PatchDNA: A Flexible and Biologically-Informed Alternative to Tokenization for DNA
Authors: Alice Del Vecchio
Chantriolnt-Andreas Kapourani
Abdullah M Athar
Agnieszka Dobrowolska
Andrew Anighoro
Benjamin Tenmann
Lindsay Edwards
Cristian Regep
Venue: ICLR 2026 Poster (2026)
Keywords: DNA
DNA language model
gLM
tokenization
genomic sequence representation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: Evolutionary conservation–guided “patch” boundaries focus model capacity on the most functionally important regions, yielding smaller models that nonetheless outperform current state-of-the-art benchmarks and, uniquely, permit on-the-fly re-patching

Abstract: DNA language models are emerging as powerful tools for representing genomic sequences, with recent progress driven by self-supervised learning. However, performance on downstream tasks is sensitive to tokenization strategies reflecting the complex encodings in DNA, where both regulatory elements and single-nucleotide changes can be functionally significant. Yet existing models are fixed to their initial tokenization strategy; single-nucleotide encodings result in long sequences that challenge transformer architectures, while fixed multi-nucleotide schemes like byte pair encoding struggle with character level modeling. Drawing inspiration from the Byte Latent Transformer's combining of bytes into patches, we propose that 'patching' provides a competitive and more efficient alternative to tokenization for DNA sequences. Furthermore, patching eliminates the need for a fixed vocabulary, which offers unique advantages to DNA. Leveraging this, we propose a biologically informed strategy, using evolutionary conservation scores as a guide for 'patch' boundaries. By prioritizing conserved regions, our approach directs computational resources to the most functionally relevant parts of the DNA sequence. We show that models up to an order of magnitude smaller surpass current state-of-the-art performance in existing DNA benchmarks. Importantly, our approach provides the flexibility to change patching without retraining, overcoming a fundamental limitation of current tokenization methods.

Published as a conference paper at ICLR 2026
PATCHDNA: A FLEXIBLE AND BIOLOGICALLY- INFORMED ALTERNATIVE TO TOKENIZATION FOR DNA
Alice Del Vecchio, Chantriont-Andreas Kapourani, Abdullah M Athar, Agnieszka Dobrowolska, Andrew Anighoro. Benjamin Tenmann, Lindsay Edwards and Cristian Regep Relation Therapeutics
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ABSTRACT
DNA language models are emerging as powerful tools for representing genomic sequences, with recent progress driven by self-supervised learning. However, performance on downstream tasks is sensitive to tokenization strategies reflect- ing the complex encodings in DNA, where both regulatory elements and single- nucleotide changes can be functionally significant. Yet existing models are fixed their initial tokenization strategy: single-nucleotide encodings result long sequences that challenge transformer architectures, while fixed multi-nucleotide schemes like byte pair encoding struggle with character level modeling. Drawing inspiration from the Byte Latent Transformer's combining of bytes into patches, we propose that 'patching' provides competitive and more efficient alternative tokenization for DNA sequences. Furthermore, patching eliminates the need for fixed vocabulary, which offers unique advantages DNA. Leveraging this, we propose biologically informed strategy, using evolutionary conservation scores as a guide for 'patch' boundaries. By prioritizing conserved regions, our approach directs computational resources the most functionally relevant parts of the DNA sequence. We show that models up to an order magnitude smaller surpass cur- rent state-of-the-as performance in existing DNA benchmarks. Importantly, approach provides the flexibility to change patching without retraining, overcom- ing fundamental limitation of current tokenization methods.
INTRODUCTION
Self-supervised learning has led to surge interest in DNA language models, sequence models trained on nucleotide data to produce general-purposs genomic representations. These models have shown promise across diverse tasks, from identifying regulatory elements to variant effect pre- diction (Brixi et al.) 2025 Nguyen al. 2023 Schiff et al. 2024). A central challenge in adapting language modeling to DNA is how to tokenize the input sequence. Unlike natural language, where subword or word-level tokenization can exploit semantic structure and redundancy (Mielke al., 2021), genomic sequences encode both fine-grained (e.g. letter level single-mucleotide variants) and coarse- searse-grained (regulatory elements) information, often within the same genomic region. The choice of tokenization thus directly impacts both resolution and efficiency.
