What if we trained a logic AI from absolute zero—without even giving it math or physics?

This idea (and most likely not an original one) started when I read the recent white paper “Absolute Zero: Reinforced Self-Play Reasoning with Zero Data”.

https://arxiv.org/abs/2505.03335

In it, researchers train a logic-based AI without human-labeled datasets. The model generates its own reasoning tasks, solves them, and validates solutions using code execution. It’s a major step toward self-supervised logic systems.

But it got me thinking—what if we pushed this even further?

Not just “zero data,” but zero assumptions. No physics. No math. No language. Just a raw environment where the AI must: • Invent symbolic representations from scratch • Define its own logic and reasoning structures • Develop number systems (base-3? base-12? dynamic base switching?) • Construct internal causal models and test them through self-play

Then—after it builds a functioning epistemology—we introduce real-world data: • Does it rediscover physics as we know it? • Does it build something alien but internally consistent? • Could it offer a new perspective on causality, space, or energy?

It might not just be smarter than us. It might reason differently than us in ways we can’t anticipate.

Instead of cloning human cognition, we’d be cultivating a truly foreign intelligence—one that could help us rethink nuclear fusion, quantum theory, or math itself.

Prompting discussion: • Would such an approach be technically feasible today? • What kind of simulation environments would be needed? • Could this logic-native AI eventually serve as a verifier or co-discoverer in theoretical science? • Is there a risk in letting a machine evolve its own epistemology untethered from ours?