DataCOPE

DataCOPE: Unsupervised skill discovery framework for data-analytic agents

Researchers introduce DataCOPE, an unsupervised verifier-guided framework for discovering reusable procedural skills in data-analytic agents without labeled supervision or parameter updates. The system coordinates three components—a data-analytic agent, an unsupervised verifier, and a skill manager for contrastive skill distillation—with task-specific verifier instantiations for report-style and reasoning-style analysis. Evaluated on Deep Data Research and DABStep benchmarks, DataCOPE improves mean scores by 9.71% and 32.30% respectively across four model settings. The approach addresses a key bottleneck in agentic data analysis: acquiring reliable skill supervision at scale.