发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出UNICON这一数值智能基础模型，可跨学科泛化，结合语言模型智能体后能超越训练未涉及学科的最先进专业人员，训练语料库多样性可提升泛化能力。
AI 中文摘要
智能通常被理解为获取和应用知识、适应陌生情境并解决新问题的能力。大语言模型通过从文本上下文推断任务相关知识并将其应用于新任务，展现出这种能力。然而，智能不必局限于语言。对于科学和社会系统，我们需要能从数值上下文获取和应用知识的模型——我们将这种能力称为数值智能。本文提出UNified In-Context Operator Networks（UNICON，统一上下文算子网络），一种在各学科中展现数值智能的基础模型。利用系统中基于图的示例作为上下文，UNICON可推断它们之间共享的预测关系，并将其应用于同一系统的查询。在科学和社会系统中，包括训练中未涉及的学科，该模型无需重新训练即可达到专业人员的性能。将UNICON与语言模型智能体结合可进一步提升性能，使其在训练中未涉及的学科中超越最先进的专业人员。我们还表明，训练语料库的多样性可提升对未见过学科的泛化能力。这些结果共同确立了UNICON作为数值智能基础模型的地位，并使其成为更广泛人工智能生态系统的构建模块。
英文摘要
Intelligence is commonly understood as the ability to acquire and apply knowledge, adapt to unfamiliar situations and solve new problems. Large language models exhibit this capacity by inferring task-relevant knowledge from textual context and applying it to new tasks. Yet intelligence need not be confined to language. For scientific and social systems, we need models that acquire and apply knowledge from numerical context-an ability we call numerical intelligence. Here we introduce UNified In-Context Operator Networks (UNICON), a foundation model that exhibits numerical intelligence across disciplines. Using graph-based examples from a system as context, UNICON infers the predictive relation shared across them and applies it to queries from the same system. Across scientific and social systems, including those from disciplines absent from training, the same model approaches specialist performance without retraining. Combining UNICON with language-model agents to perform contextual ensemble learning (CEL) yields further gains, enabling it to surpass state-of-the-art specialists in a discipline unseen during training. We further show that training-corpus diversity improves generalization to unseen disciplines. Together, these results establish UNICON as a foundation model of numerical intelligence and position it as a building block for a broader ecosystem of artificial intelligence.
