📄 中文摘要
AI模型在回答问题时，用户无法确定其答案的正确性。信任模型、提供者、基准分数和自信的表述并不能保证答案的准确性。单一模型给出的答案往往只是一个语法正确的猜测。这种现象被称为收敛问题，BAION（生物AI协调网络）因此不信任任何单一的AI模型。在现实世界中，每一个重大决策都依赖于多个判断来源，例如法院使用陪审团而非单一法官，医院在手术前需要第二意见，工程公司进行独立的结构分析，科学研究则要求可重复性。这些做法强调了多元化判断的重要性，以确保决策的准确性和可靠性。
没有收敛，便无所依靠
出处: Without Convergence, Nothing Holds
发布: 2026年3月3日
📄 English Summary
Without Convergence, Nothing Holds
When an AI model answers a question, users cannot ascertain the correctness of the response. Trusting the model, the provider, benchmark scores, and the confident delivery does not guarantee accuracy. A single model's answer is often just a well-structured guess. This phenomenon is known as the convergence problem, which leads BAION (Biological AI Orchestration Network) to distrust any single AI model. In the real world, major decisions rely on multiple sources of judgment; for instance, courts use juries instead of a single judge, hospitals seek second opinions before surgeries, engineering firms conduct independent structural analyses, and science demands reproducibility. These practices underscore the importance of diverse judgments to ensure the accuracy and reliability of decisions.