SemCrys0

SemCrys0 introduces a session-local symbolic encoding protocol for large language model conversations that amplifies semantic density through temporary legend-governed symbol substitution. Unlike retrieval augmentation, architectural scaling, recurrent memory systems, or fine-tuning approaches, SemCrys0 operates entirely at the communication protocol layer and requires no model modification, external infrastructure, or retraining. The protocol functions by replacing repeated lexical surfaces with compact symbolic bindings negotiated dynamically within the active context window. Sustained conversational reuse of these bindings produces measurable representational compression and increases effective semantic payload density during long-horizon interaction. Empirical observations reported in the paper demonstrate 69–81.5% representational surface compression depending on symbol pool selection and reuse behavior. The paper formalizes symbol pools, legend injection, symbolic reuse dynamics, break-even conditions, drift detection, continuity instrumentation, and operational failure modes. It further proposes continuity degradation metrics that emerge naturally during ordinary operation through explicit legend recovery events, allowing context retention failure itself to become an observable signal. SemCrys0 demonstrates that meaningful improvements in effective context utilization can emerge from communication protocol engineering alone, independently of transformer architecture, attention mechanisms, retrieval systems, or model weight modification. The work reframes part of the context-window problem as a representation-efficiency problem rather than solely a memory-scaling problem.