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A three-model jury costs roughly three times the tokens per decision of a single model, according to the author’s own estimate, and a serial version adds at least two inference round trips before any action is taken. Those numbers are the author’s estimates, not measured provider prices. Agreement among models also does not by itself make a decision safer. The real question is whether consensus improves outcomes enough to justify the extra tokens, latency and review workload.
How a three-model jury works
The design treats several models as independent witnesses to the same situation. Each model receives the same observation, the same allow-listed tools and the same safety constraints, and each returns a structured recommendation. The example in the source post includes three fields: an action, a rationale and a confidence value.
A controller collects the three recommendations and applies a policy to decide what happens next. The policy, not the models, determines the outcome. That is why the design only works when quorum and fallback rules are written down before the first decision is made.
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For safety-critical decisions, the author proposes a quorum of at least two matching votes, with human review as the fallback. The sequence looks like this:
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- Each juror receives the identical observation and returns an action, a rationale and a confidence value.
- The controller groups the recommendations by action.
- If two or more jurors recommend the same action, that action proceeds under the policy.
- If no action reaches quorum, or a juror abstains in a way that prevents quorum, the case goes to a human reviewer.
This is the author’s proposed design. It is not an industry standard, and the post does not present it as a validated safety guarantee. Teams adopting it will need to choose their own thresholds for what counts as a safety-critical action.
What consensus costs
The post compares two ways of running a jury, and both add cost relative to a single call. The table below lists the figures the author gives and what remains unquantified.
| Arrangement | How calls are made | Cost stated by the author | Latency |
|---|---|---|---|
| Single model | One recommendation per decision | Not stated in the indexed text | Not stated in the indexed text |
| Parallel three-model jury | Three models receive the same observation and vote independently | Roughly three times the tokens per decision; retries add to this usage | Not quantified |
| Serial actor, critic and arbiter | A proposer drafts an action, a challenger objects, and an arbiter decides | At least two inference round trips before action | Grows with each round trip; not quantified |
The source is a post by maref, dated September 29, with no year shown in the indexed copy. The figures above are the author’s estimates. The post gives no measurement setup, model or provider combination, baseline, or sample size, so they should not be read as universal costs or as current pricing from any provider. Use them as a starting point for your own measurement, not as a budget.
Where agreement misleads
Consensus feels like verification, but three failure patterns can make it weaker than it looks.
Correlated blind spots
Models trained on overlapping data may share the same biases. When they agree, they may simply repeat one error three times. Three votes from related models are not three independent checks, so a unanimous decision can still be wrong in a predictable way.
Vote instability
Model outputs can be nondeterministic. If a juror is rerun on the same observation, it may return a different action, which can move a case across the quorum boundary. The author warns that when this happens, reliability starts to depend on chance rather than on the system’s design.
Abstention and workload
A juror that abstains can break quorum and push a case to a human reviewer. The post recommends tracking abstention separately from disagreement, because they describe different conditions. A juror that declines to answer tells you something other than jurors that answer and conflict.
False confidence
Agreement is not proof of correctness. The author’s central point is that consensus should be tested against later outcomes and against the view of the dissenting juror. In the author’s words: “I do not trust the majority. I trust the minority report.” That is a call to examine the dissent, not to ignore the majority.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to log and measure
The post recommends tracing every vote so that a decision can be reconstructed later. Useful trace fields include:
- the jury identifier and the model name for each juror
- each juror’s action and a hash of its rationale
- the final decision the policy produced
- the minority rationale, preserved in full
- disagreement and abstention, logged as separate state types
Two comparisons turn these traces into evidence. First, compare each final jury decision with a later success or failure signal. Second, compare jury outcomes with single-model outcomes over the same kinds of decisions, to see whether consensus improves safety enough to justify the added latency and tokens. The post recommends these comparisons but does not report results from them.
When a jury is worth its price
Use a jury when a wrong action is costly and hard to reverse, and when the decision can tolerate the added latency of multiple calls and retries. Before deployment, define the quorum threshold, the fallback path and the abstention rules. Run a single-model baseline on comparable decisions, and keep the baseline running long enough to see outcomes rather than agreement rates. If the jurors share a provider or training lineage, count their agreement as weaker evidence. A jury earns its cost only if its outcome record beats the baseline; agreement alone does not show that it does.
Once a jury is in place, its value is decided by the outcome comparison, not by the vote count.
Quick Recap
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