Author

M. Nojiri

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Jul 2026

Articulating Assumptions in AI-Generated Scientific Analyses through Task Decomposition

This work introduces quantity grounded semantic differencing, a multi-agent framework for analyzing and comparing scientific programs generated by LLMs, and demonstrates that the modular task decomposition enhances both transparency and reliability relative to the previous single prompt approach.

A. Hammad, M. Nojiri · arXiv.org · 2 citations