Existing DNA models typically fix their tokenization strategy prior to training. Models that operate at the single-nucleotide level preserve maximal resolution but produce extremely long sequences that challenge transformer architectures. Conversely, fixed multi-nuc schemes such as k-mers or byte pair encoding improve efficiency but often lose critical single-base information. Prior work has shown that downstream performance can be highly sensitive to this tradcoff (Lindsey et al. 2025] Patel et al. 2024). Therefore exploring alternative tokenization strategies and their suitability for encoding DNA sequences a compelling research direction.
The Byte Latent Transformer (BLT), originally proposed for natural language processing, intro- duces a dynamic altermative to tokenization, that segments input sequences into veriable-length patches based predictive entropy (Pagnoni al. 2025). This enables models to allocate at-

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tention and computation to regions of high uncertainty, capturing context-dependenn structure more effectively. Recognizing the potential advantages that patching offers for genomic data, we intro- duce PatchDNA, model that represents DNA sequences contiguous, dynamically determined patches rather than individual tokens (Figure I. This general framework aligns naturally with the structure of genomic data and offers clear advantages over traditional tokenization for DNA. Patch- ing improves efficiency as patch sizes can far exceed the size of tokens, while preserving single- nucleotide resolution. Morcover, the lack of fixed vocabulary also offers greater flexibility than tokenization, enabling the design of more biologically informed approaches.
Our key contributions can be summarized as follows:
We extend dynamic patching for DNA by modifying the BLT framework and show that patches are a better alternative to noken-level representations of genomic sequences in effi ciency and flexibility.
We introduce novel conservation-guiie guided patching scheme that leverages evolutionary signals to guide patch boundaries, providing biologically informed inductive bias.
We introduce re-patching, allowing the patching strategy of the model to be changed after pretraining, overcoming fundamental limitation of current tokenization methods. This enables flexible downstream application with minimal computational overhead.
Through extensive experiments, we show that conservation guidedd patching systematically achieves the strongest results, while alternative patching strategies remain competitive. We further demon- strate the flexibility of the framework through re-patching, enabling models to adapt their patching strategy for different downstream tasks with no retraining from scratch. Our results demonstrate value of patching in advancing genomic language modeling.
A. Patching produces biologically relevant
Pretraining conservation patching
Context m-patching
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PatchONA
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Kemer J.G A AIC G.
Local Decoder
Local Decoder
Ryde Pair Encading A.C. Tie AACITT R8R
Downabiamm Teckkr
Latent Transforer
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Comeanatton Store
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Figure 1: Overview PatchDNA. (A) Unlike fixed tokenization methods, PatchDNA segments sequences into biologically meaningful patches without relying fixed vocabulary. (B) During pretraining, patch boundaries are guided by evolutionary conservation scores, enabling the model focus computational resources functionally important regions. (C) We introduce re-patching. enabling flexible downstream application with no retraining from scratch.
2 EXISTING DNA TOKENIZATION SCHEMES
Several tokenization strategies offer trade-offs between vocabulary size, biological interpretability, computational efficiency and exhaustiveness of coverage:
The input sequence is split into fixed-length sub-strings of length k done in the Nu- cleotide Transformer [Dala-Torre et al. 2025). However, small changes to the input sequence can drastically alter the tokenized sequence, making it difficult for the model to align representations of near-id identical inputs. This inconsistency hinders efficient learning and may degrade model perfor- mance (Zhou al. 2024).
K-mers:
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Byte-Pair Encoding (BPE) To address issues with k-mer tokenization, DNABERT2 (Zhou al. 2024) applies BPE (Sennrich et al. 2016) to DNA. This method iteratively merges the most frequent co-occurring nucleotides into variable-length tokens, enabling the discovery of common sequence motifs while controlling vocabulary growth. This is a popular approach, utilized by other DNA models such GENA-LM (Fishman al., 2025) and MistralIDNA (Mourad (024). However, BPE-tokenized models have shown poor performance on character-level tasks in natural language, such as spelling (Pagnoni et al., 2025). This is a particularly relevant issue in DNA, where letter level single nucleotide variants are critical.
