arXiv:2604.04287v2 Announce Type: replace-cross Abstract: Foundation models in genomics have shown mixed success compared to their counterparts in natural language processing. Yet, the reasons for their limited effectiveness remain poorly understood. In this work, we investigate the role of entropy as a fundamental factor limiting the capacities of such models to learn from their training data and develop foundational capabilities. We train ensembles of models on text and DNA sequences and analyze their predictions, static embeddings, and empirical Fisher information flow. We show that the hig
This research, published in 2026, emerges as the limitations of foundation models across various scientific domains become a critical area of investigation following their rapid proliferation.
It highlights a fundamental technical challenge in applying AI models successfully to complex biological data, potentially redirecting research and investment in AI for life sciences and pharmaceuticals.
The understanding of why foundation models, successful in NLP, struggle in genomics due to inherent data properties and entropy, shifting focus from scaling to architectural innovation.
- · AI researchers specializing in bespoke biological models
- · Biotechnology companies with deep domain expertise
- · Drug discovery platforms leveraging smaller, specialized AI
- · Companies pushing generic large language models for biology
- · AI platforms lacking genomic-specific architectural innovation
Increased focus on fundamental AI research tailored to biological data's unique characteristics and complexity.
Potential for a 'winter' in general-purpose foundation models for life sciences, leading to more targeted, smaller AI solutions.
Reallocation of R&D funding from brute-force foundation model scaling in biology to more sophisticated, knowledge-infused AI approaches, accelerating some drug discovery, and slowing others.
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Read at arXiv cs.CL