SCIENCE

A holistic model of knowledge.

Uncertain Systems is grounded in a physical view of learning: knowledge configuration space, proximity as the measure of knowing, and education technology as a path toward transformation with less wasted effort.

Our thesis

We need a measurement system based on something abstracted away from pure result samples.

Flywire — problem-solving signals mapped into knowledge space — Source: flywire.ai

The Hypothesis

We cannot map a brain, biomarkers, or a fancy predictive model. Cognition is too complex to model that way. As a proxy, we watch how they solve problems, turn those signals into a mathematical space, and compare them to regions that correspond to the target knowledge we are validating.

Epistemic policy

Epistemic foraging is the policy: we search for information that reduces uncertainty about knowledge configuration, rather than chasing the reward of a correct test answer.

Proof of Work proxy

Accredited expert work as the signal we can capture.

Configuration space

Beyond the brain — tools, workplace, applied context.

High-dimensional embeddings with labeled knowing-X regions

Distance to “knowing X”

Embeddings + labeled regions instead of pass-rates.

INFLUENCE

Learning is reducing uncertainty.

Our point of view on learning is heavily influenced by the Free Energy Principle. Adaptive systems that persist act as if they minimize surprise: variational free energy is an upper bound on surprise, and prediction error is the signal that drives belief updates and action. Learning, in that picture, is reducing uncertainty about the hidden causes of what you encounter.

That is why the platform is named Uncertain Systems: we treat learning as reducing uncertainty about a useful knowledge configuration, not as accumulating test scores.

The Free Energy Principle is an influence on this point of view. We do not claim that Uncertain Systems is Friston's full formal theory, and we do not claim completed FEP empirical results.

POLICY

Foraging for information, not chasing scores.

Epistemic foraging is an active search for information to reduce uncertainty about an environment, rather than immediately chasing rewards. In Karl Friston’s active-inference account, actions carry epistemic value: information that shrinks uncertainty. We treat learning technology as a forage for that information.

On Uncertain Systems, Think Aloud Protocol probes, interruptions, knowledge maps, and Proof of Work traces are the forage: they search for signal about what is actually held.

Epistemic foraging is an influence on this point of view. We do not claim that Uncertain Systems is Friston’s full formal theory, and we do not claim completed active-inference empirical results.

Knowledge Configuration

The full physical state of a human brain at a specific point in time.

Every moment of thought, memory, and skill lives in a unique configuration of neural activity. Understanding learning means understanding how one configuration relates to another — not just what was answered on a test.

Knowledge = Proximity

A useful configuration is close enough to retrieve, apply, and transform.

Knowledge is not a binary flag. It is how near your current brain state is to a configuration where you can reliably retrieve, apply, and transform what you need. Closeness — not completion percentage — is the meaningful signal.

Learning = Transformation

Learning is movement through configuration space, ideally with less wasted effort.

To learn is to move from one configuration toward another useful one. The goal of educational technology should be to shorten that path — reducing wasted effort while preserving depth of understanding.

Non-Invasive Path

Start with software attention loops, then add world models, stimulation, and biofeedback.

We begin with software: attention loops, Socratic questioning, and proof-of-work verification. Over time we layer world models, non-invasive stimulation, and biofeedback — building toward self-driving learning without asking humans to burn proportionally more energy.

Research

Methods & planned experiments

Academic working papers on knowledge tomography, how we externalize cognition as Proof of Work, and how we plan to embed that data into a Map of Knowledge toward knowledge induction.

Working paper · 2026

Knowledge Tomography White Paper

A methods white paper defining knowledge tomography as methodologies that prompt human and agentic entities to reproduce their state of knowledge, framed against the ultimate goal of knowledge induction technology, with a planned study validating TAP as an initial tomography tool.

Read the white paper