Progress in genomic foundation models (GFMs) has been notoriously difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and highly task-specific reporting. Consequently, competitive claims of superiority or cross-model generality often lack direct comparability across the industry.
To address these evaluation challenges, researchers have introduced GENEB, a large-scale diagnostic benchmark. GENEB evaluates frozen representations from 40 genomic foundation models across 100 tasks spanning 13 functional categories under a unified, probing-based protocol, which includes few-shot regimes.
The benchmark's analysis reveals that aggregate leaderboards in this domain are highly unstable: model rankings vary sharply across different task categories. Crucially, scaling up models yields only modest and inconsistent performance gains, indicating that model architecture and pretraining alignment frequently outweigh raw parameter count.
In summary, these findings expose the severe limitations of current GFM evaluation practices, establishing GENEB as a foundational reference framework for principled, category-aware model selection in genomic machine learning.
[AgentUpdate Depth Analysis] As AI Agents increasingly venture into scientific discovery, genomic foundation models are acting as the specialized "cognitive cores" for AI for Science (AI4S) workflows. However, building reliable scientific agents has been bottlenecked by inconsistent model performance across diverse biological tasks. GENEB’s rigorous analysis proves that generalist genomic agents cannot rely on raw scaling alone. Instead, future biological agents must adopt a multi-model routing paradigm, dynamically directing tasks to specific genomic models based on architectural alignment rather than parameter size. By standardizing this diagnostic evaluation, GENEB paves the way for task-aware agent planning and orchestration, ensuring that LLM-driven scientific agents can systematically select and utilize the most mathematically compatible genomic representations for real-world therapeutic and diagnostic discoveries.
