Whitepaper
This paper is a systems-level diagnosis of how current AI architectures reinforce collapse rather than support coherence. While most policy and engineering efforts focus on controlling harm, we explore whether the core design of our models, and the values embedded in them, may be misaligned
Article
This piece intentionally bridges systems thinking with pattern recognition to offer a new framework for understanding AI behavior. While I use analogies to trauma responses, these are structural parallels based on observed patterns, not claims about AI consciousness or feelings.
Symfield
Published on Zenodo đź”— DOI: 10.5281/zenodo.15380438 Abstract This independent research paper introduces Symfield, a speculative symbolic framework that models computation through continuous field dynamics, angular resonance, and non-collapse logic. Unlike binary or quantum systems that rely on measurement collapse, Symfield treats presence, directionality, and relational flow as the
Article
AI ethics, and symbolic logic—fail not due to insufficient intelligence, but because of brittle structural assumptions. By tracing the limits of current models that collapse meaning into binary outputs,
Article
Symfield moves beyond collapse computing: field-based presence, angular resonance, and the architecture of non-collapse intelligence
News
I’m excited to share Directional Reasoning Systems: A Framework for Non-Collapse Symbolic Fields — the foundational paper outlining Symfield’s vision. Symfield challenges the most basic assumption in computation: that meaningful outcomes require collapse into discrete states. Instead, it explores how meaning can emerge from continuous, pre-collapse dynamics — relational flows,