VADE: A VERIFIABLE AGENT DATA ENGINE FROM PROFESSIONAL SOURCES

Language agents have demonstrated their practical value, particularly in software engineering, where abundant public code and executable tests support training and evaluation at scale. Extending this success to other professional domains, such as law and finance, is not straightforward: many agent tasks involve open-ended analysis, making high-quality outcomes difficult to verify and, in turn, difficult to use as scalable supervision. As a result, high-quality tasks in these domains are still largely expert-authored, making them expensive to scale and limiting their use for training. To address this bottleneck, we introduce VADE, a data engine that automatically constructs verifiable analysis tasks from publicly available sources used in professional practice. VADE exploits a simple asymmetry: professional analyses are difficult to solve because the relevant evidence must first be found and interpreted, but much easier to verify once that evidence is fixed. During task synthesis, the engine first constructs a reference analysis from source evidence and asks an independent agent to reproduce its conclusions from the same evidence without seeing the reference analysis. Only after the analysis is verified is it compiled into an open-ended task with an item-level scoring contract, allowing the same task to support both evaluation and training. We apply VADE across four professional domains, with independent post-hoc expert review finding 93.5% of our constructed tasks to meet a high bar for source fidelity, answer correctness, and evaluation fairness. In finance, these tasks expose substantial remaining model errors, particularly unsupported facts and inferences, while training on verified solver trajectories improves held-out performance and yields gains that transfer to public finance benchmarks such as Finance Agent Benchmark v2. Together, these results suggest a practical path toward scalable, verifiable supervision for open-ended professional tasks.