Technical documentation (CMPSBL Substrate v5.5.0)
Abstract
Cognitive Operating System for AI Models and Apps This update introduces the full technical documentation set for CMPSBL v5.5.0, including module specifications, API references, system architecture, performance validation methodology, and real-world runtime evidence. CMPSBL is a cognitive operating system designed for multi-model AI systems, providing persistent memory, observability, resilience, defense, and self-improvement cycles through a structured substrate architecture. The v5.5.0 release documents the complete module suite: BRAIN (persistent memory store) DECODE (epistemic interpreter) CORTEX (planning and cognitive execution) DREAM (reflective synthesis) DEFENSE (threat detection & mitigation) VISION (telemetry & observability) MODERNIZER (self-improvement proposals) NEXUS (multi-provider model routing) SYSTEM (runtime governance) CORE (substrate kernel) ACCESS (integration & permissioning) RIPPLE (state propagation) CORTEX EXT. (evaluation & deployment) This release also includes: • validation methodology • performance benchmarks • runtime evidence logs • glossary & bibliographic references • licensing considerations CMPSBL operates as an autonomy substrate rather than an agent framework, enabling AI systems that persist, evolve, improve, and coordinate over time. It is intended for research, simulation, enterprise, defense, and cognitive infrastructure studies. Licensing & Opportunity The CMPSBL substrate is currently available for licensing to research labs, universities, enterprise R&D groups, and computational cognition teams, and we are open to acquisition discussions for groups seeking to integrate cognitive OS capabilities directly. For demos, licensing, or acquisition inquiries, please contact through the site at: Official Website https://CMPSBL.com Created By: Kenneth E Sweet Jr | PromptFluid@gmail.com
Fields of Study & Subjects
Psychiatry
Mental Health
Neuroscience
Cognitive Computing and Networks
Artificial Intelligence Applications
Publication details
- Journal / source
- OSF Preprints (OSF Preprints)
- Publication date
- 1 January 2026
- Document type
- Preprint