The prompt had 9 reasoning layers:
Novel candidate generation (with invented names + simulated properties)
Bayesian priors per candidate — P(H), P(D|H), P(H|D)
Observational constraint mapping (CMB, Lyman-alpha, BBN, lensing)
Sensitivity analysis + phase transition detection
Conflict mapping between candidates
Epistemic Geometry — each hypothesis treated as a point in belief space, with regions of attraction, repulsion, and blind spots
Plausibility ranking with uncertainty intervals
Final synthesis
The system auto-decomposed it into 14 parallel sub-tasks via swarm execution. I typed the prompt and walked away.
No orchestration code. No manual chaining. No babysitting.
This is what the Tasks feature on TruthAGI.ai does — it takes a complex, multi-axis prompt and runs it as a coordinated agent swarm.
You can try out at: TruthAGI
The screenshot below is live output. That spinning loader is real.
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