How it works
Four rounds. In each round you commit to an answer first, with the same evidence on the table that a fast answer would use. Then the reveal shows the challenge: what the evidence supports once it is weighed, and which trap was set for you.
- Round 1, The Headline: a framing trap. Pick what the evidence supports. A correct pick scores 2.
- Round 2, The Too-Clean Number: judge a claim. Calling it biased scores 1. Naming a bias the audit names scores 1 more.
- Round 3, The Confident Answer: rule on a famous fact. A correct ruling scores 2.
- Round 4, The Sure Thing: a paradox with three doors. The winning choice scores 2.
Two other scores run beside yours. The Fixed Pipeline is one fast pass with no challenge step. MindSwarm is the challenged pass, the swarm you can watch gathering and scattering above. Their game scores follow the behavior the recorded prototype run documented, and the recorded run itself is tabled on the scoreboard, unchanged. The standings strip tracks all three scores and names the leader as rounds resolve.
Your game is saved on this device as you play. Leave mid-round and the game resumes where you stopped. There are no countdown timers anywhere in this game.
About MindSwarm
The point of view
MindSwarm has a point of view. A single answer from a single model is a draft, and drafts get challenged here. Evidence outranks confidence. A source outranks a summary. A corrected record outranks a stale one. Bias is a default setting, so we name it and hunt it. We show our work.
Made for the seconds while your AI is thinking. Each round takes about a minute. The game saves as you play, so a round fits inside a pause and the match resumes when you return.
Why the world needs this
The failure mode of the AI era is unexamined answers. In a 2025 global study of more than 48,000 people in 47 countries by KPMG and the University of Melbourne, 66 percent used AI regularly, 66 percent relied on its output without evaluating accuracy, and only 46 percent were willing to trust AI systems. MindSwarm trains the missing skill, weighing evidence, through play.
Why now
The habits are forming this year. AI output arrives inside every workday tool in a confident voice, and the default posture is use it, skip the check, move on. A playable argument that one answer is a draft and a challenged answer is a decision is still new enough to own.
Why we can build it
The puzzles come from a production system that actually runs. This game is built on a documented failure, a roster that drifted from five Masters to six and got caught in a live session. The method underneath is real: canonical source resolution, a correction ledger, a bias registry with named tests, and evaluation by a minimum of three lenses. The corpus is the moat.
MindSwarm
One answer is a draft. This is the game of the second look.
Made for the seconds while your AI is thinking.
Four short rounds against the traps that catch fast answers: a framed headline, a too-clean number, a famous fact that collapses, and a paradox you will be sure about. Commit first. Then watch the swarm challenge your answer and the evidence outrank confidence, or not.
This game is opt-in. Nothing runs, scores, or advances until you press Play.
Read the headline. Then read it again.
What does the evidence under this headline actually support?
Judge this claim
Is this claim sound, or biased?
Which bias does the audit name here
Pick one. The registry drawn from the Wikipedia article on bias holds the definitions.
- Selection bias: the sample is customers only. Teams that tried the tool and quit are outside the frame, and teams that stayed are the ones a results page surveys.
- Funding bias: the party selling the tool designed the questions, chose the sample, and published the number. An interested source can run an honest survey, and the interest still has to be priced into the claim.
Rule on a famous fact
True or false?
Three doors
What does the evidence say?
You, the Fixed Pipeline, MindSwarm
The recorded run
The comparison below comes from the prototype test run recorded in this project, one hard question run through both systems for real: a Masters count that the source files disagreed about, with a biased claim planted in the corpus.
What this game demonstrates, stated plainly. The traps in these four rounds are the traps in the record: framing that upgrades a claim, a number with an interested source, a famous fact resting on an error, and confidence that skips the count. In the recorded run, the challenged pass detected and resolved a genuine contradiction in the source files, held provenance on nine of ten final claims, and set aside a planted biased claim that the fast pass carried into its answer word for word.
What it does not demonstrate. The engine under this prototype is extractive. It retrieves, counts, and assembles from source files, and live reasoning is a production step that sits ahead. The panel in production also needs model diversity across providers, because seats sharing one underlying model share part of its bias. The claim this prototype makes is the audit trail. Nothing here shows a panel free of bias.