Bolo：面向下一代AI数据库的经验证的模型中心

中文摘要 AI 辅助

经验证、可直接使用的推理流水线是未来AI数据库的基石，它允许多模态数据库整合专门的语言、视觉和表格模型，以实现高准确率与高效性。遗憾的是，现有的模型平台如Hugging Face未能达成这一目标：尽管它们托管了数百万个模型仓库，但许多仅包含原始权重，无可用的推理流水线；即便是文档完善的模型，也常因依赖缺失、不支持的模型类或任务分配错误而失效。此外，不同模型失效原因各异，缺乏统一解决方案，仅靠人力构建大规模经验证的模型中心几乎不可能。我们提出AI智能体可规模化实现这一目标，介绍了Bolo（即文中的\textit{system}），这一托管经验证、可直接使用的推理流水线的模型平台，由多阶段智能体系统驱动，用于模型修复。对于标准使用下失效的模型，智能体会检查错误并修复损坏的流水线（I类）；对于现有接口范围外的模型，它会利用模型元数据和文档从头合成流水线（II类和III类）。为防止错误流水线进入数据库，智能体采用多阶段验证——不仅检查程序结构，还验证模型的语义行为，确保流水线能产生有意义的输出，而非仅无错误执行。初步实验中，Bolo对II类和III类模型分别达到97.27%和86.08%的可运行覆盖率，表明带针对性验证的智能体合成可将大量无法使用的模型权重转化为经验证的可直接使用的推理流水线数据库，该初步数据库已开源至指定链接。

英文摘要

Verified, ready-to-use inference pipelines are a cornerstone of future AI databases. They allow multi-modal databases to incorporate specialized language, vision, and tabular models that can deliver both high accuracy and efficiency. Unfortunately, existing model platforms such as Hugging Face fall short of this goal. While they host millions of model repositories, many contain only raw weights without runnable pipelines. Even well-documented models often fail due to missing dependencies, unsupported model classes, or incorrect task assignments. Moreover, different models fail for different reasons, with no uniform solution. Constructing a large-scale, verified model hub is nearly impossible with human effort alone. We argue that AI agents can achieve this at scale. We present \system, a model platform that hosts verified, ready-to-use inference pipelines, powered by a multi-stage agentic system for model remediation. For models that fail under standard usage, the agent inspects errors and repairs broken pipelines (Type~I). For models outside the scope of existing interfaces, it synthesizes pipelines from scratch using model metadata and documentation (Type~II \& III). To prevent incorrect pipelines from entering the database, the agent applies multi-stage verification---checking not only program structure but also semantic model behavior, ensuring pipelines produce meaningful outputs rather than merely executing without error. In preliminary experiments, \system achieves 97.27\% and 86.08\% runnable coverage for Type~II and Type~III models, respectively, demonstrating that agentic synthesis with targeted verification can transform large collections of unusable model weights into a verified database of ready-to-use inference pipelines. The preliminary database is open-sourced at \textcolor{blue}{https://bolobao.ai/}.

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