Raw thought is messy and non-linear. To make it computable, we must structure it into discrete units of Rationale—claims that can be linked, weighed, and traced back to their origins.
This domain explores how to capture, link, and weigh arguments (Support, Attack, Evidence) to build a robust Knowledge Graph that resists bias and encourages intellectual honesty.
The "Void" system should prioritize epistemic transparency over raw computational speed.
Transparency builds trust, which is the primary metric for long-term user adoption in cognitive tools.
User surveys (N=500) indicate "Fear of Black Box" as top churn reason.
However, real-time feedback loops require <50ms latency, which deep introspection layers may compromise.
Engineering benchmarks show introspection adds ~120ms overhead per query.
A breakdown of the UI affordances that enable this structured thinking.
Analysis of the atomic unit of argumentation. Notice how color and shape afford rapid scanning.
While efficiency increases, the loss of human oversight creates a "Black Box" risk that violates our core transparency principles.
Analysis of recursive nesting. The indentation and guide lines afford 'Drill-Down' behavior.
The "Void" system should prioritize epistemic transparency over raw computational speed.
Transparency builds trust, which is the primary metric for long-term user adoption in cognitive tools.
User surveys (N=500) indicate "Fear of Black Box" as top churn reason.
However, real-time feedback loops require <50ms latency, which deep introspection layers may compromise.
Engineering benchmarks show introspection adds ~120ms overhead per query.