TECA: Twin Expandable Connector Architecture proposes a novel architectural framework for AI systems that grow organically, specialize coordinately, and evolve toward distributed intelligence, while maintaining constitutionally enforced safety constraints. Current large language model architectures are fundamentally static:once trained, their structure is fixed and their parameters immutable. TECA addresses this limitation through five coordinated mechanisms: Expandable Connectors (EC): structural modules that automatically generate adjacent empty connectors upon activation, enabling organic growth analogous to biological synaptic plasticity. Twin Model Architecture with Global Connector Namespace: multiple model instances sharing an identical base architecture and a single shared connector space, enabling natural inter-model communication without explicit translation protocols. Cross-Twin Connector Propagation Protocol (CTCPP): a formal mechanism for distributing structural capacity across twin models, enabling coordinated divergent specialization. Adaptive External Router Brain (AERB): an intelligent coordinator that learns which twin is most competent for each domain and synthesizes multi-domain responses. Constitutional Layered Security Model: three immutable security layers integrated by design, with human supervisors rather than operators. The framework's central hypothesis, the Emergent Distributed Brain, conjectures that beyond a threshold of structural interdependence, the TECA system exhibits collective intelligence surpassing the sum of its parts. Implementation pathways are grounded in current technology (LoRA adapters, standard transformer architectures). All ideas, architectural concepts, and theoretical framework are original contributions of the author.
Nicolas Servalli (Wed,) studied this question.