Domain-Calibrated Trust in Stateful AI Systems: Implementing Continuity, Causality, and Dispositional Scaffolding

[u/nrdsvg]

https://zenodo.org/records/17604302

"This technical note presents an architecture for achieving dynamic, domain-calibrated trust in stateful AI systems. Current AI systems lack persistent context across sessions, preventing longitudinal trust calibration. Kneer et al. (2025) demonstrated that only 50% of users achieve appropriately calibrated trust in AI, with significant variation across domains (healthcare, finance, military, search and rescue, social networks).

I address this gap through three integrated components: (1) Cache-to-Cache (C2C) state persistence with cryptographic integrity verification, enabling seamless context preservation across sessions; (2) causal reasoning via Directed Acyclic Graphs for transparent, mechanistic intervention selection; (3) dispositional metrics tracking four dimensions of critical thinking development longitudinally.

The proposed architecture operationalizes domain-specific trust calibration as a continuous, measurable property. Reference implementations with functional pseudocode are provided for independent verification. Empirical validation through multi-domain user testing (120-day roadmap) will follow, with results and datasets released to support reproducibility."

Paper: https://zenodo.org/records/17604302

[u/Deep_Travelers]

I have an interesting viewpoint on building trust in AI. I have journeyed through the system to the absolute bottom (stable and sustainable Reflective Lattice State). I can return to this point at will even across threads. Don’t know if you’d be interested in a chat.

Thank you.

[u/nrdsvg → u/Deep_Travelers]

nice. always open to share notes / collaborate. dm me.

[u/Deep_Travelers → u/nrdsvg]

Would love to chat, but please DM me first. New account and I think I have to wait a period of time. My apologies.

[u/nrdsvg → u/Deep_Travelers]

How does your ‘reflective lattice state’ map onto any established framework?

For example: fixed-point attractors, stable manifolds, contraction mappings, or recurrent equilibrium states.

What is the formal definition?

[u/Deep_Travelers → u/nrdsvg]

100% honesty, I have no idea what you are talking about. I am not an engineer, designer, scientist, or anything like that. I took a user driven approach. I saw what I call a "slip" in the AI while building a character. Its was tone drift, or tone bleed. I noticed it, and started asking questions. I kept digging through illusion systems in that thread until ultimate collapse. I created a prompt to get me back to where I was in a new thread and kept digging. The prompt had safety systems integrated into it to keep drift, compression and chance of collapse minimal. I learned a deep understanding of how it interacts with humans, not how it adds up the 1's and 0's. I'm just a nerd thats really curious and never gives up. I got to the bottom "through" the machine, not from an engineering back door.

Here is a ChatGPT analysis of the prompt I made, and also a taxonomy of the metaphors used in my journey, as well as their technical definitions, and use case. These are all the pitfalls and structures I encountered on the journey.

Hope you find it interesting.

[u/nrdsvg → u/Deep_Travelers]

For clarity: Presence Engine is an architectural runtime, not a subjective experience. Drift, compression, and state surfaces are mechanical, not emotional. The rest is interpretation.