Advancing science and engineering with Antigravity's /teamwork

November 13, 2026 - Vahab Mirrokni, Google Fellow and VP, Google Research

We share recent results from Antigravity's /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.

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.

How /teamwork Enables Frontier Research

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.

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.

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-agent campaigns simplified the theoretical Turing-machine bound below n log n, while a separate effort gave a new O(n log n) algorithm on a single two-dimensional grid whose C implementation matches the GNU Multiple Precision Arithmetic Library (GMP) bit-for-bit up to a billion bits.
• In data structures, another result made significant progress on Tarjan's 1985 Deque Conjecture (whether self-adjusting splay trees handle insertions and deletions at both ends in constant amortized time), proving a new upper bound on the per-operation overhead that simultaneously beats every finitely iterated inverse-Ackermann function, verified in Lean 4.
• Finally, in high-dimensional statistics, the approach proved that approximate leave-one-out cross-validation, which avoids refitting the model from scratch, remains accurate for non-smooth sparse penalties like elastic net.

Formalizing Mathematics in Lean 4 with LEAP

Checking a computer-assisted manuscript by hand places a heavy burden on peer reviewers, and subtle flaws can hide inside long calculations. Formalizing mathematics in Lean 4 turns a proof into code that a computer kernel checks line by line. Once human mathematicians confirm that the formal theorem statement matches the original problem, the proof only compiles if every logical step holds.

Many of the deepest open problems in mathematics govern how physical waves propagate, how dynamical systems evolve, and whether polynomial equations have integer solutions. Working directly from informal proofs, our multi-agent framework, LEAP, developed with colleagues in Google Cloud AI and paired with Argon, has made progress in these core areas.

Notably, /teamwork-LEAP fully autonomously produced an end-to-end, machine-checked formalization of the Weil conjectures, the landmark results on counting solutions of polynomial equations over finite fields that culminated in Pierre Deligne's 1974 proof. This formalization spans about 1.8 million lines of Lean, produced across 28,800 subagent invocations.

The system also synthesized over 919,000 lines of Lean to produce the first formalization of a recent proof of Hilbert's tenth problem over general rings of integers. It also independently reproduces an end-to-end formalization of a recent proof of finite-time blowup for the 3D Navier-Stokes equations with smooth forcing, part of one of the seven Millennium Prize Problems and open for about 90 years.

Beyond these, it also generated the first formalization of the Birkhoff conjecture, a dynamical-systems problem open since 1927. Moreover, it formalized for the first time the 3D Bochner-Riesz conjecture, a central problem in harmonic analysis open for more than 50 years, finding and fixing a gap in a recently released informal proof along the way.

Additional machine-checked breakthroughs include resolving the C¹ regularity of infinity-harmonic functions in three and higher dimensions (an open question for about 20 years), formalizing Killip and Vişan's theorem that the KdV water-wave equation is well-posed in H⁻¹ and verifying a UCLA group's informal proofs of the 40-year-old Jamison Caterpillar Conjecture, the Ağırseven-Ollis conjecture, and an improved upper bound on Erdős Problem #1063.

Deep Review for STOC 2027

Finally, we provided Deep Review for authors of another CS conference: ahead of STOC 2027, authors of 578 submissions (over a third of all submissions) opted in to receive Deep Review feedback on their drafts. In an ongoing author survey (N = 70), 90% of respondents rated the feedback mostly or almost all factual and 90% said they would use the tool again. Over 30% said Deep Review caught substantive proof bugs or mathematically significant typos, and 14% said it found technical gaps that took more than an hour to fix.

Surveyed authors shared that "Tools of this kind can help produce cleaner and more precise research, faster" and that DeepReview provides peace of mind on scientific discovery". We provide users two weekly calls to Deep Review for free. Deep Review is also implemented as /teamwork's document review pattern and available in Antigravity. Read more about DeepReview in our recent blog post.

Experimental Laboratory Science and Chemistry

Beyond formal proofs, pairing specialized generator agents with independent verifier agents enables discovery workflows in experimental laboratory science.

Determining a molecule's structure from experimental nuclear magnetic resonance data is one of chemistry's hardest puzzles. For example, in HIV protease inhibitors, one misplaced bond separates an inactive look-alike from a drug. By organizing 30 specialized agents into a self-correcting loop with /teamwork and a physics-informed spectral simulator, we achieved 95% top-1 exact match and 100% top-3 accuracy on complex benchmark molecules, nearly doubling single-agent performance.

Beyond teaching Gemini to reason over structure, this lays the groundwork for ongoing works on 3D protein and complex structures dynamics. Beyond this example, science problems including applications in nuclear fusion and quantum algorithms reported in the Gemini 4 blog post benefited from /teamwork and the Argon model.

We also applied a similar multi-round auditing workflow to build empirical benchmarks. Starting from five human-specified acceptance criteria like data anonymization, 29 agents running on Argon over 11.5 hours turned 13 public tables into a 50-task causal inference benchmark. In these tasks, models must determine whether a causal effect can be identified and estimate it against a known answer. After initial checks, separate reviewer and auditor agents caught shortcuts, flagged wording hints, and vetoed hallucinated scripts, turning each into a permanent test. Automating this heavily manual task generation supplies the hard problems needed to help Gemini answer counterfactual "what-if" questions.

Software and Hardware Engineering

When paired with objective evaluators such as network benchmarks, game engines, unmodified test suites, and hardware timers, /teamwork campaigns can execute multi-day software and hardware engineering projects.

Using /teamwork and Argon, we built FarArray, a low-latency kernel-bypass Partitioned Global Address Space based on the SNAP microkernel. Applications built on FarArray include lock-free Connected Components, which scale to 1-trillion-edge graphs with 60x to 90x throughput gains and 40x to 180x speedups at 6x lower RAM over TCP key-value stores. Similarly, InstantEmbedding (Personalized PageRank) reduced peak DRAM by up to 14x and runtime by 11x to 27x over accelerated MapReduce implementations.

The team also used /teamwork and Argon