Learnable tokenization: Approaches such as VQDNA (Li et al. 2024) and MxDNA (Qiao al, 2024) learn discrete embeddings or mixture-of-exxpee assignments for sequence fragments, produc- ing vocabularies tailored to genomic corpora. Although adaptive, these methods introduce additional training and inference overhead while not reducing the input sizes to the transformer, and the learned vocabulary are opaque.
Single nucleotide:
Despite these innovations, no single tokenization paradigm consistently outper- forms others across diverse genomic tasks (Dotan et al. (2024] Lindsey et al. 2025 Patel et al. 2024). Consequently, the canonical nucleotide-level representation still widely used, for instance in Hye-
naDNA (Nguyen et al. )023), Caduceus (Schiff et al.) 2024) and the 40B-parameter Evo2 (Brixi 2025) This resolution is essential for fine-gramed tasks such variant effect prediction, ct al.
which aims to accurately model DNA functional impact Benegas et al. (2025). However, it is com- putationally inefficient, genomic sequences tory elements, such as enhancers, can be over 100kbp from their target genes (Sanyal et al. 2012) Thus, effective sequence compression is critical for scalable DNA modeling.
far longer than natural language, and key regula-
The approach presented here explores an alternative to tokenization that maintains single-nucleotide granularity, compresses low-information regions, remains interpretable, and allows post-training adaptation. This unique combination of features is unmet by existing methods and yields superior model performance.
3 PATCHDNA: BIOLOGICALLY-NFORIIH MODELING OF DNA
3.1 PATCHING PRELIMINARIES
We follow the patching framework set out by the BLT (Pagnoni et al., 2025). Let x (21,22.-. ... xn be a vector denoting a sequence n bytes. A patching function is defined as fo + b E 10. where 6, = indicates that position marks the beginning of new patch, and b, = otherwise. To ensure existence of at least a single patch we set b = 1. This binary sequence b (b1, by...., b,) partitions the input sequence x into E d contiguous subsequences, or patches,
...
(pI.P2,.....
We distinguish between tokens and patches in the context of sequence modeling. Tokens prede- fined groupings of bytes drawn from a finite vocabulary V, which is determined prior to training. In contrast, patches are variable-length subsequences derived computationally from the input by the patching function fy. without relying on a fixed vocabulary.
Eatro patching: In BLT, patch boundaries are determined dynamically based on pre- dictive uncertainty. Specifically, the patching function relies on the estimated conditional entropy Á [z; Zi....,, Ii-1) computed by a lightweight next-token prediction model. A new patch is ini- tiated when the entropy exceeds a predefined threshold 0g. Formally, the entropy-based patching function is defined as:
Sentrogy (11+1) [1 if (x; /......, zi-1) OH, otherwise,
The threshold fg controls a tradeoff between granularity and efficiency: lower values yield smaller patches and longer sequences; higher values result in coarser patches and improved efficiency.
Generalized patching strategy: We define a flexible class patching functions fp where bound- aries determined when the scoring function 9p. evaluated over the input sequence, exceeds
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predefined threshold 0y:
{
if gp(xx) > pp. otherwise.
Throughout, we use Sp and 0g define the patching strategy.
3.2 APPLICATION OF PATCHING FOR DNA MODELING
The novelty of our work lies in demonstrating the unique advantages of patching for DNA language models. While the BLT framework was developed for NLP, its potential extensions to genomics remain unexplored. Unlike BLT, we move beyond entropy-based patching and show that the lack of fixed vocabulary allows more tailored patching functions that be designed to incorporate domain-specific inductive biases. Leveraging this, we propose biologically informed patching ap- proaches, where highlight the superiority of conservation-based patching compared to previous methods. We further extend the framework in several ways. First, we introduce re-patching, allow- ing patching strategies to be modified after pretraining, a capability particularly valuable for DNA and potentially other domains. Second, while BLT primarily focuses on generation, we demonstrate that extracting embeddings
cangle-nucleotin resolution provides unique advantages for genomic analysis. Finally, DNA sequences are much longer than typical NLP inputs. While BLT only con- siders sequence lengths up 16k bytes, we process sequences exceeding 100k nucleotides by using larger average patch sizes, yielding far fewer FL.OPs than existing DNA models at similar lengths (see Table [8 in Section A.3.4). Achieving equivalent efficiency with tokenization would require 20-mer tokens, leading to intractable vocabulary of size 400 Together, these extensions establish patching as a practical and scalable paradigm for modeling realistic DNA sequences.
