While zero-cost proxies evaluate neural architectures with minimal overhead, their ranking reliability frequently degrades across different search spaces. CoRA-NAS counters this instability through a two-stage approach: it establishes a static ranking prior by aggregating initialization proxies, then samples anchor models and extrapolates their early validation curves to propagate residual corrections via an ExtraTrees model. Requiring roughly 1% of full training compute without relying on ground-truth training labels, CoRA-NAS achieves mean Spearman correlations between 0.715 and 0.946 across four standard NAS benchmarks, selecting top architectures within 0.05% of the benchmark ceiling.
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