Xinyu Zhang

The page layout of a disk-resident graph vector index decides how many SSD pages a search reads, yet layouts such as Starling and MARGO are computed from the graph and the vectors and use no query logs. HyperPage treats the expanded-node sets logged in one finished Search-R1 agent run as the workload, builds layouts for the uncached records under a declared memory budget, then replays validation steps to pick a layout and the split of the budget between hot records and a page cache. Across seven cache budgets on 1M and 21M passage indexes it reads 5.2% to 21.2% fewer pages than MARGO, with the largest gain when the cache is small.

The page layout of a disk-resident graph vector index decides how many SSD pages a search reads, yet layouts such as Starling and MARGO are computed from the graph and the vectors and use no query logs. HyperPage treats the expanded-node sets logged in one finished Search-R1 agent run as the workload, builds layouts for the uncached records under a declared memory budget, then replays validation steps to pick a layout and the split of the budget between hot records and a page cache. Across seven cache budgets on 1M and 21M passage indexes it reads 5.2% to 21.2% fewer pages than MARGO, with the largest gain when the cache is small.

Constrained vector search with per-category caps previously relied on a fixed visit budget or on stopping rules trained for average recall, neither of which guarantees an exact answer. CaST restricts stopping rules to a monotone class over a declared interface, calibrates them with fixed-sequence binomial tests, and derives a lower bound on the class-optimal cost via Lagrangian weak duality. On two datasets of one million items each, its replayed cost is at most 1.132 times the class optimum for risk levels 0.03 to 0.10 at 95% confidence, and at a 0.05 risk target its mean latency is lower than every other calibrated rule timed.

Constrained vector search with per-category caps previously relied on a fixed visit budget or on stopping rules trained for average recall, neither of which guarantees an exact answer. CaST restricts stopping rules to a monotone class over a declared interface, calibrates them with fixed-sequence binomial tests, and derives a lower bound on the class-optimal cost via Lagrangian weak duality. On two datasets of one million items each, its replayed cost is at most 1.132 times the class optimum for risk levels 0.03 to 0.10 at 95% confidence, and at a 0.05 risk target its mean latency is lower than every other calibrated rule timed.