Google Research has published results from Antigravity’s /teamwork, a multiagent system that pairs Gemini 4 models with human experts on open problems in mathematics, chemistry, systems software, and ML engineering. The write-up reports work in information theory, formal verification, molecular-structure discovery, kernel optimization, and production infrastructure.

Google says the same tooling is now reaching outside users, alongside /boost and other agents.

That combination is the story. Plenty of labs show an AI model solving a puzzle. Far fewer describe a team of agents, checked by proof assistants and test suites, working next to people on problems nobody has closed yet.

In the material reviewed for this report, Google Research does not attach benchmark scores, speedup factors, or named researchers to those results, so this analysis separates what the announcement states from what it leaves open.

The timing matters too. Gemini 4 Carbon, Gemini 4 Argon, and Gemini 4 Hydrogen all reached developers this month, and our earlier look at Carbon’s Lean-verified code work showed how central formal checking has become to Google’s pitch. /teamwork is the layer that turns three separate models into one coordinated crew.

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The Google Research post frames /teamwork as a multiagent system built to support human-AI collaboration. It does not present the agents as replacing researchers. The work is described as done in collaboration with human experts, which shapes how readers should interpret every result in it.

Four fields get named: mathematics, chemistry, systems software, and ML engineering. Those are very different disciplines, and they reward different kinds of correctness. A proof either checks or it does not. A molecular structure has to match physical data. A kernel has to run faster on real hardware without breaking behavior. A production change has to survive live traffic.

The reported application areas map onto those fields. Google lists information theory and formal verification on the math side, molecular-structure discovery on the chemistry side, kernel optimization for performance work, and production-infrastructure work inside real systems.

Google also says the models and tools are being made available externally through /teamwork, which means developers and scientists can try the approach instead of reading about it.

One caution applies from the start. The announcement describes areas of work, not a leaderboard. Readers hoping for a single number that proves the system works will not find one in the reviewed material.

Antigravity’s documentation draws a clear line between its agent modes. The /boost command activates an on-demand multi-agent reasoning pipeline for hard software-engineering tasks, such as stubborn bugs, algorithm problems, race conditions, and tricky refactors.

The /teamwork command targets something bigger: large software projects, complex simulations, multi-milestone work, repository-scale migrations, and deep research.

The documentation also describes how a /teamwork session starts. It opens with a scoping interview, and only then does the system build a project plan. A coordination layer built around a Sentinel and a Project Orchestrator manages specialized roles and review gates.

That structure resembles a small engineering department more than a chatbot, and it explains why Google can aim the tool at open problems instead of single prompts.

For a working engineer, the practical difference is scope and duration. /boost fits an afternoon of debugging. /teamwork fits a project that spans days, with milestones and checkpoints. The Antigravity docs list subagents and a swarm mode as further options, so the product now offers a ladder from one delegated side task up to a full coordinated team.

Google Research ties the science results to that second tier. The research blog describes multiagent systems running inside /teamwork, not a lone model answering a question, and the distinction is the whole point of the release.

Google names Gemini 4 Argon, Gemini 4 Carbon, and Gemini 4 Hydrogen in the announcement. Each plays a different part, and Google’s model documentation spells out the split. The table below summarizes how Google describes them, drawing on its AI Studio and model-card pages.

Google calls Carbon its strongest reasoning model, built for long-horizon agentic work across software engineering, multimodal knowledge work, scientific reasoning, and cybersecurity defense. Availability varies by product and account tier. A multiagent team with open scientific problems on its desk would naturally put Carbon on the hardest reasoning steps.

Argon aims for frontier intelligence with fewer tokens per turn. The introductory price runs through November 30, and the listed rates double after that date. For a long /teamwork run, token spend adds up quickly, so that price step is worth planning around. Our report on Gemini 4 Carbon’s $2 intro price and uneven rollout covers a related availability wrinkle.

Google describes Hydrogen as built for speed, a model that can stand alone or pair with Carbon and Argon as the high-efficiency execution engine. That fits a team design where many cheap workers run in parallel while a stronger model plans and judges. We covered the launch pricing in our piece on Gemini 4 Hydrogen’s $1.50 input price and 1M context.

Google Research groups its…