Semantic Fidelity Failure Modes Series: Diagnostics for AI and LLM Systems

This component contains the Semantic Fidelity Lab AI Failure Modes series, a set of short technical papers within the broader Reality Drift Framework. The collection examines how AI systems can appear coherent, accurate, or high-performing while gradually losing alignment with meaning, intent, and real-world reliability. The papers focus on practical failure modes in large language models, RAG systems, embeddings, benchmarks, multi-agent workflows, and agentic AI systems. Key topics include semantic misalignment, evaluation misalignment, semantic compression error, cascading semantic drift, agent drift measurement, interpretation failure, stepwise inconsistency, and evaluation blindness. Together, SFL-01 through SFL-08 provide a focused framework for identifying where semantic fidelity breaks down in AI systems. The series argues that reliability cannot be measured by accuracy, retrieval success, task completion, or benchmark performance alone. It must also account for whether meaning is preserved across compression, retrieval, generation, reasoning, and multi-step transformation.