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A single agent can disentangle true mechanisms from computational twin errors by jointly learning them, unlocking more accurate mechanistic discovery in complex biological systems.
A single diffusion operator can jointly predict intervention outcomes and select optimal measurements by treating decision quality as an interface property, outperforming Bayesian experimental design in cost-efficiency.
