New Evaluation Framework developed for Real Time Data Science Tasks

the new research shared on arXiv reveals a revolutionary method to assess the artificial intelligence agents that conduct data analysis. Since existing evaluation systems are based on static reference answers, they were not able to cope with continuously updated data. For example, the answer to a question like “what was the number of viewers?” is changing every week, which makes the old methods unused.

Basic findings of research:

• Live data support: The framework provides “real accuracy” with direct operation of database queries and does not require static references -Format independent assessment: Automatically evaluates the answers to different data formats -Dynamic questions solution: It can measure the performance of agents against changing data in time
• LLM judgement problem: exceeds the limitations of traditional LLM-as-a-judge systems that compare with fixed answers

This framework is a critical step to measure how well AI agents work in the real world, which automates data analytics roles. Financial reports are now possible to evaluate agents in continuously updated areas such as market analysis or operational metrics. Research solves this problem using accuracy (executable ground truth) and format-agnostic scoring methods.

This development is a milestone for agents that have access to databases. Data science tools that work with real-time data can now be considered more reliable and measurable.