Fact-Checking Protocol

The initial stage isolates discrete factual assertions from the source document. Each sentence is parsed to identify triples (subject-predicate-object) and numerical claims. These claims are then decontextualized—rewritten as standalone, unambiguous statements—to prevent the verification process from being influenced by surrounding narrative framing. For example, 'Revenue grew 20%' becomes 'Company X's revenue in fiscal year Y grew by 20% compared to fiscal year Z.'

Each decontextualized claim triggers a hybrid retrieval process against a curated knowledge base of high-confidence sources. This combines:

• Dense vector search for semantic similarity to the claim
• Sparse keyword retrieval (BM25) on exact entity names and figures
• Metadata filtering to prioritize sources with high Domain Authority and E-A-T scores The system retrieves a candidate set of documents that potentially corroborate or refute the claim.

The core verification logic evaluates the alignment between the extracted claim and the retrieved evidence. A Natural Language Inference (NLI) model classifies each evidence-claim pair as entailment, contradiction, or neutral. The protocol then applies a Multi-Source Agreement heuristic: a claim's confidence score increases when multiple independent, authoritative sources exhibit entailment. A Bayesian Trust Model updates the final score by weighting each source's historical reliability.

Every verification decision is auditable. The protocol generates a provenance chain that records:

• The original document and exact text span of the claim
• The specific evidence snippets retrieved from each source
• The NLI model's classification and confidence score for each pair
• The final aggregated verdict and the weighted contribution of each source This creates a deterministic citation graph linking the output back to immutable evidence.

The protocol applies a Temporal Decay Function to source trustworthiness. A source's authority on a claim is modulated by its publication date relative to the claim's timestamp. For queries where user intent demands recency, evidence from outdated sources is deprioritized. The system cross-references the claim's temporal context against the source's Content Freshness score to ensure the verification is historically coherent.

When the evidence is conflicting or insufficient, the protocol does not force a binary true/false decision. It outputs a calibrated confidence score and a detailed uncertainty report. Claims that fall below a defined threshold are flagged for human-in-the-loop review. The system logs the specific points of contention, such as contradictory figures from two high-authority sources, enabling efficient manual adjudication.

A verification technique that boosts the confidence score of a factual claim when multiple independent, authoritative sources corroborate the same information. This protocol step reduces reliance on any single potentially biased source.

• Requires sources to be truly independent, not citing each other
• Weights agreement by the authority score of each corroborating source
• Flags claims with high source divergence for manual review

The process of documenting the origin, custody, and transformation history of a piece of information to establish its authenticity and chain of attribution. Fact-checking protocols rely on unbroken provenance to validate claims.

• Records every entity that has modified or transmitted the data
• Uses cryptographic hashing to detect tampering in the custody chain
• Essential for distinguishing original reporting from derivative commentary

A probabilistic framework that updates the trustworthiness score of a source by combining prior beliefs with new evidence of content accuracy or deception. This allows fact-checking systems to dynamically adjust source reliability.

• Prior probability represents initial reputation or domain authority
• Likelihood updates occur when claims are verified or debunked
• Converges on a stable trust score with sufficient evidence volume

A framework representing Expertise, Authoritativeness, and Trustworthiness, used by human quality raters to evaluate the credibility of a webpage's primary content and its creator. Fact-checking protocols often map to these dimensions.

• Expertise: Does the creator have formal credentials or demonstrated experience?
• Authoritativeness: Is the creator or site recognized as a go-to source?
• Trustworthiness: Are factual claims accurate and citations present?

The application of natural language processing and stance detection models to automatically identify false or misleading information spread unintentionally. This serves as the first-pass filter in automated fact-checking pipelines.

• Stance detection identifies whether a claim agrees, disagrees, or discusses a known fact
• Linguistic feature analysis flags emotional language and logical fallacies
• Requires a ground-truth knowledge base of verified claims for comparison

A scoring metric that rewards documents for providing unique, novel information beyond what the user has already seen in previously ranked results. In fact-checking, it ensures each source contributes additive value.

• Calculated by comparing the conditional probability of content given prior results
• Penalizes redundant or derivative sources that add no new evidence
• Prioritizes sources that fill gaps in the verification knowledge base