Interestingness as an Inductive Heuristic for Future Compression Progress

The paper's sharpest contribution is not the concept of interestingness itself, which has circulated informally in active learning and curiosity-driven RL for years, but the specific claim that expected future compression breakthroughs correlate exponentially with discovery recency, a quantitative relationship that, if it holds empirically, gives curriculum designers an actual signal to optimize rather than a vague intuition.

This connects most directly to the in-context learning work covered the same day ('In-Context Learning for Data-Driven Censored Inventory Control'), which grapples with a related problem from the applied side: how a system should allocate attention across learning opportunities when data collection is itself shaped by prior decisions. That paper solves it through meta-training and generative imputation; this paper asks whether a principled measure of interestingness could guide that selection process upstream. The broader thread across recent coverage is a quiet shift toward self-directed learning architectures, systems that do not just respond to training data but participate in choosing it.

The exponential recency claim is the load-bearing assertion here. Watch whether any group publishes empirical validation on a standard curriculum learning benchmark within the next six months; without that, the framework remains a compelling theoretical sketch rather than a deployable heuristic.