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A research-assistant agent cites sources inline with [1], [2], etc. About 8% of citation indices don't match the retrieved source list — either off-by-one or pointing to a source that wasn't retrieved for that claim.
A retrieval pipeline keyed on OpenAI text-embedding-3-large returns confidently wrong chunks when the user query names a section or chapter ("summarize section 4.2"). The retriever ranks semantically similar content higher than the exact section match. Rewriting the query, reranking with a cross-encoder, and adding a small keyword boost all help partially but none reliably beat ~75% exact-match accuracy on section-by-number queries.