Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response

Researchers developed a Neuro-Symbolic Agentic Framework combining machine learning with LLM-based reasoning to predict colorectal cancer drug responses. The system achieved significant predictive accuracy (r=0.504) and introduces 'Inverse Reasoning' for simulating genomic edits to predict drug sensitivity changes.