Semeion: A Systems-Level Defense Against Coordinated AI Swarms in Democratic and Commercial Decision-Making

Recent advances in large language models and autonomous agent frameworks have enabled coordinated AI swarms capable of generating, amplifying, and sustaining realistic input across digital systems. We argue that the primary threat posed by coordinated AI swarms is not persuasion, but the corruption of signal used by human and machine decision-making systems. We introduce Semeion, a speech-neutral, source-agnostic architectural defense that normalizes, weights, clusters, and gates incoming signal before it influences decisions. The architecture comprises signal normalization, independent multi-dimensional scoring with adaptive defense mechanisms, narrative clustering, weighted impact assessment, evidence-gated decision points, privacy-preserving PII redaction, and constrained AI summarization. Unlike content moderation or counter-influence strategies, Semeion preserves open input while enforcing evidence standards and auditability. We present a reference implementation and report preliminary evaluation results measuring resilience under adversarial signal pressure.