PAIR 2026 roundtable: who verifies AI-generated scientific findings?
PAIR Conference 2026, themed on the frontier of next-generation physical intelligence, was held in Shanghai on Sept 20–21, and its "next-generation scientific discovery" forum closed with a roundtable on how model outputs become trustworthy scientific evidence — cross-disciplinary verification, reproduction and applicability limits. Panelists said AI generates candidates far faster than experiments can check them: the best AI virtual-cell data covers only about 100 perturbed genes out of roughly 20,000 human genes, GNoME's ~2.2 million predicted crystal structures were criticized as largely simple extensions of known compounds, and A-Lab corrected its "new materials" wording after re-analysis of its phase identification. Their proposed principles: fix verification standards before experiments and independently of results, separate the generator from the verifier, use independent labs and cross-platform replication, and require results to be independent, prospective and extrapolative.
Generation capacity is running ahead of verification rules, so who verifies, against what standards and at whose cost is becoming a practical question for research management and industry decisions.