Research
Questions before conclusions.
Research into memory, cognitive architectures, intelligent agents, interpretability, and software that remains coherent as it evolves.
Artificial Intelligence and Memory Architectures
DREAM
DREAM is a model-independent architecture for persistent, opt-in, and governed episodic memory in artificial intelligence agents. It investigates how long-running systems can preserve useful continuity without allowing accumulated memories to become an uncontrolled source of outdated assumptions, semantic drift, recursive reinforcement, or false coherence. The architecture evolved from adaptive retention and lifecycle management toward explicit governance of memory influence. Its central principle is that retaining or retrieving a memory does not automatically authorize that memory to influence current reasoning. DREAM therefore treats persistence, retrieval, authorization, consolidation, correction, and deletion as distinct architectural responsibilities. The current version includes a Python reference implementation, formal data contracts, automated tests, a local governance playground, controlled synthetic benchmarks, component-ablation experiments, and auditable decision traces.
Software Architecture and Sustainability
Legacy-First Design
Legacy-First Design (LFD) is a conceptual software architecture methodology that treats time as a primary design constraint. It distinguishes between permanent elements, which preserve the identity and essential behavior of a system, and transient elements, such as frameworks, databases, protocols, and infrastructure, which must remain replaceable. Through six fundamental principles, LFD proposes that software should preserve meaning, resist technological obsolescence, evolve without losing its identity, remain understandable to future maintainers, and continue to make sense during prolonged periods without maintenance. Rather than replacing established approaches such as Clean Architecture, Domain-Driven Design, or Hexagonal Architecture, LFD provides temporal criteria for deciding what must endure and what may change.
Artificial General Intelligence and Cognitive Architectures
MQ-AGI
MQ-AGI is a neuroinspired, modular, and quantum-extended architecture for Artificial General Intelligence. It proposes an alternative to increasingly large monolithic models by separating perception, specialized reasoning, memory, integration, metacognition, and safety into components with explicit responsibilities. The architecture combines Domain Expert Networks, a Global Integrator Network, hierarchical long-term memory, distributed orchestration, and a quantum-inspired optimization core. Specialized experts generate parallel hypotheses, while the integrator constructs a coherent working representation, resolves conflicts, coordinates memory retrieval, and supports deliberative reasoning. Rather than sending large volumes of raw data to a quantum processor, MQ-AGI reserves quantum or quantum-inspired computation for bounded combinatorial problems such as expert-coalition selection, hypothesis optimization, and constraint satisfaction. This design reduces exposure to the quantum data-loading bottleneck while establishing a possible development path through classical emulation, NISQ-era optimization, and future quantum-native components. The research includes an architectural proposal, a mathematical formalization based on QUBO and Ising Hamiltonians, category-theoretic integration, and active inference, as well as a critical theoretical evaluation of its scientific coherence, engineering limitations, and long-term feasibility.
Interpretable Machine Learning and Explainable AI
HGC-Net
HGC-Net is a hybrid semantic bottleneck architecture that combines supervised, human-interpretable concepts with unconstrained latent dimensions. It investigates whether selective semantic supervision can preserve auditability while recovering most of the predictive performance lost by strict Concept Bottleneck Models.