为拉美构建AI评估基准层，解决本地化模型验证与优化难题
On the missing benchmarks layer and a potential solution
- 提出EvalsHub框架，以拉美首个区域实例LatamBoard为核心
- 支持机构与企业持续评估、对比不同AI系统性能表现
- 开放共建机制，推动本地AI发展与跨国系统审计
拉丁美洲缺失了本土AI发展的基础层：评估基准层。该层兼具两项独特功能：一是依据地区社会需求审计AI系统，二是引导AI在经济相关场景中的优化方向。缺乏此层，公共机构无法独立评估外来AI系统，企业亦难以针对本地问题实现最优性能的模型优化。其代价是双重的：既丧失了技术可审计性，也失去了关键基础设施的优化路径。为此，我们提出EvalsHub，以拉美首个区域实例LatamBoard为基础，构建一个开放、任务导向的评估基础设施。该平台允许高校、公共机构、专业社区及企业发布、执行、比较和维护跨模型、工作流与智能体的评估任务。一次构建，长期复用——随着新AI系统上线，机构可重跑评估；企业每次系统迭代后亦可即时验证。平台设计开放，激励机制驱动。
原文摘要 · Abstract (English)
Latin America is missing a foundational layer for native AI development: the benchmark layer. The benchmark layer does two things no other layer can - it audits AI systems against regional social requirements and it directs AI optimization in economically relevant environments. Without it, public institutions cannot independently evaluate foreign AI systems, and companies cannot optimize AI systems to solve local problems with SOTA performance. The cost of the missing layer is dual: a loss of auditability and a loss of optimization direction over a technology that is increasingly critical infrastructure. We propose an EvalsHub, with LatamBoard as its first regional instance - an open, task-first benchmark infrastructure where universities, public institutions, professional communities, and companies can publish, execute, compare, and maintain evaluations across models, workflows, and agents. Built once, measured forever - re-run by institutions as new AI systems ship and by industry teams after every system change. Open by design and incentive-driven by construction.
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