Intelligence from Learnable Novelty
- Published
- Source
- arXiv
- Paper number
- 691
- Field
- Machine Learning
- arXiv ID
- 2607.18433
Key points
- It shows that the three forms of intelligence, data compression, universal computation, and adaptive behavior, all come from a single principle: maximizing learnable novelty.
- It recovers the Turing-complete rule 110 as the top one-dimensional cellular automaton, which is consistent with decades of complexity-ranking research.
- It achieves representation learning that separates MNIST digit classes without labels, which is fully unsupervised.
- When used as intrinsic reward in reinforcement learning, it improves performance in 9 of 10 environments.
- It has a mathematical structure that avoids both the noisy-TV trap and the dark-room trap.
Paper links
External research summaries. These are not HDATF publications or measured product results.