An Empirical Study of Coordination Mode as the First-Class Citizen in From-Scratch Multi-Agent Coding
Multi-agent vibe coding promises to accelerate software development, yet existing benchmarks rely on synthetic environments that ignore practical time and monetary costs, conflate reasoning with communication, and reward only superficial completion. We introduce multi-agent from-scratch evaluation benchmark, MSEval, evaluating multi-agent coding on real-world tasks. Grounded in 10 authentic, full-stack projects across 10 domains, MSEval scores performance using hierarchical requirements and deterministic rubrics. Its execution engine, LegoGent, tests 10 collaboration topologies where agents coordinate via periodic sync intervals and deploy through native CI/CD pipelines. Concurrently, the...
| Authors | Yanyu Ren, Yunfeng Bai, Xizheng Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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