A Public Theory of Distillation Resistance via Constraint-Coupled Reasoning Architectures
This work addresses a critical issue in AI governance by providing a public, trade-secret-safe theoretical foundation for distillation resistance, though it is incremental as it focuses on theoretical contributions rather than operational implementation.
The paper tackles the problem of reducing the risk of knowledge distillation and model extraction in frontier AI by proposing a theoretical framework that couples high-level capability to internal stability constraints, making distillation less valuable as a shortcut.
Knowledge distillation, model extraction, and behavior transfer have become central concerns in frontier AI. The main risk is not merely copying, but the possibility that useful capability can be transferred more cheaply than the governance structure that originally accompanied it. This paper presents a public, trade-secret-safe theoretical framework for reducing that asymmetry at the architectural level. The core claim is that distillation becomes less valuable as a shortcut when high-level capability is coupled to internal stability constraints that shape state transitions over time. To formalize this idea, the paper introduces a constraint-coupled reasoning framework with four elements: bounded transition burden, path-load accumulation, dynamically evolving feasible regions, and a capability-stability coupling condition. The paper is intentionally public-safe: it omits proprietary implementation details, training recipes, thresholds, hidden-state instrumentation, deployment procedures, and confidential system design choices. The contribution is therefore theoretical rather than operational. It offers a falsifiable architectural thesis, a clear threat model, and a set of experimentally testable hypotheses for future work on distillation resistance, alignment, and model governance.