Authority Vector

Unlike a scalar Trust Score, an Authority Vector projects an entity's influence across a topical ontology. Each dimension corresponds to a specific domain (e.g., quantum_computing, corporate_law).

• Sparse Representation: Most entities have zero authority in irrelevant fields.
• Granularity: Vectors can resolve expertise down to fine-grained concepts, not just broad categories.
• Dynamic Shifting: An entity's vector magnitude in a topic changes as they publish new content or gain citations.

Trust algorithms use cosine similarity to match an Authority Vector against a query's intent vector. This determines if an entity is an expert on the specific subject matter of a claim.

• Cosine Distance: Measures the angle between the entity's authority vector and the topic vector.
• Thresholding: A minimum similarity score is required before an entity's signals are weighted heavily.
• Contextual Relevance: Prevents a Nobel laureate in physics from being used as an authority on financial derivatives.

The vector's magnitude in each dimension is derived from fusing heterogeneous authority signals via a Signal Aggregation Layer.

• Publication Graph: Citation count and h-index within a specific field.
• Engagement Metrics: Meaningful interactions (not vanity metrics) from verified domain experts.
• Provenance Data: Cryptographic attestations of credentials and institutional affiliations.
• Temporal Weighting: Recent contributions are weighted more heavily via a Reputation Decay Function.

Authority Vectors are often computed over an Authority Graph using Trust Propagation. If Entity A (high authority in topic X) consistently cites Entity B, B's vector receives a fraction of A's authority in that dimension.

• Random Walks: Algorithms like Trust Rank bias walks towards authoritative seed nodes.
• Transitive Trust: Authority flows through citation links but decays with distance.
• Spam Resistance: Inbound links from low-authority or off-topic sources contribute negligible magnitude.

Raw vector magnitudes are statistically normalized to enable fair comparison across different domains. A score of 0.9 in astrophysics should represent the same percentile of expertise as 0.9 in culinary arts.

• Z-Score Normalization: Rescales values based on the mean and standard deviation of the population.
• Min-Max Scaling: Maps values to a 0-1 range for bounded input to downstream models.
• Trust Calibration: Iteratively adjusting normalization parameters against a ground-truth dataset of verified experts.

An Authority Vector is a living object. Without a Reputation Decay Function, a retired expert's vector would remain dominant indefinitely.

• Half-Life Parameter: Defines the time it takes for an inactive entity's magnitude to halve.
• Recency Bias: Recent publications and citations contribute exponentially more to the vector.
• Staleness Detection: Vectors that haven't been updated are flagged with a lower confidence interval.

Modern search engines decompose a domain's overall authority into topical authority vectors to determine ranking for specific queries. A site strong in 'medical research' but weak in 'financial analysis' will rank accordingly.

• Entity-based retrieval: Vectors align content with knowledge graph entities
• Query-document relevance: Cosine similarity between query intent vector and document authority vector
• Topical PageRank: Biased random walks that propagate authority along domain-specific citation edges

A cardiology journal's authority vector will show high magnitude in 'cardiovascular medicine' but near-zero in 'automotive engineering,' preventing irrelevant high-authority domains from dominating unrelated searches.

RAG systems use authority vectors to weight and filter retrieved documents before they enter the language model's context window. This prevents low-authority sources from contaminating generated outputs.

• Source selection: Only documents exceeding an authority vector threshold are retrieved
• Attention weighting: Higher-authority passages receive greater weight in the model's attention mechanism
• Citation prioritization: Generated claims are preferentially attributed to sources with strong topical authority vectors

When a user asks a medical question, the retriever scores each candidate document's authority vector against the query's topic distribution, ensuring responses are grounded in peer-reviewed literature rather than forum posts.

Platforms compute dynamic authority vectors for users, domains, and content creators to automate moderation, content promotion, and trust decisions at scale.

