Advancing science and engineering with Antigravity's /teamwork

Vahab Mirrokni, Google Fellow and VP, Google Research

The Details

/teamwork multiagent systems, which enhance human-AI collaborations to solve open problems across mathematics, chemistry, systems software, and ML engineering. All of this work is done in collaboration with human experts, and we are making these tools available externally.

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Over the past several weeks, teams across Google and academic partner institutions have tackled open problems in math, science, and complex systems engineering that had remained open for years. Using Gemini 4 Argon, Gemini 4 Carbon, Gemini 4 Hydrogen and multi-agent workflows such as Antigravity’s /teamwork (building on various Gemini models), these collaborations have produced new innovations across information theory, formal verifications, molecular structure discoveries, and kernel optimization, along with several production infrastructure gains at Google. We are making these agentic tools available in Antigravity’s /teamwork (alongside /boost and other agents) so that researchers and developers can apply them to their own work.

Frontier research and systems engineering require navigating vast literature, building custom computational pipelines, testing against physical measurements, and verifying every step. Progress comes from an interactive discovery loop where researchers frame questions and define the theoretical or physical constraints, while long-horizon agents write custom solvers, explore large search spaces, test candidate designs, and check long chains of logic.

Two limits of current language models motivate

/teamwork's design. First, model generation can be unpredictable, leaving a gap between a single attempt and finding the correct answer. The system bridges this gap by pairing parallel exploration with dedicated falsifiers (e.g., the Lean 4 proof checker, unmodified software test suites, execution simulators, web search, or hardware counters) to automatically verify which candidate solutions work.

Second, AI models often lose track of details over multi-day tasks because attention disperses across growing context windows. To prevent this, /teamwork partitions context by construction: each specialized agent receives only the exact files and tools it needs for a specific step. Coordinators hand off tasks to fresh successors, while critics and auditors challenge and verify results from clean contexts without the original producer's reasoning.

Teamwork expresses these workflows as declarative self-improving natural-language patterns and dynamically routes each user prompt to the pattern best suited to the task. These patterns range from distributed coding and iterative problem-solving (packaged standalone as /boost, a sequential solve-and-verify loop refined from prior trajectories) to specialized research workflows like long proof and document review, all of which are available in Antigravity today.

This allows teams to design custom specifications from past run transcripts routing across different harness patterns and dynamically improving agentic harness. While many of these features are available in Antigravity, we will provide more updates in the coming weeks providing all the features to our external users on Antigravity. Following our previous posts on Antigravity /teamwork, we provide new results to open challenges empowered by new variants of /teamwork and Gemini 4 models.

Mathematics and theoretical computer science

Across mathematics and theoretical computer science, researchers paired with Gemini 4 and /teamwork harnesses to collaborate on large-scale proofs and generate complete algorithms and proofs directly from open problem statements. We present several examples.

• Proving and verifying the Courtade–Kumar conjecture
• Researchers from Google,
• CUHK,
• USC, and
• CMU used
• /teamwork's
• Long Proof pattern
• (implementing
• Stellar Colosseum in Antigravity) to prove information theory's

2013 Courtade–Kumar conjecture in full generality. The result confirms that when a random bit string passes through a noisy channel, no compression of the input to a single bit preserves more information than a "dictator function", which just copies a single input bit. Gemini first found counterexamples to a generalized conjecture from the CUHK team's earlier paper. The CUHK team then proposed a new candidate function that reduced the conjecture to a 14-variable optimization problem, along with a key analytical lower bound.

Gemini 4 Argon and earlier Gemini models then partitioned the parameter space and developed most of the new analytical bounds and interval-arithmetic certificates. They also extended the proof from balanced to unbalanced functions without human input. The team formalized the entire proof in Lean 4, producing 50 million lines of code.

Resolving open questions in optimization, statistics, quantum computing, and algorithms Researchers also posed open problems directly to Gemini 4 and to multi-agent harnesses such as Stellar Colosseum and /teamwork, receiving end-to-end algorithms and proofs (often with Lean 4 formalizations) that the authors verified, simplified, and edited for publication. In adaptive data analysis, a multi-agent harness running on Gemini 4 Argon independently proved and Lean-verified near-optimal sample bounds for statistical queries chosen adaptively from earlier answers, closing a gap open since 2014.

These bounds yield the first nearly space-optimal algorithm for data streams with both insertions and deletions that stays accurate against adaptive adversaries. In quantum computing, a new construction settled a 2014 open question by finding the first n -bit Boolean functions needing strictly more than n /2 quantum queries at constant error, while pinning down the exact query overhead across symmetric functions.

Several results break long standing barriers in processing and compressing large matrices and graphs. For fifty years until STOC 2024, combinatorial algorithms for Boolean matrix multiplication and triangle detection (avoiding the algebraic cancellations of fast matrix multiplication) saved only logarithmic factors over cubic time. A new deterministic algorithm resolves STOC 2024's main open question, achieving its target speedup over cubic time.

In high-dimensional geometry, a new lower bound resolves an open problem on sparse networks, proving that approximating all pairwise distances among n points within any constant factor can require polynomially more than n edges, even with extra Steiner points and arbitrarily many hops. In randomized linear algebra, stacked sparse sketches resolve a 2025 open question by breaking the classical sparsity barrier for compressing a matrix to linear dimension without collapsing any direction of an r -dimensional subspace, needing only √log r nonzeros per row (quadratically fewer than the log r required to preserve all lengths) and nearly matching a new lower bound.

For weighted graphs under dynamic edge-weight insertions and deletions, Stellar Colosseum resolved an open question left by FOCS 2022, developing the first single-pass streaming algorithm for cut sparsification that achieves near-optimal memory, update time, and reconstruction time simultaneously.

Our results also span optimization, arithmetic, data structures, and statistics. In optimization, resolving an open question on online inverse linear optimization (inferring an expert's hidden objective from decisions over time), the approach gave a deterministic algorithm achieving optimal regret while cutting runtime from exponential in the dimension to polynomial time. For n -bit integer multiplication, multi-a