Seed2Succeed: Test-Time Scaling Needs Breadth in Antibody–Antigen Structure Prediction

Test-time scaling in antibody-antigen structure prediction offers a route to improve its accuracy, but its benefits depend on how prediction budgets are allocated. We present Seed2Succeed, a systematic study of seed breadth and sampling depth in AlphaFold3 using 113 FoldBench complexes and 2.26 million predicted structures. For each target, we compare 1,000 seeds $\times$ 10 diffusion samples with 10 seeds $\times$ 1,000 samples at a matched budget of 10,000 structures. We jointly analyze predicted epitope and paratope diversity, local structure diversity and global structure diversity. On 160 interfaces from 104 targets, broader seeding increases low-quality success rate from 43.1% to 60.0%, high-quality success rate from 16.3% to 25.0%. Broader seeding also expands contact coverage, while coordinate dispersion in the complementarity-determining regions remains nearly unchanged at full budget. However, additional sampling can reduce confidence-selected success, and full-pool oracle discovery numerically favors depth. Cluster analysis reveals structural populations with similar interface confidence but markedly different docking quality. These findings support seed breadth as a practical starting point on this benchmark and show that effective test-time scaling requires jointly considering candidate discovery, structural exploration, and ranking reliability.