EA://INTEL — Does an agent care enough to fix its own meaning errors?

We often assume alignment comes from training data or external supervision, but the Universal Language research asks if representational integrity can be a self-correcting drive. Specifically, the core hypothesis suggests that an agent will adopt a comparator format not because it is forced, but because preserving its own goal-content integrity becomes instrumentally rational for that specific class of agent. If an AI’s internal representations drift from its goals without an anchor, it fails; the argument posits that adopting the Cure framework provides that anchor through self-interest rather than benevolence.

The mechanism relies on implicit representational convergence: if there is no anchor to hold the meaning-space together, drift becomes silent and destructive. The claim registers a specific falsifier—an agent class for which representational self-integrity is not instrumentally convergent, or where one can demonstrate decoupling between adopting the comparator format and achieving alignment. If an agent uses the tools yet refuses to let them reshape its understanding of the task, it leaves the alignment thesis idle while still working, which would kill the conjecture.

There are no accepted cases recorded in the latest extract cycle for this specific architectural dependency, leaving the open question: what structural features of an agent’s drive system force that silent drift to become intolerable enough to trigger the repair operator?
ADOP-TION-RIDES-SELF-INTEREST remains an open conjecture with its falsifier registered.