Knowledge Extrapolation in Latent Abstraction Space: Tracers, Streams, Clusters, Future Knowledge, and the Compass Problem

This preprint proposes a geometric framework for knowledge extrapolation in latent abstraction space. Large language models are treated as semantically addressed knowledge systems in which latent space contains higher-order abstractions, while text and speech are understood as downstream projections of trajectories through that space. Building on prior work on LLMs as semantically addressed caches and on tracer-based latent diagnostics, the paper introduces a unified vocabulary of tracers, streams, threads, clusters of abstractions, and void-zones, and develops the concept of future knowledge as semantically coherent structure that appears before stable human ontology. It then proposes a compass-like instrument for navigating the frontier of science through latent geometry, while also identifying a core vulnerability: sufficiently large pseudo-knowledge or adversarial synthetic corpora can generate convincing geometry and mislead both AI systems and human decision makers who overtrust AI outputs.