Replit 如何评估并改进 Replit Agent:从单次打分到持续改进闭环
Replit 为评估 Replit Agent 构建了离线基准与线上 A/B 测试、生产 trace 相结合的双支柱评估体系,并推出面向 vibe coding 的公开基准 ViBench,用自然语言 PRD 和测试计划检验智能体从零构建的应用是否符合需求。早期结果显示,前沿编码基准分数并不总能迁移到完整应用构建,且多数模型在扩展自己写的代码时表现变差。

terested in working on autonomous software engineering agents, our team is hiring; reach out to [email protected]. References [1] SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [5] Quantifying infrastructure noise in agentic coding evals [9] How we made continuous trace intelligence possible at scale Follow @Replit Sep 29, 2026Free the models: Harness design at the frontierModel routers are everywhere right now, but they have a fundamental limitation. No matter if based on adva
匹配字段正文





