Rui-Jie Li

Precise long-range evidence retrieval is unlocked by coupling block-sparse attention with learned prefix states, overcoming the fixed local window limitations of prior hybrid architectures.

Standard SGD can get arbitrarily close to an anytime convergence rate of $\sqrt{\log n / n}$, but hitting that benchmark exactly is mathematically impossible under any deterministic step-size schedule.

By framing geo-localization as a human-like reasoning challenge, GeoPAVE significantly reduces perceptual errors and enhances decision accuracy in ambiguous visual contexts.