DS-Lighting: Making Agent Harnesses Explicit for Data-Science Automation

A team of researchers has introduced DS‑Lighting, a tool designed to explicitly structure the “harness” or framework that large‑language‑model (LLM) agents use to automate data‑science tasks. In current systems, this framework is often implicit, making it hard to reproduce results, compare different solutions, or attribute performance to specific components. DS‑Lighting breaks the framework into four reusable layers—data, workflow, execution, and evaluation—and lets agents be described as executable operator programs. This approach allows agents to follow predefined pipelines or adaptively explore alternative solutions. The authors also incorporated several open‑source data‑science benchmarks into an MLE‑Bench‑style format, providing a common task interface, an isolated execution environment, and a standardized metrics protocol. Experiments with various agents, frameworks, and models show that an explicit definition of the framework improves reproducibility, comparability, and reliability of workflows while reducing avoidable systemic failures. This proposal is significant because it enables objective evaluation of AI solutions in data science, potentially accelerating the adoption of more robust and transparent automation in professional practice.

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