  The reconstruction of Earth's history faces significant challenges due to the
nonunique interpretations often derived from rock records. The problem has long
been recognized but there are no systematic solutions in practice. This study
introduces an innovative approach that leverages Large Language Models (LLMs)
along with retrieval augmented generation and real-time search capabilities to
counteract interpretation biases, thereby enhancing the accuracy and
reliability of geological analyses. By applying this framework to sedimentology
and paleogeography, we demonstrate its effectiveness in mitigating
interpretations biases through the generation and evaluation of multiple
hypotheses for the same data, which can effectively reduce human bias. Our
research illuminates the transformative potential of LLMs in refining
paleoenvironmental studies and extends their applicability across various
sub-disciplines of Earth sciences, enabling a deeper and more accurate
depiction of Earth's evolution.
