Belief Network Propagator
Ki · Reasoning
The Problem
Updating the local weather model belief without network propagation leads to isolated prediction errors.
The Operation
Under this ability, the model must identify key beliefs in reasoning chain; map dependencies, which beliefs support or depend on others. Identify which belief is most directly affected by new evidence or changed premise. Update that belief, then propagate through dependency network, determine how much each connected belief should shift. Check second-order effects where shifted beliefs trigger further updates. Identify tipping points where small additional evidence would cause large cascades, flag as critical sensitivities. The constraint: never update beliefs isolation.
The Structure
The reasoning structure is a cascade propagation where effects ripple through each stage of the analysis. Execution cycles until the evidence set is fully consumed.
If a belief revision is applied locally without propagating its implications through the connected belief network, network propagation was skipped.
Haki · Reasoning-Multi
Cross-Domain Suppression
In Haki mode, the API retrieves the primary ability first, then fans out to three synergy roles that compound its reasoning.
When retrieved in Haki mode, the primary ability is augmented with failure guards extracted from 3 abilities in different cognitive domains. Each guard blocks a specific reasoning failure the primary alone wouldn't catch. A self-check forces verification before output. The result is cross-domain coverage that no single ability can reach alone.
Appears in Use Cases