Self-Improving World
the environment it acts in
A simulator of complex, critical, high‑GDP systems that improves its own fidelity, and knows what its fidelity is.
The loop
- 01 · Build
- Learn what happens next from logs and live outcomes.
- 02 · Grade
- Say what an outcome was worth, long before it arrives.
- 03 · Trust
- Measure fidelity against reality, and report it.
- 04 · Refresh
- Let real outcomes correct the world, then regenerate it.
What it owns
What happens next, what it was worth, and how much any of that can be trusted. In a verifiable domain all three are handed to you. Here every one of them is a research problem.
Why it can improve itself
The model generates environments, the agent acts in them, real outcomes correct the model, and the corrected model generates better environments.
Dynamics and signal improve from compute. Fidelity improves only from reality, and reality is rationed. That makes this the pillar with the weakest internal scoring and the highest consequence when it is wrong, which is why it gets built first rather than last.
What it looks like when it works
Policies trained in the world outperform policies trained on logs, and the gap holds up when a real experiment checks it. Then the world starts proposing which experiment to run next.