In harness scaling, the transferability of execution controls follows a model-distance hierarchy where the transferable artifact becomes progressively more abstract as architectural and task distance increase:

1. Frozen Transfer Within a Model Family: Closely related model generations or variants (such as GPT-5.5 to GPT-5.6) share recurring execution failure modes. This allows an exact, frozen control profile—including concrete states, activation policies, routing logic, pre-commit checks, and repair routines—to transfer successfully without any target-model modifications.
2. Adapted Transfer Across Model Providers: Crossing provider boundaries (such as OpenAI to DeepSeek) introduces distinct base-agent behavioral variations, causing direct frozen transfer of concrete profiles to fail or cause slight regressions. However, transfer succeeds at an architectural and methodological level: the generic runtime, high-level runbook structure, golden rules, and failure-analysis loop remain fully reusable, requiring only lightweight adaptation of concrete practices to the target model's residual failure distribution.
3. Task-Side Generalization Across Distributions: Across heterogeneous task families, transfer operates at the level of principles and mechanisms—specifically the methodology for identifying, locating, and enforcing sparse, consequential execution boundaries.