AvalancheBench: Evaluating Enterprise Data Agents Through Latent World Recovery
We introduce AvalancheBench, a benchmark for evaluating enterprise data agents through \emph{latent world recovery}. AvalancheBench improves on existing benchmarks in three ways. First, it evaluates analytical understanding rather than pipeline completion: systems are scored on whether they recover the segments, drivers, temporal events, and relationships that explain the data, not merely on whether they execute a workflow or produce a plausible report. Second, it provides ground truth for goal-driven analytics by generating observations from a known latent world, enabling partial credit for incomplete but valid recoveries. Third, it exposes how early analytical mistakes propagate into la...
| Authors | Darek Kleczek, Fuheng Zhao, Alexander W. Lee et al. |
|---|---|
| Primary category | cs.DB |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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