Author

Kyle Wild

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Preprint Aug 2026

RAG Deserves an Index: Why Ingest-Time Compilation Beats Query-Time Interpretation

Nearly every retrieval-augmented question-answering system in production ships with a hidden interpreter: on each query a language model re-derives the meaning of raw corpus text and then throws that work away. Cheaper models do not close the gap: per-token prices have fallen by orders of magnitude while inference spen...

Kyle Wild, Yusuke Takahashi, Asako Uraki · 0 citations

#artificial intelligence Preprint Aug 2026

Ingest-Time Fact Compilation for Cost-Efficient and Reliable Question Answering over Revised Corpora

Most agentic question answering (QA) systems do an important part of their semantic work at the worst possible time: every time someone asks a question. When a corpus contains revisions, drafts, revocations, deletions, and sources with different levels of authority, the model must reconstruct the governed current state...

Kyle Wild, Yusuke Takahashi, Asako Uraki · 0 citations

Preprint Aug 2026

Cost Scales with Change, Not Corpus Size: Incrementally Maintaining an Evolving Semantic Substrate

Retrieval-augmented and agentic question-answering systems increasingly re-derive the meaning of a corpus at query time. Put plainly, instead of re-deriving what a corpus means on every question, the work is done once when a document arrives and is thereafter merely consulted -- a compiler, not an interpreter, of meani...

Yusuke Takahashi, Kyle Wild, Asako Uraki · 1 citation · ⚡1