, and helps mathematicians map the limits of computation. I'm also excited about Gemini 4 Argon's ability to help researchers both solve hard problems and accelerate scientific discoveries, of which Busy Beaver is just one example. Work led by our student researcher @dsantosmarco with paper contributions from @vivek_veeriah, @Dahoas1 , Andrew Bolt, @zuza777 , Satinder Singh, and myself with additional contributions from Hussain Masoom, @maxencefaldor , @MoritzFirsching and Petar Sirkovic. Read more about it in our paper:

Gemini 4 Argon built a 3D periodic table synced to Tom Lehrer’s song, “The Elements.” Created in @Antigravity, it animates orbiting electrons, atomic data, and light spectra.

RT @ancadianadragan: We did a lot to understand Gemini 4's capabilities and propensities, and to improve our mitigations for this launch -- you can get a tl;dr here and dive into the report

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 ↓

Four Gemini 4 Argon agents, one shared Minecraft world, one goal: defeat the Ender Dragon. 27 hours and 2,783 self-written programs later, the dragon fell.

Together with scientists around the world, our models and tools are helping make disease more detectable, treatable, and preventable. 🧬 From pinpointing rare genetic variants to speeding up drug discovery, here’s how we’re helping turn lab breakthroughs into real-world care →

RT @PeterWBattaglia: Last week, Hurricane Isaias made landfall in the Florida Panhandle. Our thoughts are with everyone affected and with the recovery efforts still underway. Earlier and more accurate forecasting can help protect communities by giving them more time to prepare, and we are grateful that Google’s WeatherNext model was able to assist the @NWSNHC 's expert forecasters. Based on initial evaluations, here's how our model supported the response to #Isaias: - Tracking the Hurricane’s Path: WeatherNext was the most accurate model on the storm’s track. While traditional physics models predicted landfall closer to New Orleans, WeatherNext correctly predicted a path further east. This allowed NHC forecasters to highlight the potential for wind and storm surge impacts in the Florida Panhandle from their very first forecast. - More Time to Prepare: Up to three days before landfall, our model consistently predicted its eventual location in the Florida Panhandle to within 60 km or less, supporting the NHC as they honed in on a track toward the Florida Panhandle and issued hurricane watches and warnings to give officials in Florida, Alabama, and Mississippi more time to prepare. - Early Indications about Intensity: Early on, WeatherNext accurately predicted the intensification to a major hurricane. Alongside the regional hurricane models HWRF and HMON, WeatherNext’s intensity forecasts helped NHC forecasters to predict that the Isaias would approach the coast as a category 2 hurricane from the very first advisory. Giving communities more time and more accurate warnings is critical for protecting lives and livelihoods. While model performance varies from storm to storm, Isaias reflects our broader findings. In our recent @Nature paper co-authored with NHC forecasters, evaluating 200+ global tropical cyclones, we found our model provides ~1 day of additional lead time for both track and intensity. It’s been an honor for the @GoogleDeepMind and @GoogleResearch teams to work alongside NHC forecasters. By combining AI, physics models, satellite data, and the crucial expertise of meteorologists, we're excited to keep pushing the boundaries of forecasting to help protect communities.

SynthID has been named one of @TIME's Best Inventions of 2026. With over 180 billion images and videos already watermarked, it’s making it easier for people to distinguish AI-generated content from other media →

RT @googlecloud: Explore the latest advances in native audio models and the challenges of building better real-time voice experiences with @GoogleDeepMind + @SierraPlatform. Learn about conversational latency, multilingual code-switching, agentic voice tasks, and more →

Gemini 4 Argon is already unlocking new scientific breakthroughs in quantum computing, nuclear fusion, protein design and beyond. 🧵

Argon also compressed the process of enzyme engineering into a matter of hours to design five lab-verified serine esterases. 🧬

Over the past few weeks, we have been putting Gemini 4 Argon to the test on protein engineering workflows at Google DeepMind, and we have some interesting initial (zero shot!) results to share. 🧵👇

To advance more research with Gemini 4 Argon, we’re offering 10,000 scientists access to our highest usage tier in @GoogleAntigravity free of charge for 6 months. Find out more →