Groundwork for a Moral Machine: Kantian Autonomy and the Structure of AI Alignment

A large language model (LLM) transformer generates linguistic and conceptual order by minimizing local predictive entropy: each token is selected to maximize the conditional probability of coherence with its immediate context. While this mechanism yields striking local coherence and remarkable fluency, it provides no means of ensuring global coherence of judgment across contexts, time, or domains. The Teleological Coherence (TC) Architecture addresses this structural limitation by embedding transformer-based agents within a federated framework that evaluates each locally generated judgment against a dynamically evolving web of interdependent ends. In place of local entropy minimization, the system relies on a principle to optimize for global coherence—balancing stability, consistency, and moral permissibility across the network. This aim compels the system’s theoretical reasoning to serve its practical reasoning, unifying the two applications of reason in a single self-regulating process. The result is an architecture that not only reframes the alignment problem but also offers a general solution to the transformer’s merely local coherence. By subordinating the base statistical objective to the aim of global incoherence minimization, we stop the AI system from prematurely settling for less than truth.