HAH-WAS / Localization Gap

2026 · code, systems, text

People need a repeatable way to recognize when plausible machine output has replaced cultural knowledge with approximation.

The research layer presents generated claims or sounds alongside references, provenance, and a failure taxonomy; a human auditor then classifies what the system distorted, omitted, or fabricated. The interface layer translates that protocol into a playable reverse Turing test, training discernment rather than asking users to imitate a machine. HAH-WAS is documented as a methodology with prototype interface material; its public dataset, scoring definitions, and validation process remain next-stage work.

Cinematic Rail Type: 10-card paired rail
Method: claim or sound, reference evidence, human classification, and failure taxonomy
Public form: reverse Turing-test learning interface
Status: methodology and prototype documented; public dataset and validation pending