SSRN
Preprint
2026-06-17
AIZ Limited
Cognitive Tri-State Dynamics Architecture (CTDA): A Technical Whitepaper on Machine Creativity and Scientific Hypothesis Generation
This whitepaper presents CTDA as a cognitive control layer above foundation models, knowledge graphs, tool use, and experimental environments. It emphasizes executable research loops, anomaly preservation, premise tracing, candidate recombination, and verification pathways designed to produce auditable hypothesis workflows in AI for Science.
Why it is included
Included as background material relevant to systems architecture, machine reasoning, and higher-level orchestration concepts that sit adjacent to infrastructure planning and AI deployment contexts.
Preprints.org
Preprint
2026-05-01
AIZ Limited
From Behavioural Offloading to Governance Responsibility: Social Behaviour, AI Governance, and Educational Reconstruction in the Age of Ubiquitous AI
This paper examines how widespread AI adoption shifts human behaviour, judgement, and responsibility. It argues that while AI can generate answers and recommendations, accountability for action remains with people and institutions, and proposes governance and education frameworks that emphasise verification and responsibility training.
Why it is included
Included as background material on AI governance, institutional responsibility, and the operating context in which AI infrastructure and AI system deployment are adopted.
engrXiv
Preprint
2026-04-12
AIZ Limited
A Biomimetic Dual-Brain Architecture for Robotics: Bridging Large Language Models and Reactive Control through Control Barrier Functions, Experience Memory, and Entropy-Guided Fine-Tuning
This preprint proposes a dual-brain robotics architecture that bridges high-level language reasoning with low-level reactive control. It uses contract-constrained planning, local safety filtering, embodied memory, and hardware-aware adaptation to improve safety, contract compliance, and cross-platform transfer.
Why it is included
Included as background material on AI systems engineering, control layers, embodied deployment contexts, and adjacent technical thinking that informs broader infrastructure discussion.