3.3 CONSERVATION-DRIVEE PATCHING
We apply the generalized patching framework to genomic sequences by treating each byte as one of the four canonical nucleotides (A, C, G, T) or the unknown base N. While entropy-based patching in BLT is motivated by linguistic ambiguity, we hypothesize that in the genomic domain, computational focus should instead align with regions of high evolutionary conservation (Figure (B).
To implement this, we define the scoring function 9p as the PhyloP conservation score (Pollard al. 2010, Siepel et al., (2055). scalar value derived from multi-species alignments (Edgar & Batzoglou 2006) that quantifies the evolutionary constraint at each nucleotide. In Section 4 we demonstrate that conservation-bas patching serves as a strong general- strategy for DNA language models, offering robust performance across diverse downstream tasks.
3.4 RE-PATCHING
Genomic tasks often require modeling context cell-type-specifie signals, and the optimal patching strategy may vary by task. As discussed in Section 2 different tokenization schemes can yield varying performance across distinct genomic tasks.
To accommodate this, we introduce re-patching, novel capability to redefine patch boundaries after pretraining. Unlike models constrained by fixed token vocabularies, our approach enables post-hoc modification on the patching function fp. which depends only on the scoring function 9p and thresh- old 0p. This makes straightforward to substitute Ip inference or fine-tuning time with task- or tissue-specift epigenetic signals, such as chromatin accessibility measured by DNase-seq (Klemm) jet al. (201)). See Section A.6 for further implementation details. As shown in Section 4.5 this sim- ple adaptation yields substantial gains on cell-type-specine benchmarks, without requiring model retraining from scratch. Importantly, our approach is not constrained by need for biological infor- mation, but can exploit it to guide patching when available and informative. When conservation or other biological signals are absent, the model can readily turn to alternative patching strategies, such as fixed patching, without requiring architectural changes (see Table 12 Section A.3.
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3.5 ARCHITECTURE
The backbone for the work above the BLT model (Pagnoni al. 2025). which an autoregressive model consisting of three main components: a small local encoder, deep latent global transformer, and small local decoder.
Local encoder:
This is shallow transformer that computes patch-level representations from Lungle-nulleotid input sequence x, using patch boundaries provided by the patching function fp- It alternates between sliding window self-attention layers (operating over the nucleotide sequence) and cross-attention layers, following the Perceiver architecture (Jaegle al. [021). Patch represen- tations are queries, which attend only to the nucleotides (keys) within their respective patch.
Latent global transformer: This is a standard transformer (Vaswani et al.] 2017), using rotary positional encodings (Su et al. 2024), operating on the patch embeddings produced by the local encoder. It models long-range interactions across the full sequence using global attention. Since the patch sequence p is much shorter than the input sequence x, this module can be made significantly deeper, allowing the bulk of the model's capacity to focus on global reasoning without incurring prohibitive computational cost.
Local decoder: This lightweight transformer updates the nucleotide-level representations from the local encoder incorporate the patch embedding output from the global transformer. Like the local encoder, it alternates between sliding window self-attention and cross-attention layers. In this case, the single-nucleotide embeddings serve as queries, while the patch embeddings act as keys and values. A language modeling head is applied to the final nucleotide embeddings to produce logits for nexl-nucleotide prediction during autoregressive pretraining.
