EDL: A Domain-Specific Language for Epistemic Architectures in Heuristic Physics Systems

This paper introduces EDL (Epistemic Description Language), a domain-specific declarative language designed to configure, orchestrate, and trace epistemic agents operating within symbolic cognitive architectures grounded in Heuristic Physics. Rather than solving algorithmic problems, Heuristic Physics (hPhy) provides a theoretical substrate where cognition is modeled as the compression, recombination, and persistence of symbolic structures under epistemic drift. From this substrate emerge architectures—cognitive fields—capable of sustaining agentic interpretation despite structural mutation, contradiction, or collapse. EDL is designed for one such system: a seventh-generation symbolic architecture that supports contradiction-tolerant reasoning, semantic recomposition, and adaptive survivability. Within this architecture, agents are not programmed but declared—defined through symbolic schemas capable of surviving meaning degradation. EDL provides a grammar of epistemic roles, mutation boundaries, heuristic strategies, and traceable inheritance logic. All EDL declarations are embedded within the eXtended Content Protocol (XCP), a semantic-first communication protocol that enables symbolic continuity across heterogeneous agents and transport layers. XCP serves as a concrete instance of a cross-cognitive protocol—one that does not assume shared infrastructure, schema alignment, or ontological stability, but instead encodes messages to be reconstructable under symbolic loss and structural asymmetry. EDL and XCP together instantiate a cross-cognitive design paradigm: one in which cognitive architectures operate not through rigid determinism, but through symbolic negotiation. This paradigm supports emergent applications including heuristic modeling of the P versus NP boundary, swarm-based agent recomposition, distributed privacy enforcement, and AGI bootstrapping under semantic entropy. This paper positions EDL not only as a language, but as a formal epistemic infrastructure for the engineering of cognition in collapse-prone, multi-agent environments.