The exploration of large language models (LLMs) reveals that they construct a hidden relational architecture known as the Vector Block prior to generating language. This spatial organization transforms input into a complex geometric structure, representing relationships among words and ideas across multiple dimensions, thus capturing the essence of internal meaning. The Vector Block serves as a cognitive map where meaning coalesces, indicating that the underlying structure is fundamental to language production, existing invisibly before any words are articulated.
Beneath every coherent sentence generated by an LLM lies a silent, invisible structureâa spatial organization of relationships and resonances, woven together from the input it receives.
The Vector Block is the high-dimensional field a language model creates when it processes a prompt; it captures the resonance of internal meaningâbefore it ever becomes language.
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