ACTS: Controlling Evidence Acquisition Before Writing Questions for Search Agents

Training search agents for open-world problems requires high-quality long-horizon trajectories that remain scarce and costly. The bottleneck begins before teacher rollout: a synthetic question can appear semantically complex yet collapse to one lookup when its clues reside on a single page, while a larger evidence graph may add redundancy without requiring new retrieval. Existing synthesis methods control end-product properties such as hop count, graph size, or question-level difficulty, but not the acquisition process realized during task construction. This creates a search-demand gap between apparent complexity and realized acquisition. We introduce ACTS (Acquisition-Controlled Task Synthesis), which designs a task’s evidence-acquisition topology before writing its question. From construction-time retrieval, ACTS builds linked entity and document-navigation hypergraphs that record grounded evidence and the queries and documents that made it reachable. Complementary search policies expand a shared state, and a lexicographic max–min objective selects the branch subset that maximizes the weaker of its semantic and acquired-evidence scores, with mean quality and nonredundancy as later tie-breakers. ACTS separately chooses evidence for question writing and the next construction frontier, preserving exploration without exposing every observation to the writer. Generated questions are screened for answer leakage and no-tool shortcuts, then independently solved by a tool-using teacher. In matched construction, ACTS increases mean committed evidence sources from 4.50 to 9.69 while achieving a 70.17% teacher solve rate. Under anti-contamination evaluation, ACTS-trained models reach 67.61% on full BrowseComp and 52.13% on HLE-Text with SFT alone, demonstrating that acquisition-controlled synthesis produces effective supervision across diverse search settings.