• Author authority vectors: Track an individual's demonstrated expertise across topics based on their publication history and peer validation
• Domain authority vectors: Aggregate the topical influence of all content published under a domain
• Temporal decay: Older contributions gradually lose magnitude unless reinforced by recent, high-quality output

A developer who consistently publishes accurate Python tutorials accumulates a high-magnitude vector in 'Python programming.' This vector is used to auto-approve their contributions while flagging first-time contributors for manual review.

Authority vectors enable granular citation evaluation by comparing the topical alignment between a claim and its cited source. A citation is scored higher when the source's authority vector strongly matches the claim's subject matter.

• Claim-to-source mapping: Extract the topic distribution of a claim and compare it to the cited source's authority vector
• Transitive trust: A source cited by multiple high-authority entities inherits a portion of their vector magnitude
• Contradiction detection: When a claim contradicts the consensus of high-authority sources in that topic, it receives a low integrity score

An AI-generated statement citing a marketing blog for a medical claim would receive a near-zero integrity score because the source's authority vector has negligible magnitude in biomedical topics.

Authority vectors serve as edge weights in knowledge graphs, quantifying the strength of the relationship between an entity and a specific domain of expertise.

• Entity enrichment: Each node in the graph carries a multi-dimensional authority vector
• Relationship typing: Edges are typed by the topical dimension they represent, with weight derived from the authority vector magnitude
• Query-time reasoning: Graph traversal algorithms use authority vectors to prioritize paths through high-expertise nodes

When resolving a query about a medical condition, the graph traverser follows edges weighted by biomedical authority vectors, ignoring paths through nodes with high authority in unrelated fields. This produces factually grounded, expert-verified answers.

Authority vectors provide a defense surface against content poisoning attacks by detecting when injected content's topical authority claims are statistically anomalous.

• Vector consistency checks: Flag entities whose authority vector changes abruptly without corresponding high-quality contributions
• Topical mismatch detection: Identify content that claims expertise in a domain where the publisher's authority vector shows no historical magnitude
• Coordinated inauthentic behavior: Detect clusters of entities with near-identical authority vectors, indicating fabricated reputation

A network of spam domains attempting to build fake authority in 'financial advice' would be detected because their authority vectors lack the gradual, organic growth pattern and interconnected citation structure of legitimate financial institutions.

The architectural component responsible for ingesting, normalizing, and fusing heterogeneous authority signals—including the authority vector—from disparate sources into a unified scoring input. This layer handles signal fusion by applying normalization techniques like Z-score standardization or min-max scaling.

• Resolves conflicts between contradictory signals
• Applies dynamic weighting based on signal freshness
• Outputs a clean feature vector for downstream scoring models

The process of assigning a probabilistic coefficient to individual data points or signals based on their estimated reliability before aggregation. When an authority vector contains dimensions with varying certainty levels, confidence weighting prevents low-confidence dimensions from disproportionately influencing the final trust score.

• Uses Bayesian priors to model signal uncertainty
• Essential when combining human judgments with automated metrics
• Reduces the impact of sparse or noisy authority data

A time-dependent mathematical formula that systematically reduces the weight of older trust signals to prevent stale authority from indefinitely influencing a current trust score. An authority vector computed from outdated publications or citations must be decayed to reflect current expertise.

• Common implementations: exponential decay, linear decay, half-life models
• Prevents 'authority lock-in' where legacy entities dominate indefinitely
• Parameterized by domain-specific decay rates

The algorithmic mechanism by which a trust score is transitively assigned from a known, high-authority entity to connected or cited entities within a reputation graph. When an entity's authority vector is sparse, trust propagation infers authority from its network neighborhood.

• Based on graph traversal algorithms like Trust Rank
• Attenuates trust across multiple hops to prevent over-propagation
• Critical for cold-start entities with limited direct signals

The iterative process of adjusting the parameters, weights, and thresholds of a trust scoring model to align its output scores with empirically observed, real-world trustworthiness outcomes. Calibration ensures that an authority vector's contribution to the final score reflects its actual predictive power.

• Uses held-out ground-truth datasets for validation
• Employs techniques like Platt scaling or isotonic regression
• Monitored via Expected Calibration Error (ECE) metrics