Features
Several models, still one inspectable score
Asking several AI models the same question and taking the majority answer feels thorough. It is less thorough than it looks. Models trained on overlapping text tend to make the same mistakes, so their agreement says more about what they read than about what is true.
Why crowds are sometimes wise
In 1906 Francis Galton studied a contest at a livestock fair in Plymouth, where visitors guessed the weight of an ox. The median of their guesses landed within about one percent of the true weight. The crowd was accurate because each guess was made separately, from different vantage points, with errors that pointed in different directions and cancelled out.
Remove that independence and the effect disappears. When everyone reads the same newspaper before guessing, their errors move together.
Models share their errors
Large language models are trained on heavily overlapping public text and tuned in similar ways. When one repeats a common misconception, others often repeat it too. Five fluent answers that agree may be one answer said five times, and a majority vote among them can make a shared error look settled.
Agreement is still worth noticing, though on its own it proves nothing, and it should never stand in for a source.
What a Decision Room does instead
A room can draw on more than one model, and different Participants may run on different ones, which lowers the chance that one model's habits shape the whole argument. The roles are opposed by design: each option has a Champion, the Challenger attacks the weakest reasoning, and the Contrarian attacks whatever the room has come to agree on.
Claims are never settled by agreement. Scout investigates facts from public sources, Inspector checks what that evidence actually proves, and the Judge decides what survived the argument rather than which answer was most popular. Challenge the Verdict then runs an independent second pass that can overturn the first.
What you see on the record
You see the arguments, the evidence, the status of each claim, and a Verdict that states what it rests on. Which model served which role stays out of view, because the record should be judged on its evidence rather than on the reputation of whatever wrote a line. The Human Decision that follows belongs to you.
Three agreeing answers, one stale figure
A restaurant owner weighing a later closing time asks what the local minimum wage will add to the cost of each extra shift. Three general AI assistants agree on the hourly figure. In a Decision Room, Scout looks up the rate on the city's own published notice and finds that it rose on January 1, while all three answers used the previous year's figure. Inspector marks the agreed figure contradicted, and the shift costs are worked again from the current rate. One current source outweighed three answers that agreed.
Questions
- Which models does Argumont use?
- The room draws on more than one, and the mix can change as better models appear. The record is built to be judged on its evidence rather than on which model wrote a given line.
- Is disagreement between models a problem?
- Often it is the most useful thing in the room. A disagreement points to a claim that needs evidence, which is exactly where Scout and Inspector do their work.
- Do more models make a Verdict more certain?
- Not by themselves. Confidence in a Verdict describes the strength of the evidence record, never the number of models that agreed.
Bring Argumont a Decision worth challenging.
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