Google Antigravity et Gemini 3.7 Flash résolvent sept problèmes ouverts en mathématiques et ingénierie
L'association d'un modèle rapide à une orchestration multi-agents franchit un cap critique en automatisant des recherches théoriques complexes et l'optimisation de code système bas niveau.
Pourquoi c'est important
Cette avancée démontre que l'orchestration multi-agents couplée à des modèles hautement réactifs ne se limite plus à la génération de code basique, mais devient capable de contribuer directement à la recherche fondamentale et au développement d'infrastructures logicielles critiques.
Resume
En combinant Google Antigravity et Gemini 3.7 Flash, un système multi-agents a résolu sept problèmes ouverts en informatique théorique et mathématiques, générant plus de 40 pages de preuves et atteignant 71 % sur TCSBench. L'architecture a également conçu un simulateur de processeur RISC-V avec 0,71 % d'erreur d'alignement et intégré des optimisations dans Eigen et ParlayHash, doublant le débit d'insertion et réduisant l'empreinte mémoire de 25 %.
Le point a retenir
L'orchestration multi-agents transforme l'IA en un collaborateur autonome pour la recherche scientifique et l'ingénierie système de haut niveau.
Texte source original
Breadcrumb Home Innovation & AI Technology Developer tools Pairing Google Antigravity with Gemini 3.7 Flash solves notable multi-agent math and engineering problems. Aug 31, 2026 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). Read about all the wins on the Antigravity blog . Posted in: Developer tools Related stories AI The latest AI news we announced in August 2026 By News from Google Team Developer tools Gemini Omni 1.1 Flash lets you build with more control By Anish Nangia & Alisa Fortin Developer tools How developers build AI for good with Gemma 4 By Glenn Cameron & Kristen Quan Developer tools Inside the Gemmaverse: Celebrating one billion Gemma downloads By Clement Farabet & Olivier Lacombe Breadcrumb Home Innovation & AI Technology Developer tools Pairing Google Antigravity with Gemini 3.7 Flash solves notable multi-agent math and engineering problems. Aug 31, 2026 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). Read about all the wins on the Antigravity blog .