The emerging field of diverse intelligence seeks an integrated view of problem-solving in agents of very different provenance, composition, and substrates. From subcellular chemical networks to swarms of organisms, and across evolved, engineered, and chimeric systems, it is hypothesized that scale-invariant principles of decision-making can be discovered. We propose that cognition in both natural and synthetic systems can be characterized and understood by the interplay between two equally important invariants: (1) the remapping of embedding spaces, and (2) the navigation within these spaces. Biological collectives, from single cells to entire organisms (and beyond), remap transcriptional, morphological, physiological, or 3D spaces to maintain homeostasis and regenerate structure, while navigating these spaces through distributed error correction. Modern Artificial Intelligence (AI) systems, including transformers, diffusion models, and neural cellular automata enact analogous processes by remapping data into latent embeddings and refining them iteratively through contextualization. We argue that this dual principle - remapping and navigation of embedding spaces via iterative error minimization - constitutes a substrate-independent invariant of cognition. Recognizing this shared mechanism not only illuminates deep parallels between living systems and artificial models, but also provides a unifying framework for engineering adaptive intelligence across scales.

翻译：新兴的多样性智能领域致力于寻求对不同起源、构成与基质的智能体问题解决机制的整合性视角。从亚细胞化学网络到生物群体，跨越进化系统、工程系统及嵌合系统，我们假设能够发现决策过程中尺度不变的基本原则。我们认为，自然与人工系统中的认知皆可通过两个同等重要的不变量的相互作用来表征与理解：(1) 嵌入空间的重映射，以及 (2) 在这些空间内的导航。从单细胞到完整有机体（乃至更高层级）的生物集体，通过重映射转录、形态、生理或三维空间来维持稳态并再生结构，同时通过分布式误差修正在这些空间中进行导航。现代人工智能系统，包括Transformer、扩散模型与神经细胞自动机，通过将数据重映射至潜在嵌入空间，并经由语境化迭代优化这些嵌入，实现了类似的过程。我们主张，这种通过迭代误差最小化实现嵌入空间重映射与导航的双重原则，构成了认知中一种独立于基质的不变量。认识到这一共享机制，不仅揭示了生命系统与人工模型之间的深刻共性，更为跨尺度构建适应性智能提供了统一的理论框架。