Sentience: A Reflective Executable Language for Structured Thought and Memory in Cognitive AI Systems

This paper introduces Sentience, a domain-specific language for representing, executing, and reflecting on structured thoughts in cognitive agents. Sentience tokens are structured as abstract syntax trees (ASTs) with semantic embeddings, enabling introspective reasoning, memory persistence, and self-modeling over time. The system integrates DSL-based cognition with hybrid memory graphs and runtime execution, forming the basis of a new AI architecture: Structured Reflective AI (SRAI). The paper defines the language, token format, execution model, and memory system, and provides experiments demonstrating reflection, goal coherence, and identity stabilization.