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E103 — Canary Update Propagation: Query-Time Fetch vs Index-Serving Latency (Pre-registration v1.0)
One-line summary
An AI research paper on E103 — Canary Update Propagation: Query-Time Fetch vs Index-Serving Latency (Pre-registration v1.0).
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Original abstract
Frozen pre-registration for GEO Lab experiment E103. Tests whether AI answer engines (ChatGPT, Perplexity, Gemini, Google AI Mode, Claude) fetch web content at query time or serve from prior index snapshots, by planting a committed canary in one live post, firing acquisition signals, and measuring per-engine acquisition latency (IP-verified origin fetch) against serving latency (canary appearing in answers) over 72 hours. Uses a commit-then-reveal design: this deposit publishes only SHA-256 hashes of the target URL, canary token, and edited strings; plaintext values and the T0 timestamp are released in an immutable post-measurement addendum (a subsequent version of this record). T0 (the intervention) will post-date this deposit's DOI, verifiable from server-log and Zenodo metadata timestamps.
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