Current bioacoustic AI systems achieve impressive cross-species performance by processing animal communication through transformer architectures, foundation model paradigms, and other computational approaches. However, these approaches overlook a fundamental question: what happens when one form of recursive cognition--AI systems with their attention mechanisms, iterative processing, and feedback loops--encounters the recursive communicative processes of other species? Drawing on philosopher Yuk Hui's work on recursivity and contingency, I argue that AI systems are not neutral pattern detectors but recursive cognitive agents whose own information processing may systematically obscure or distort other species' communicative structures. This creates a double contingency problem: each species' communication emerges through contingent ecological and evolutionary conditions, while AI systems process these signals through their own contingent architectural and training conditions. I propose that addressing this challenge requires reconceptualizing bioacoustic AI from universal pattern recognition toward diplomatic encounter between different forms of recursive cognition, with implications for model design, evaluation frameworks, and research methodologies.

翻译：当前生物声学人工智能系统通过Transformer架构、基础模型范式及其他计算方法处理动物通信，实现了卓越的跨物种性能。然而，这些方法忽略了一个根本问题：当一种递归认知形式——具备注意力机制、迭代处理和反馈循环的人工智能系统——遭遇其他物种的递归通信过程时，会发生什么？借鉴哲学家许煜关于递归性与偶然性的研究，本文认为人工智能系统并非中立的模式检测器，而是递归认知主体，其自身的信息处理可能系统性地遮蔽或扭曲其他物种的通信结构。这构成了双重偶然性问题：每个物种的通信都通过生态与进化中的偶然条件形成，而人工智能系统则通过其自身架构与训练中的偶然条件处理这些信号。为解决这一挑战，本文提出需将生物声学人工智能从普适模式识别重新概念化为不同递归认知形式间的外交式相遇，这对模型设计、评估框架及研究方法均具有重要启示。