OmniMaster: Global Relations as the Unit of Verification in Deep Research

Deep Research increasingly relies on iterative search, evidence verification, and cross-source synthesis. Yet locally correct evidence does not guarantee a valid research result, since relations across evidence, sources, and outputs may still fail. We identify a failure mode, Relational Verification Collapse (RVC), in which relational requirements are weakened during decomposition, judgment, or aggregation, allowing local checks to pass while the underlying relation remains invalid. To study this problem, we introduce OmniMaster-Bench, which evaluates seven global relation types in two complementary settings, long-horizon open-web research and controlled multi-hop retrieval. Evaluations of frontier models reveal substantial remaining headroom, while strong performance on existing Deep Research benchmarks does not reliably transfer to global relational capability. We further develop relation-centered data synthesis and agent training. Relation-aware mid-training (MT) learns atomic capabilities for tracking relational states, while relation-preserving supervised fine-tuning (SFT) composes these capabilities into complete research trajectories. Without reinforcement learning, the resulting agents enter the frontier range on OmniMaster-Closed, narrow the gap on OmniMaster-Open, and remain competitive on established Deep Research benchmarks, showing that global relations can be explicitly learned through relation-centered supervision.