Source Verification Protocol

Verifies the source is who it claims to be using multiple independent signals. This goes beyond domain matching to include:

• TLS/SSL certificate validation against Certificate Transparency logs
• Organization-level digital signatures on published content
• Cross-referencing the source's cryptographic identity with known Attestation Tokens
• DNS-level verification of the publishing infrastructure

A single-factor check (e.g., HTTPS) is insufficient for high-stakes grounding.

Quantifies the source's topical credibility using a Source Authority Vector—a multi-dimensional embedding that factors in:

• Historical accuracy: How often the source's claims have been verified against ground truth
• Domain expertise: The source's recognized standing in a specific knowledge domain
• Citation graph centrality: How frequently other authoritative sources reference this source
• Author-level credentials: Verified professional or academic affiliations

This score is dynamic, decaying over time if the source's accuracy degrades.

Traces the full Attribution Chain from the current document back to the primary source. The protocol must:

• Detect and flag citation laundering—where a dubious source is cited through a chain of seemingly reputable intermediaries
• Verify each link in the chain using Provenance Hashing to ensure no tampering occurred at any hop
• Reject sources where the chain is broken, circular, or terminates at an unverifiable origin
• Prefer sources with a direct, short-chain connection to primary data

Confirms the content itself has not been altered since publication. This layer employs:

• Cryptographic Provenance checks using hash-based integrity verification
• C2PA Content Credentials validation for tamper-evident metadata
• Citation Anchoring analysis to verify that specific claims within the text map precisely to supporting data points
• Attribution Drift Detection to flag when a previously verified source has been silently updated or retracted

Integrity failures automatically downgrade the source's trust score.

Evaluates whether the source's information is current enough for the specific grounding task. The protocol implements:

• Trusted Timestamping verification to confirm the actual publication date, not just the declared one
• Domain-specific freshness thresholds (e.g., medical guidelines may require sources < 2 years old; historical analysis may accept older sources)
• Update frequency monitoring to assess whether the source is actively maintained
• Automatic deprecation of sources that have been superseded by newer, authoritative publications

Cross-references the source's claims against a corpus of already-verified, high-confidence sources. The protocol:

• Uses semantic entailment models to detect when a new source directly contradicts an established Source-of-Truth
• Flags contradictions for human review or automatic rejection based on a Citation Confidence Scoring threshold
• Maintains a Provenance Ledger recording all verification decisions for auditability
• Applies Source Disambiguation to ensure the contradiction is genuine and not a case of mistaken entity identity

The computational task of resolving which specific entity a citation refers to when names are ambiguous. Critical for ensuring AI models cite the correct authoritative source rather than a similarly named but less credible entity.

• Leverages entity linking against knowledge bases like Wikidata
• Uses contextual clues such as co-authors, affiliations, and publication venues
• Prevents citation confusion between researchers with identical names

Example: Distinguishing 'J. Smith' the climate scientist from 'J. Smith' the financial analyst when grounding a claim about carbon emissions.

An algorithmic method for assigning a quantitative score to a source-citation pair, reflecting the model's certainty that the source substantiates the claim. This score informs downstream filtering and presentation decisions.

• Factors include semantic similarity, source authority, and recency
• Thresholds determine whether a citation is surfaced or suppressed
• Enables graduated trust rather than binary accept/reject decisions

A claim supported by a peer-reviewed study with high textual alignment receives a 0.97 confidence score, while a tangential blog reference scores 0.34.

The automated monitoring process that identifies when a cited source has been updated, retracted, or altered, causing a misalignment with the original claim. Essential for maintaining citation integrity over time.

• Continuously re-crawls and diffs referenced sources
• Triggers re-verification workflows when drift exceeds threshold
• Protects against silent content changes on live web pages

Example: A product specification page is updated post-launch; drift detection flags the change so AI-generated answers referencing the old spec are refreshed.