"Interesting things like intelligence love to show up in dynamic, evolving systems.
There needs to be an opportunity cost, and you don't see that in static systems.
The trouble isn't in creating dynamic systems, it's in creating the right dynamics."
- Justin Lietz, Founder & CEO
VDM anchors this in a proven Reaction–Diffusion baseline and a verified single‑site logistic invariant (Q_FUM). Current validations span hydrodynamics (LBM → NS), Memory Steering, and goal directed structural plasticity (GDSP + SIE).
These results are reproducible, with falsifiable scripts, logs, explicit acceptance criteria, and rigorous physics benchmarks.
This model trades scale and compute for time, where learning increases with time while scale and total cost of ownership decrease.
My philosophy from the beginning has been simple. Start from domain agnostic observations of nature's elegance. I saw how unpredictable systems that manage to balance chaos and order just right showed up everywhere. I noticed that it was not the chaos, nor the order that created the emergent phenomena. It was the space in between the interaction of these two concepts that seemed to produce the most interesting results.
The Void Dynamics Model is build entirely on this principle, voids create a kind of pressure, or a call to action. Either there is something to be filled, or something to remove. Rivers will let inefficient tributaries dry up to redirect flow to more efficient paths because there is no choice. This is a physics principle where systems evolve to minimize energy expenditure.
It may be more accurate to say that this is a model of opportunity cost in action more than it is a model of intelligence. It might just so happens that intelligence seems to always find itself strung up in opportunity cost.
Canonical leading‑order continuum model; Fisher–KPP front speed and linear dispersion validations with published acceptance criteria.
Per‑site logistic constant of motion with ΔQ ≤ 1e−8 numerical verification for the discrete on‑site law.
D2Q9 BGK with Taylor‑Green vortex and Lid‑driven cavity benchmarks; criteria specified for ν_fit vs ν_th and ‖∇·v‖₂.
Refractive‑index routing n(x,t)=exp[ηM(x,t)] with boundedness, linear step response, and target convergence acceptance tests.
Event‑driven growth/pruning and intrinsic valence (novelty/TD/habituation) integrated with sparse connectome runtime.
Void‑walker announcers, non‑interference guards, KPIs, and CI hygiene for deterministic, sparse‑first measurement.
Physics: Map emergent intelligence through Void Dynamics to the rigor of physics (RD and Q_FUM), advance the EFT/KG branch with publishable derivations, accurately predict behaviors of memory steering, further strengthen Tachyon Condensation derivation, and aim to maintain a transparent validation pipeline.
Intelligence Model: Complete my public release roadmap by integrating convergent reasoning with the existing divergent reasoning, upgrade the Universal Transduction Decoder / Universal Temporal Encoder to upscale undersanding and precision measurements of per-millisecond neural activity analysis, refine and maintain my philosophy for unidirectional, sparsity + emergence architecture, and attempt to drive the model to its limits in efficiency, reasoning, and safe autonomy.
We enumerate what has been validated and what criteria are used. The items below reflect reproducible results mapped explicitly to some area of the model's architecture, with explicit acceptance thresholds—no projections or marketing hype.
Leading‑order continuum model with Fisher–KPP front speed and linear dispersion validations meeting published acceptance criteria.
Exact single‑site constant of motion for the autonomous on‑site logistic ODE verified numerically to machine precision.
Operational reduction via D2Q9 BGK and Chapman–Enskog; validates Taylor‑Green vortex decay and Lid‑driven cavity divergence norms.
Refractive‑index routing n(x,t)=exp[ηM(x,t)] with boundedness, linear step response, and target convergence.
A consolidated index of derivations, benchmarks, and acceptance criteria will be published here. Until then, the summary document in the repository captures scope and current status.
Draft companion papers to be submitted to arXiv. Links below provide repository context and direct PDF access.