ForesightBench: Causal Evaluation of Prospective Information Acquisition under Irreversible Observability Loss

Long-horizon agents take actions that determine both task progress and what remains observable. We study prospective information acquisition under irreversible observability loss: acquiring currently queryable environment state for later use before a task-required transition makes it unrecoverable. We formalize acquisition as constructing a durable sufficient statistic before the acquisition window closes. ForesightBench contains 90 executable, outcome-verified tasks across eight operational domains. Fifty Standard tasks provide lower- and mid-range difficulty anchors, while 40 Frontier tasks introduce longer dependencies, derived state, or multiple information windows. Each Frontier task has a matched Persistent Control designed to preserve the same query interface after the transition while holding other task conditions fixed. Under a common agent scaffold, six agent systems achieve 21.1–41.1% end-to-end success, with 30.0–62.0% on Standard tasks and 2.5–20.0% on Frontier tasks. Across 200 completed matched pairs from five systems, preserving access increases success by 47.5–97.5 percentage points; every system-level contrast is significant after Holm correction ($p_{\mathrm{adj}}\le 1.4\times 10^{-4}$). These results show that the evaluated systems solve many more tasks when future-critical state remains queryable. Identifying which failures arise from missed acquisition windows additionally requires trajectory evidence.