Can an 8B Model Meet a 35B Model's Acceptance Bar? Introducing Artificial Deterministic Intelligence

Branden Laskowski of Cortex Agentics Global published a preprint introducing Artificial Deterministic Intelligence (ADI), an architecture that separates probabilistic language-model reasoning from externally enforced deterministic authority over memory admission, identity, permissions, execution rights, verification, and release. In a first-party synthetic historical-memory study of 30 task designs repeated three times per configuration, a scoped 8B model met the same recorded acceptance contract as a scoped 35B model (90/90 accepted runs each), while a broader-context 35B configuration accepted only 46/90 runs with 43 truncated responses under its fixed output budget. The author cautions the results are first-party observations on a bounded task family and do not establish general equivalence between smaller and larger models.

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. 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? Artificial Deterministic Intelligence: Probabilistic Reasoning, Deterministic Authority Read the published research preprint: https://doi.org/10.5281/zenodo.23265200 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. ADI is the foundation for a planned series of supporting publications, targeting one evidence-reviewed paper per week. Upcoming research: Release timing depends on evidence review and disclosure approval. 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 https://github.com/brandenlaskowski7-bot/cortex-adi-research/tree/main/challenges/governed-memory 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: Branden Laskowski Cortex Agentics Global Bridging Humanity and Technology. Probabilistic Reasoning. Deterministic Authority.