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Introduction
A 1024-dimensional embedding is not a summary of a manuscript. It is a statistical representation of how that manuscript relates to all other text the model has seen during training.
Research shows that embeddings encode multiple linguistic properties simultaneously, including syntax, semantics, and even domain-specific signals, in a distributed manner rather than isolating them into single dimensions.
Why can't embeddings be interpreted directly?
The difficulty in interpreting embeddings comes from their distributed representation structure. Unlike structured metadata, where each field has a defined meaning, embeddings spread information across all dimensions.
How does the geometry of expertise actually work?
Embedding space behaves like a high-dimensional landscape where proximity reflects conceptual similarity. In this space, manuscripts and researchers naturally form clusters based on shared themes, methods, and domains.
