Epistemic Horizons
Predicting Reality
with Exponential Accuracy
“An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed — for such an intellect, nothing would be uncertain, and the future just like the past would be present before its eyes.”
— Pierre-Simon Laplace, 1814
We are building that intellect. Not as a metaphor — as an architecture that recursively improves its own ability to model, predict, and understand the world. Each cycle of self-improvement brings us asymptotically closer to perfect prediction.
The Architecture That Learns to Learn
Vertically Integrated
Not a wrapper around existing models. A full-stack cognitive architecture — perception, memory, reasoning, and planning unified under a single framework with a shared objective function.
Recursively Self-Improving
Every component is a target for optimization, including the optimization process itself. The system inspects its own performance, identifies structural weaknesses, and rewrites its own cognitive strategies. Improvement compounds.
Grounded in Theory
Built on Cognitive Utility Theory, Bayesian inference, and systems theory — not vibes. Every architectural decision follows from first principles about how an agent should allocate computational resources to maximize predictive accuracy.
Validation accuracy on rigorous paper-review prediction — correctly modeling expert evaluation of research quality across multiple dimensions, achieved by a mid-tier model in 101 generations of recursive self-improvement.
Logo Exploration
Four candidates for the Epistemic Horizons identity. Each encodes a different facet of the project.
Lorentzian · a = 0.2
Your exact specification: y = a³/(x²+a²), narrow needle peak with symmetric asymptotic tails.
Lorentzian · a = 0.6
Same function, wider parameter. The bell shape is more visible — smoother, more recognizable as a horizon profile.
Lorentzian · Gradient Fill
a = 0.4 with a gold gradient fill under the curve. Adds depth and luminosity — the horizon glows.
Dual Lorentzian
Two offset peaks — knowledge and meta-knowledge. The valley between encodes recursive self-reference.
This is active research, not a product roadmap.
Every experiment, failure, and insight is documented in the open.
Read the Research Log →