[2603.02274] Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response

Quantitative Biology > Quantitative Methods arXiv:2603.02274 (q-bio) [Submitted on 1 Mar 2026] Title:Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response Authors:Christopher Baker, Karen Rafferty, Hui Wang View a PDF of the paper titled Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response, by Christopher Baker and 2 other authors View PDF Abstract:Precision oncology is currently limited by the small-N, large-P paradox, where high-dimensional genomic data is abundant, but high-quality drug response samples are often sparse. While deep learning models achieve high predictive accuracy, they remain black boxes that fail to provide the causal mechanisms required for clinical decision-making. We present a Neuro-Symbolic Agentic Framework that bridges this gap by integrating a quantitative machine learning World Model with an LLM-based agentic reasoning layer. Our system utilises a forensic data pipeline built on the Sanger GDSC dataset (N=83), achieving a robust predictive correlation (r=0.504) and a significant performance gain through the explicit modelling of clinical context, specifically Microsatellite Instability (MSI) status. We introduce the concept of Inverse Reasoning, where the agentic layer performs in silico CRISPR perturbations to predict how specific genomic edits, such as APC or TP53 repair, alter drug sensitivity. By distinguishing between therapeutic oppo...