aiqbench analysis
GENEB addresses a critical bottleneck in genomic AI by introducing a unified benchmark that allows for the direct comparison of frozen representations across dozens of foundation models. The study reveals that aggregate leaderboards are often misleading due to task-specific trade-offs, demonstrating that architectural choices and pretraining data alignment frequently outweigh raw parameter counts in determining model performance. This work establishes a necessary reference framework for principled evaluation within genomic machine learning, moving the field beyond fragmented metrics toward more robust category-aware selection.
GENEB: Why Genomic Models Are Hard to Compare
arXiv:2606.04525v3 Announce Type: replace Abstract: Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and task-specific reporting. As a result, claims of superiority or generality across models are often not directly comparable. We introduce GENEB, a large-scale diagnostic benchmark that evaluates frozen representations from 40 genomic foundation models across 100 tasks spanning 13 functional categories under a unified probing-based protocol, including few-shot regimes. GENEB enables controlled comparison across model scale, architecture, tokenization, and pretraining data while explicitly exposing task-level trade-offs. Our analysis shows that aggregate leaderboards are unstable: model rankings vary sharply across task categories, scale provides only modest and inconsistent gains, and architectural and pretraining alignment frequently outweigh parameter count. These results highlight limitations of current evaluation practices and position GENEB as a reference framework for principled comparison and category-aware model selection in genomic machine learning.
Abstract:Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and task-specific reporting. As a result, claims of superiority or generality across models are often not directly comparable. We introduce GENEB, a large-scale diagnostic benchmark that evaluates frozen representations from 40 genomic foundation models across 100 tasks spanning 13 functional categories under a unified probing-based protocol, including few-shot regimes. GENEB enables controlled comparison across model scale, architecture, tokenization, and pretraining data while explicitly exposing task-level trade-offs. Our analysis shows that aggregate leaderboards are unstable: model rankings vary sharply across task categories, scale provides only modest and inconsistent gains, and architectural and pretraining alignment frequently outweigh parameter count. These results highlight limitations of current evaluation practices and position GENEB as a reference framework for principled comparison and category-aware model selection in genomic machine learning.
Computation and Language (cs.CL); Machine Learning (cs.LG); Genomics (q-bio.GN)
arXiv:2606.04525 [cs.CL]
(or arXiv:2606.04525v3 [cs.CL] for this version)
Full text under the publisher's open licence · 156 words
Related coverage
GENEB: Why Genomic Models Are Hard to Compare
arXiv · Jun 8, 2026
GENEB: Why Genomic Models Are Hard to Compare
arXiv · Jun 5, 2026
GENEB: Why Genomic Models Are Hard to Compare
arXiv · Aug 6, 2026
How to build a cancer vaccine, and whether they will work this time
LessWrong · Jun 8, 2026