LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis

Researchers have introduced LongDS-Bench to evaluate how AI agents perform during complex, multi-step data analysis tasks that require tracking evolving context over time. By shifting focus from isolated interactions to long-horizon workflows, this benchmark aims to address the limitations of existing testing methods that currently fail to measure how well agents manage iterative analytical processes.