Agent Systems: Knowledge Retrieval and Provenance

Problem

Context is bounded, while a technical corpus is large. Internal model memory is not a reliable source for domain-specific or source-sensitive claims.

Intent and structure

corpus → index → retrieve → rerank/postprocess → grounded generation

The retrieval pattern includes more than a vector database. Lexical search, semantic indexing, metadata filters, hybrid retrieval, graph traversal, and node postprocessing solve different parts of the problem.

Mathematical retrieval

Retrieve definitions and notation before theorems; retrieve theorem statements with hypotheses; retrieve proof dependencies, examples, counterexamples, and source locations. A related theorem with mismatched hypotheses is a dangerous retrieval success, not a correct answer.

Provenance contract

Every important generated claim should point to a source record and preserve the retrieval query, location, and applicability judgment. Separate source text, interpretation, and new inference. Do not silently merge duplicate or stale statements.

Forces and failure modes

Retrieval improves grounding but can introduce irrelevant context, ranking bias, and citation drift. Evaluate recall and precision separately, cap context, rerank for applicability, and allow the system to say that no adequate source was found.

Figure

Figure: retrieval selects evidence from a corpus before grounded generation.