A blog post from ColorTokens exploring how cosine similarity and trigonometric identities underpin LLM embeddings, and why geometric proximity alone cannot guarantee factual accuracy in AI outputs. It introduces a 'progressive refinement loop' architecture for Zero Trust microsegmentation policy generation, drawing parallels to the Aletheia math-research agent (generator–verifier–reviser pattern). The Xshield system applies this loop to avoid hallucinated policies that could break legitimate traffic or leave lateral movement paths open, requiring domain-specific evaluators and human checkpoints before implementation.
Table of contents
1. A School Identity Hidden Inside a 1 Km Circular Field2. When Words Become Vectors, Our Trig Identities Return3. How AI Rediscovers Our Trig Identity4. Why Geometry Alone Can Still Hallucinate5. From “Vanilla” LLM Generation to a Progressive Refinement Loop6. From cos(x + y) and Zero TrustSort: