3 papers
cs.AI2026
Ingest-Time Fact Compilation for Cost-Efficient and Reliable Question Answering over Revised Corpora
Kyle Wild, Yusuke Takahashi, Asako Uraki
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 revi…
cs.AI2026
RAG Deserves an Index: Why Ingest-Time Compilation Beats Query-Time Interpretation
Kyle Wild, Yusuke Takahashi, Asako Uraki
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 a…
cs.AI2026
Cost Scales with Change, Not Corpus Size: Incrementally Maintaining an Evolving Semantic Substrate
Yusuke Takahashi, Kyle Wild, Asako Uraki
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…