docimprint

Research & academia workflows

How do research agents cite sources without hallucinating?

Q&A and ask-collection modes return answers with chunk_id citations. Each citation includes a Merkle proof binding the quoted text to the captured document — verifiable offline without re-reading the full paper.

Can teams build a literature review corpus with semantic search?

Yes. Collections index multiple evidence bundles. Semantic search returns relevant passages with bundle_id and chunk_id. Cross-document ask synthesizes findings with independently verifiable citations per source.

How is DocImprint different from RAG for academic workflows?

RAG stores embeddings with no proof of unmodified content. DocImprint evidence bundles bind captures to SHA-256 manifests at extraction time — supporting reproducible research where citations must be auditable months later.

Typical research agent workflow

  1. Capture PDFs and preprints as evidence bundles
  2. Index bundles into a project collection
  3. Run semantic search and cross-document Q&A with citations
  4. Verify critical citations via Merkle proof before publication
  5. Compare preprint vs journal version when revisions arrive

Evaluation metrics (labeled examples)

Example scenario: A literature review corpus of 25 papers indexed for semantic search. Cross-document ask cites 8 passages across 4 bundle_ids — each citation verified via Merkle proof (~320 bytes per proof). Offline bundle verification completes in <10ms without network access to DocImprint.

Illustrative workflow metrics — not customer testimonials.

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