Attribution Mapping

Unlike traditional page-level citations, effective attribution mapping operates at the passage, sentence, or span level. This requires chunking source documents into discrete, self-contained semantic units during ingestion. Each unit receives a unique, persistent identifier. When a model generates a factual claim, the system logs the exact chunk ID, enabling byte-level provenance rather than vague document references. This granularity is essential for high-stakes verification in regulated industries.

A robust map supports two-way navigation:

• Claim-to-Source: Given an AI output, instantly retrieve the exact source passage that grounds it.
• Source-to-Claim: Given a source document, identify every AI-generated statement that cited it. This bidirectional graph is critical for attribution drift detection. If a source document is retracted or updated, the system can instantly flag all dependent claims for review, preventing the propagation of outdated or false information.

The map is not merely a pointer; it is a rich metadata record. Each link between a claim and a source is augmented with:

• Confidence Score: An algorithmic assessment of how strongly the source supports the claim.
• Timestamp: When the attribution was created and when the source was last verified.
• Authority Vector: A multi-dimensional score reflecting the source's expertise, objectivity, and historical accuracy. This transforms a simple citation into a machine-readable trust assertion.

To ensure the map itself is tamper-proof, each attribution record should be sealed using provenance hashing. By generating a cryptographic hash of the source chunk and the generated claim together, the system creates a tamper-evident fingerprint. Any subsequent alteration to either the source or the claim breaks the hash chain. This provides a non-repudiable audit trail, proving that a specific claim was grounded in a specific source at a specific point in time.

Attribution links must survive content syndication, chunking, and summarization. This requires using content-based identifiers (e.g., cryptographic hashes of the source chunk) rather than fragile, location-based URLs. A persistent ID ensures that even if a document moves or is reformatted, the attribution link remains resolvable. This is the foundation of attribution persistence, ensuring credit and accountability are indelibly linked to the information itself.

A core technical challenge is source disambiguation. When a model cites 'Smith et al. (2023)', the mapping system must algorithmically resolve this to a specific, unique entity in its knowledge base. This involves parsing unstructured citation strings and matching them against a curated authority register using entity resolution techniques. Without this, the map becomes a tangled web of ambiguous references, undermining the integrity of the entire citation signal.