NOVA: Fundamental Limits of Knowledge Discovery Through AI

Computer Science > Artificial Intelligence [Submitted on 12 May 2026] NOVA: Fundamental Limits of Knowledge Discovery Through AI Salman Avestimehr, Ken Duffy, Muriel Médard Can AI systems discover genuinely new knowledge through iterative self improvement, and if so, at what cost? We introduce the NOVA framework, which models the common ``generate, verify, accumulate, retrain'' loop as an adaptive sampling process over a knowledge space. We identify sufficient conditions under which accumulated genuine knowledge eventually covers a finite domain, and show how their violations produce distinct failure modes: contamination, forgetting, exploration failure, and acceptance failure. We then analyze imperfect verification and identify a contamination trap: as easy-to-find knowledge is exhausted, the model mass assigned to new valid artifacts shrinks, so even small false-positive rates can cause invalid artifacts to enter the knowledge base faster than genuine discoveries. We clarify that Good--Turing estimation is a local batch-diversity diagnostic, not an estimator of the historically undiscovered valid mass that governs long-term discovery. Under a separate tail-equivalence assumption relating the model's effective discovery distribution to a Zipf law with exponent \alpha>1, we prove that the cumulative generation cost required to obtain D distinct genuine discoveries satisfies R_{\mathrm{cum}}(D)=\Theta(c_{\mathrm{gen}}D^\alpha), where c_{\mathrm{gen}} is the per-candidate generation cost. This scaling law quantifies asymptotic diminishing returns as the discovery frontier advances. Finally, we formalize human amplification through guidance, generation, and verification, explaining why expert input is most valuable near autonomous exploration barriers. Subjects: Artificial Intelligence (cs.AI); Information Theory (cs.IT) Cite as: arXiv:2605.15219 [cs.AI]   (or arXiv:2605.15219v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.15219 Focus to learn more Submission history From: Salman Avestimehr [view email] [v1] Tue, 12 May 2026 21:37:09 UTC (330 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.IT math math.IT References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)