The choice of knowledge representation (KR) determines what a knowledge base (KB) can store and how that knowledge can be retrieved and reasoned over. Recent knowledge-graph (KG) retrieval-augmented generation (RAG) systems show that explicit symbolic structure can complement the implicit knowledge encoded in large language models (LLMs). However, KGs capture only part of a text’s linguistic meaning and often leave conceptual and temporal context implicit, leading to recurrent reasoning failures. Existing approaches address these gaps through task-specific extensions but do not provide a unified framework for coordinating conceptual, instance-level, and temporal structure. We envision the Knowledge State Protocol (KSP), which coordinates canonical KRs to make known dimensions of linguistic meaning explicit. By standardizing interoperability among KRs, KSP provides a common, extensible, and open foundation for structured agent memory and neuro-symbolic training across modalities and languages.