A Public Theory of Distillation Resistance via Constraint-Coupled Reasoning Architectures

TL;DR

This paper proposes a theoretical framework for reducing the transferability of capabilities in AI models through architectural constraints, aiming to improve governance and safety in knowledge distillation and model extraction.

Contribution

It introduces a novel constraint-coupled reasoning architecture that formalizes how internal stability constraints can diminish the value of distillation as a shortcut.

Findings

Framework formalizes distillation resistance via stability constraints

Defines a threat model for capability transfer risks

Provides testable hypotheses for future research

Abstract

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.