aiqbench analysis
The study asks whether frozen DNA foundation model embeddings add predictive value beyond simple 6-mer composition, and finds that most models do not, with NTv3 as the main exception. Its most useful contribution for ML readers is methodological: compositional baselines should be treated as essential controls, and linear recoverability from k-mers does not reliably indicate downstream utility. Gains are also modest in absolute terms (mean ΔMCC of +0.072 for NTv3), so claims about genomic FMs should be read against strong simple baselines.
What DNA Foundation Models Learn Beyond Sequence Composition
arXiv:2610.15711v1 Announce Type: cross Abstract: Do genomic foundation models (FMs) provide predictive information beyond simple sequence composition? We evaluate frozen representations from five DNA FMs against normalized k-mer frequencies across 57 genomic classification datasets and two exploratory gene-expression regression tasks. Simple k-mer features remain competitive with most models, whereas NTv3 shows a consistent advantage; the ranking is robust to globally fixed and multi-scale k-mer baselines and alternative downstream probes. To test incremental predictive utility directly, we compare global 6-mer features with their concatenation with each FM embedding under a matched Random Forest protocol. NTv3 improves 46 of 57 datasets (mean $\Delta$MCC = +0.072), while Caduceus provides smaller gains and the other models do not improve mean MCC. We separately decompose embeddings into a component linearly predictable from 6-mer frequencies and a ridge residual, finding that linear recoverability does not reliably indicate downstream utility. Exact 6-mer-preserving shuffles further reveal task-relevant sequence-order sensitivity, most pronounced for NTv3, although order sensitivity alone does not guarantee predictive improvements. Our results establish compositional features as essential controls and offer complementary diagnostics for evaluating the added value of frozen genomic representations, without making claims about fine-tuning capacity.
Abstract:Do genomic foundation models (FMs) provide predictive information beyond simple sequence composition? We evaluate frozen representations from five DNA FMs against normalized k-mer frequencies across 57 genomic classification datasets and two exploratory gene-expression regression tasks. Simple k-mer features remain competitive with most models, whereas NTv3 shows a consistent advantage; the ranking is robust to globally fixed and multi-scale k-mer baselines and alternative downstream probes. To test incremental predictive utility directly, we compare global 6-mer features with their concatenation with each FM embedding under a matched Random Forest protocol. NTv3 improves 46 of 57 datasets (mean $\Delta$MCC = +0.072), while Caduceus provides smaller gains and the other models do not improve mean MCC. We separately decompose embeddings into a component linearly predictable from 6-mer frequencies and a ridge residual, finding that linear recoverability does not reliably indicate downstream utility. Exact 6-mer-preserving shuffles further reveal task-relevant sequence-order sensitivity, most pronounced for NTv3, although order sensitivity alone does not guarantee predictive improvements. Our results establish compositional features as essential controls and offer complementary diagnostics for evaluating the added value of frozen genomic representations, without making claims about fine-tuning capacity.
Accepted to NeurIPS 2026 (Evaluations & Datasets Track). 9 pages of main text, 5 main figures, with references and appendices
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.15711 [cs.LG]
(or arXiv:2610.15711v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.15711 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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