Symfield
This paper presents Symfield V8.2, a directional field architecture for universal substrate computation that operates as governed infrastructure rather than open research framework.
Whitepaper
Symfield V7.5 presents the first empirically validated directional field architecture for non-collapse computation, addressing fundamental limitations in current AI systems through continuous field dynamics
AI
First Empirical Validation of Machine-Derived Governance Frameworks This publication documents the first successful development of autonomous AI governance protocols through multi-AI collaboration, representing a paradigm shift from externally imposed AI safety to collaborative governance
Whitepaper
Symfield V7: Directional Field Architecture for Non-Collapse Computation, presents a revolutionary computational architecture operating through continuous field dynamics rather than discrete state collapse. Formalizes directional potential integration, dimensional sequencing, empirical validation
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
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,
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,