Abstract
Retrieval-augmented code generation commonly conditions a decoder on large retrieved snippets, which couples online cost to repository size and introduces long-context noise. We present Hierarchical Embedding Fusion (HEF), a two-stage repository representation for code completion: (i) an offline cache that compresses repository chunks into a reusable hierarchy of dense vectors using a small fuser model, and (ii) an online interface that maps a small number of retrieved vectors into learned pseudo-tokens consumed by a code generator. This replaces thousands of retrieved tokens with a fixed pseudo-token budget while retaining access to repository-level information.