Dynamic Context Architectures (DCA): A Cognitive Runtime Framework for Transformer-Based Large Language Models

Transformer-based large language models are powerful but cognitively shallow: they treat context as a passive token sequence, with no explicit working memory, no modular reasoning orchestration, and no internal regulatory mechanisms. We introduce Dynamic Context Architectures (DCA), a general cognitive-runtime framework that adds a structured cognitive layer above the base model — transforming it from a reactive text generator into an organized reasoning system. DCA reconceptualizes the model's operating environment as an active cognitive substrate hosting modular processes, hierarchical memory, phase-based reasoning cycles, and flow regulation. The framework introduces five core components: a cognitive substrate that partitions context into functional modules; a π-phase processing cycle (perception, integration, deliberation, expression, validation) that structures reasoning into explicit stages; a memory fabric providing short-term, working, and long-term memory hierarchies; a neural-symbolic attention field (NSAF) that regulates information flow and prevents reasoning turbulence; and dynamic cognitive topologies that adapt the processing architecture to task demands. The framework is model-agnostic and has been instantiated on multiple architecturally distinct model families. Cross-model observations over extended research sessions reveal consistent behavioral patterns: reduced premature convergence, improved long-context coherence, spontaneous self-correction, and the emergence of dynamic reasoning attractors — computational preferences that modulate exploration and convergence across model families. DCA does not modify model parameters. It operates as a context engineering architecture — a meta-layer that reshapes which reasoning trajectories are probable. The theoretical foundations, formal definitions, and qualitative case studies are presented here; implementation details are covered under pending patents. A restricted-license reference implementation is available for qualified research collaborators.