Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response
arXiv:2603.02274v3 Announce Type: replace-cross Abstract: Precision oncology is currently limited by the small-N, large-P paradox, where high-dimensional genomic data is abundant but pharmacological response samples are sparse. While deep learning achieves predictive accuracy, it frequently fails to provide the mechanistic clarity required for clinical adoption. We present the Contextual Invertible World Model (CIWM), a Neuro-Symbolic Agentic Framework that bridges this gap by integrating a quantitative machine learning emulator with a Large Language Model reasoning layer. Utilising a stringen
The convergence of advanced machine learning models (deep learning) and the capabilities of large language models (LLMs) enables sophisticated neuro-symbolic AI applications, specifically in biomedicine.
This development addresses a critical limitation in precision oncology by providing both predictive accuracy and mechanistic clarity, accelerating drug discovery and personalised treatment strategies.
The ability to integrate quantitative machine learning with LLM reasoning moves beyond 'black box' AI predictions, enabling explainable and clinically actionable insights for drug response.
- · Precision Oncology
- · Pharmaceutical Companies
- · AI/ML Biotech Startups
- · Healthcare Systems
- · Traditional Drug Discovery Methods
- · Clinical Trials Lacking Mechanistic Interpretability
Improved efficacy and reduced costs in drug development for complex diseases like cancer.
Accelerated adoption of AI in clinical decision support and personalized medicine due to increased trust and transparency.
Potential for entirely new therapeutic modalities and targets identified through AI-driven mechanistic understanding.
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Read at arXiv cs.AI