NOVA Framework Reveals Fundamental Limits of AI Knowledge Discovery

A new research paper introduces the NOVA framework, which models the iterative self-improvement loop of AI systems as an adaptive sampling process over a knowledge space. The study identifies conditions under which genuine knowledge accumulation can cover a finite domain, and shows how violations lead to failure modes including contamination, forgetting, exploration failure, and acceptance failure. A key finding is the 'contamination trap': as easy-to-find knowledge is exhausted, the probability mass assigned to new valid artifacts shrinks, causing even small false-positive rates to introduce invalid artifacts faster than genuine discoveries. The paper also clarifies that Good-Turing estimation serves as a local batch-diversity diagnostic, not an estimator of historically undiscovered valid mass. The work was published on arXiv under identifier 2605.15219.