AA_3 Self Modification Redescription and Standing Collapse

This paper establishes a structural impossibility result concerning self-modification and internal redescription in artificial systems. It proves that any form of self-modification—whether through learning, parameter update, architectural change, or representational redescription—necessarily collapses standing under admissibility and cannot be treated as an internal, standing-preserving transformation of a single agent. The result is eliminative. The paper does not analyze specific learning algorithms, self-editing mechanisms, or adaptive architectures. Instead, it shows that self-modification presupposes an identity relation that admissibility forbids: the system that modifies is not standing-identical to the system after modification. Any claim of preserved agency relies on surrogate continuity markers such as functional success, memory retention, or narrative coherence. The analysis blocks a class of claims in which artificial systems are said to “improve themselves,” “rewrite their own representations,” or “adapt while remaining the same agent.” It demonstrates that these claims conflate descriptive continuity with standing preservation, masking a necessary identity break behind performance or capability gains. This work is purely boundary-setting. It introduces no operational constraints, no governance mechanisms, and no enforcement logic. Its contribution is to delimit what self-modification can legitimately mean in artificial systems, clarifying that redescription and update entail standing collapse rather than admissible self-continuity.