Artificial Deterministic Intelligence: Probabilistic Reasoning, Deterministic Authority
What if improving AI systems isn't always a question of building larger models, but of changing the architecture surrounding them?
I've published the first paper in a continuing research series exploring Artificial Deterministic Intelligence (ADI), an approach that separates probabilistic reasoning from externally enforced deterministic authority.
The research examines how memory admission, identity, permissions, execution rights, verification, and release can be governed independently of language-model inference.
The initial experimental evidence
In a first-party synthetic historical-memory study, we evaluated 30 task designs repeated three times per configuration.
| Configuration | Accepted runs |
|---|---|
| 8B model, scoped context | 90/90 |
| 35B model, scoped context | 90/90 |
| 4B model, scoped context | 72/90 |
| 35B model, broader context | 46/90 |
The broader-context 35B condition had 43 truncated responses under its fixed output budget.
On this bounded task family, the scoped 8B configuration satisfied the same recorded acceptance contract as the scoped 35B configuration.
This does not establish that smaller models are universally equivalent to larger models or prove a reduction in total hardware memory requirements.
It does raise an important engineering question:
How much operational responsibility can be moved from the inference model into a governed architecture while preserving required task performance?
Paper 1 — Published October 9, 2026
Artificial Deterministic Intelligence: Probabilistic Reasoning, Deterministic Authority
Read the published research preprint:
https://doi.org/10.5281/zenodo.23265200
The paper introduces the architectural framework, discusses first-party benchmark observations, identifies relevant prior work, and describes limitations requiring further investigation.
A Continuing Scientific Research Series
ADI is the foundation for a planned series of supporting publications, targeting one evidence-reviewed paper per week.
Upcoming research:
- Externalized Intelligence and Model Substitution
- Deterministic Worker Construction Before Inference
- Governed Shared Memory for Multi-Agent Systems
- Model-Independent Artificial Workers
- Temporal and Authoritative AI Memory
- Behavioral Governance of Artificial Workers
- Governed Persistent Student Memory for Educational AI
- Evidence-Calibrated Computational Persona
- Persistent Artificial Identity Across Model Generations
- Persistent Artificial Identity in Robotic Embodiment
Release timing depends on evidence review and disclosure approval.
Public Developer Challenge
We've also released the Cortex Governed Memory Challenge v0.1, with public synthetic specifications and behavioral fixtures for examining governed-memory behavior.
Challenge documentation:
https://github.com/brandenlaskowski7-bot/cortex-adi-research/tree/main/challenges/governed-memory
An Invitation to Engineers and Researchers
I'm interested in technical criticism, optimized comparison baselines, independent experimental designs, and alternative explanations for our findings.
The goal is to investigate whether explicitly governed AI architectures can support more accountable, efficient, and human-centered artificial intelligence.
Our findings are first-party observations, not independent replication or a general safety guarantee. Proprietary implementation details remain private.
Follow the complete research series:
https://github.com/brandenlaskowski7-bot/cortex-adi-research
Branden Laskowski
Cortex Agentics Global
Bridging Humanity and Technology.
Probabilistic Reasoning. Deterministic Authority.
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