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NOVA: Fundamental Limits of Knowledge Discovery Through AI

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arXiv:2605. 15219v1 Announce Type: new Abstract: Can AI systems discover genuinely new knowledge through iterative self improvement, and if so, at what cost?

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Authors: Salman Avestimehr, Ken Duffy, Muriel M\'edard
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because NOVA: Fundamental Limits of Knowledge Discovery Through AI overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)". Matching terms: latin, under, good, alpha, rate, candidate, lora, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.15219

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