My work typically includes:
I do not optimize isolated workflows.
I redesign the structural conditions under which coherent decisions become possible.
A high-resolution examination of where your system’s decision logic, behavior, structure, and internal coherence begin to diverge — even if everything still appears to work.
Real-world examples of cognitive system design — where structure, logic, and human behavior align into a coherent operating model.
These projects are not disconnected ideas. They are applied examples of the same structural logic across service operations, developmental systems, logistics, and AI-integrated environments.
These projects show the visual and interaction layer of my work: websites, product interfaces, booking flows, landing pages, dashboards, and structured user experiences. The focus is not only on how the interface looks, but on how the structure supports clarity, orientation, and decision-making.
A cognitive operating system that:
– measures expertise instead of availability
– models true human capacity and workflow stress
– distributes work by relevance, not empty slots
– stabilises environments where mastery actually matters
Designed to make complex, expert-led services flow with precision — automatically.
CBSA — Cognitive Behaviour System Architecture
Preprint · DOI · Zenodo
A decision-coherence framework for AI systems that must remain interpretable, stable, and human-aligned under uncertainty.
→ Read the paper
CFT — Constitutional Framing Theory
Preprint · DOI · Zenodo
A framework for governing how AI systems frame uncertainty, define decision space, and determine what can be treated as a valid response.
→ Read the paper
Weighted Coherence Model: A State-Based Alternative to Priority-Driven Cognition
Preprint · DOI · Zenodo
A model of cognition in which decisions emerge through coherence stabilization across competing internal weights, rather than through isolated choice alone.
→ Read the paper
Error Is Not the Problem: A Coherence-Based Reframing of Failure in Complex Human–AI Systems
Preprint · DOI · Zenodo
A framework showing that error is often not a discrete failure, but the result of how systems frame, interpret, and classify deviation.
→ Read the paper
A Self-Reflective Cognitive Architecture for Human–AI Systems
Preprint · DOI · Zenodo
An architecture for AI systems that can recognize interpretive limits, preserve uncertainty, and refrain from action when coherence is not yet established.
→ Read the paper
Coherence Thinking – AI Stabilization of Unstable Human Meaning Space and the Structure of Self-Reinforcing Loops
Preprint · DOI · Zenodo
A framework for understanding how, in the human–AI interaction space, partial or unstable human meaning can become coherently stabilized into self-reinforcing loops.
→ Read the paper
If your system appears functional but becomes unstable under scale, automation, or coordination pressure, that is rarely a performance issue. Let’s look at the structure underneath it.
For collaboration, advisory, or project inquiries — reach out directly: hello@gyulajaradi.hu
Or send me a quick message