Position: General Alignment Has Hit a Ceiling; Edge Alignment Must Be Taken Seriously

2026-02-23

AI Summary

This work addresses the structural limitations of prevailing alignment approaches—such as value flattening, inadequate normative representation, and epistemic blind spots—when confronted with pluralistic values, multiple stakeholders, and irreducible uncertainties. It proposes a novel “marginal alignment” paradigm that transcends the conventional scalar reward framework by integrating multidimensional value modeling, democratic representational mechanisms, and cognitively interactive design, thereby reconceptualizing alignment as a dynamic, full-cycle process of normative governance. The study develops a comprehensive three-phase, seven-pillar framework spanning data collection, training, and evaluation to systematically expose the bottlenecks of generic alignment methods and offers an actionable pathway that harmonizes technical implementation with institutional coordination, opening new directions for AI alignment in complex sociotechnical systems.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Mechanism Design
• Philosophy and Ethics of AI: Morality & Value-based AI

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Large language models are being deployed in complex socio-technical systems, which exposes limits in current alignment practice. We take the position that the dominant paradigm of General Alignment, which compresses diverse human values into a single scalar reward, reaches a structural ceiling in settings with conflicting values, plural stakeholders, and irreducible uncertainty. These failures follow from the mathematics and incentives of scalarization and lead to \textbf{structural} value flattening, \textbf{normative} representation loss, and \textbf{cognitive} uncertainty blindness. We introduce Edge Alignment as a distinct approach in which systems preserve multi dimensional value structure, support plural and democratic representation, and incorporate epistemic mechanisms for interaction and clarification. To make this approach practical, we propose seven interdependent pillars organized into three phases. We identify key challenges in data collection, training objectives, and evaluation, outlining complementary technical and governance directions. Taken together, these measures reframe alignment as a lifecycle problem of dynamic normative governance rather than as a single instance optimization task.

Problem

• General Alignment
• value conflict
• plural stakeholders
• irreducible uncertainty
• structural limitation

Innovation

• Edge Alignment
• value pluralism
• scalarization critique
• normative governance
• epistemic interaction