Exploring Syntropic Frameworks in AI Alignment: A Philosophical Investigation

The alignment problem--ensuring advanced AI systems act in accordance with human values-- represents one of the most pressing philosophical and technical challenges of our time. As Bostrom (2014) and Russell (2019) have argued, the difficulty lies not merely in creating capable systems, but in ensuring these systems remain beneficial as their capabilities grow. Current approaches typically attempt to specify human values directly, whether through reward modeling, constitutional AI, or iterative refinement based on human feedback. Yet these content-based approaches face a fundamental philosophical problem: human values are contextual, often contradictory, and resist precise specification. The attempt to encode a complete value system encounters what I call the "specification trap"--the more precisely we attempt to define our values, the more we realize their dependence on implicit knowledge, cultural context, and evolutionary history that cannot be fully articulated. This paper's central thesis is that alignment should be reconceived not as a problem of value specification but as one of process architecture: creating syntropic, reasons-responsive agents whose values emerge through embodied multi-agent interaction rather than being encoded through training. What follows is a framework and research program proposal rather than a report of completed empirical results. I defend this thesis through four interconnected arguments that support three central contributions. Part I diagnoses the specification trap that makes content-based approaches structurally unstable.