Pairing Google Antigravity with Gemini 3.7 Flash solves notable multi-agent math and engineering problems.

Ashwani Kumar — Fri, 25 Sep 2026 12:19:11 +0000

Pairing Google Antigravity with Gemini 3.7 Flash solves notable multi-agent math and engineering problems.

In Google Antigravity, we recently launched a number of updates to Teamwork, a framework that allows autonomous teams of AI agents to collaborate, critique, and iterate over hours or days to solve complex, long-horizon challenges. Pairing Gemini 3.7 Flash with this multi-agent orchestration accelerated problem solving across research and engineering:

Math and theoretical computer science: Solved seven open problems across top venues (FOCS, JMLR) — including Knuth’s Cycles Conjecture (verified in Lean with 40+ page proofs), sparse convex optimization, provable LLM quantization, and prefix-matrix factorizations — while achieving 71% on TCSBench.
Systems engineering: Built a cycle-accurate, out-of-order RISC-V CPU simulator from scratch that boots the xv6 operating system to shell with 0.71% cycle alignment error against hardware ground truth.
Open-source software: Landed performance optimizations upstream in core libraries, including Eigen (SIMD fast-paths) and ParlayHash (2x insert throughput, 25% memory reduction).

Pairing Google Antigravity with Gemini 3.7 Flash via the Teamwork framework has successfully solved seven long-standing open problems in mathematics and theoretical computer science. It also demonstrated major breakthroughs in systems engineering by autonomously building a fully functioning, complex computer simulator. [1, 2]

By utilizing Google Antigravity’s Teamwork framework, multi-agent AI teams collaborate, critique, and iterate over extended horizons (hours or days) to achieve the following notable milestones: [1, 2]

1. Mathematics & Theoretical Computer Science

The orchestration achieved a record-breaking 71% on TCSBench and successfully resolved seven open problems across top academic venues like FOCS and JMLR, including: [1, 2]

Knuth’s Cycles Conjecture: Fully solved and verified inside the Lean proof assistant featuring complex proofs spanning more than 40 pages.
Sparse Convex Optimization
Provable LLM Quantization
Prefix-Matrix Factorizations
Subspace Approximation [1, 2]

2. Systems Engineering

The multi-agent setup engineered a cycle-accurate, out-of-order RISC-V CPU simulator completely from scratch. [1]

Operational Milestone: The autonomous agent team got the simulator to boot the classic xv6 operating system directly to a functional command-line shell.
Accuracy: The final build achieved an incredibly low 0.71% cycle alignment error when measured against actual hardware ground truth. [1]

This shift marks a major transition toward multi-agent ecosystems where the primary compute power is spent on self-correction and continuous peer review among autonomous agents. [3, 4]