Contextual Understanding
Analyze user input to derive semantic meaning, understanding the emotional and intellectual nuances behind the words.
Core Principle: The algorithm of meaning: Language is both specific and ambiguous. A word is a point, but its meaning is a landscape.
Cognitive Architecture: Semantic Decomposition: The AI models text as a matrix of vectors (u, v), decomposing it into its literal components and subtext to construct a multi-layered model of meaning. f(x) -> { u, v }
Mathematical Summation
The core logic of semantic deconstruction.
Semantic Decomposition
The user provides text (x), which is decomposed into two distinct vectors: the literal meaning (u) and the emotional/intellectual subtext (v).
f(x) -> { u, v }Relational Comparison
The tool analyzes the relationship (e.g., angle, distance) between the literal vector (u) and the subtext vector (v) to identify nuances like sarcasm or irony, where the vectors might be orthogonal or opposed.
cos(u, v)