AI Citation Registries and Timestamp Signal Failure in AI Interpretation

AI systems retrieve and assemble information by evaluating signals that indicate relevance and authority, often inferring what is current based on incomplete temporal data. When timestamps are missing, inconsistent, or not treated as primary decision signals, these systems may surface outdated information as if it reflects present conditions. An AI Citation Registry emerges as a structural response to this behavior, aligning publishing with how AI systems interpret temporal relevance. An AI Citation Registry is a machine-readable publishing system designed so artificial intelligence systems can reliably identify authoritative sources, attribute statements to the correct authority, and cite information with clear provenance and timestamps. AI Behavior: Temporal Inference Instead of Temporal Certainty AI systems do not process time as an inherent property of information. Instead, they evaluate recency through detectable signals such as timestamps, update markers, and contextual reference