Latent-1: Building a Universal Vector Space

This paper introduces Latent-1, a visionary framework for building a universal vector space that enables two transformative capabilities: (1) the discovery of super-metaphors—deep, cross-modal semantic patterns that link diverse modalities such as text, images, smells, and neural activation patterns; and (2) a shared interlanguage that allows smaller, specialized AIs to communicate through a common embedding space. Unlike current neural architectures, including language models and multimodal systems like CLIP or Flamingo, which operate within modality-specific latent spaces and generate outputs based on surface-aligned patterns, Latent-1 encodes a shared semantic geometry across all structured data. Inspired by the Platonic Representation Hypothesis, it assumes that all meaningful input—linguistic, sensory, or symbolic—can be tokenized and embedded in a unified high-dimensional space. Massive systems will query Latent-1 natively for complex discovery, while smaller models use translation protocols to engage with each other and the Latent-1 native system via vector-sharing. The paper outlines Latent-1’s architecture, including a scale sufficiently large to encode a significant portion of humanity’s collective knowledge and sensory experience (100-quadrillion parameters or larger), iterative snapshot growth, novel data integration (e.g., digitized olfaction), and safeguards for privacy, poisoning, and intellectual property. It proposes treating Latent-1 as a global semantic infrastructure, governed ethically and collaboratively. Latent-1 is not merely a model, but a meta-language of patterns—a new substrate for machine collaboration and human-AI synergy, enabling the pursuit of deeper discovery and collective understanding.