3.5.1 PRETRAINING AND DOWNSTREAM USAGE
We pretrain PatchDNA on the human reference genome using a next-nucleotide prediction objec- tive, following the same training and validation splits as Caduceus (Schiff et al. (024) and Hye- naDNA (Nguyen et al.] 2023), as originally defined by (Kelley) (020). During pretraining, we set the patching threshold Op to the 95th percentile of the scoring function 9r (based on PhyloP conser- vation scores, or entropy), resulting in an average patch size of approximately 20 nucleotides. See Section A..7ion[A.7oan for results using other conservation scoring and sensitivity analysis at other thresholds. This enables efficient training with input contexts up to 131,000 base pairs. To our knowledge, this is the first cranformer-basee architecture in DNA language modeling capable of efficiently handling such long sequences at scale. We pretrain two main models: Pat ChDA, a 19.2M parameter model with 16 kbp context window, and PatchDNA- 7M, 7.7M parameter model with 131 kbp text window. The latter is designed to enable fairer comparisons with other long-range sequence models, such Caduceus (7.7M) and HyenaDNA (6.6M). We maximum patch size to prevent corpeoompessionn of the DNA sequence in non-conserved regions. Full hyperparameter and training details are provided in Section A.2]
While the original BLT paper focused on generation tasks in natural language processing, we show that when pretrained on genomic sequences, the decoder's nucleotide-level embeddings yield mean- ingful representations for a wide range of downstream tasks. These embeddings retain single- nucleotide resolution, making them particularly well suited for fine-grained genomic prediction problems. For all downstream applications, we extract the penultimate layer of the decoder nucleotide-level embedding representation.
4 EXPERIMENTS
We compare against a range of strong baselines (see Section A.1 including small models such as HyenaDNA (Nguyen et al. 2023) and Caduceus (Schiff et al.
2024) both with around million pa-
rameters, well as large-scale DNA models ranging from 110 million to 500 million parameters, in- cluding GENA-LA (Fishman et al. 2025). DNABERT2 Zhou et al. 2024). MistralDNA (Mourad, 2024 and the Nucleotide Transformer variants (Dalla-Tone) et al. (025). In Section A.4 present extensive ablations where we compare to PatchDNA- and -- models pretrained and evaluated with entropy and fixed patching.
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4.1 NUCLEOTIDE TRANSFORMER BENCHMARK
The NT benchmark spans 18 supervised classification tasks (300~1000 bp sequences) across three categories: regulatory element detection, splice site prediction, and chromatin profile annotation. Each task is framed as supervised classification problem, and all models are evaluated using standardized protocol repeated across five random seeds. Specifically, a frozen pretrained model en- codes each DNA sequence into latent embedding space, and a linear probe is trained on top of these fixed representations, similar to (Marin et al. 2024). This setup enables controlled comparison of representational quality irrespective of the underlying architecture.
Figure shows mean Matthews Correlation Coefficient (MCC) per category. PatchDNA achieves the highest average MCC in regulatory elements and splicing tasks, and remains competitive on chromatin profile classification, matching larger-scale models such as NT-MS-50 OM. Detailed re- sults for all 18 tasks are given in Section A.3.I We further show that P at chDAA outperforms
strong baselines under finetuning (Section A.3. in addition to the probing results reported here.
Model
PelchON/-Entooyy
0.00
0.62
0.58
0.59
0.82 0.61
0.03
0.60
Hyenattiio
cinttb- se-uuce
Cacuuctt pit
MS 500m MaDAAA
Moon MCC
0.55
0.50
0.50
0:48
0.47
0.44
0.44
0.39
0.38
0.30
Regulatory
Chrometin profiles
Figure 2: Mean MCC across task categories on the NT benchmark. Models are grouped by size: orange shades indicate small models, and grey shades represent large models. Error bars denote one standard deviation across five seeds.
4.2 DART-EVAL BENCHMARK
Next, we evaluate our model on DART-Eval (Patel et al. 2024). a benchmark covering five regu- latory genomics tasks. These include distinguishing regulatory sequences from matched controls (Task 1), detecting transcription factor (TF) motifs (Task 2), identifying cell-type-specific signa- tures (Task 3), predicting regulatory activity levels (Task 4), and variant effect prediction (Task 5). The benchmark combines both classification and regression tasks, with settings that test zero-shot capabilities and supervised probing.
We use the official DART-Eval implementation and adopt the zero-shot configuration wherever available, specifically for Tasks and 5. These tasks are evaluated directly using model like- lihoods or embeddings, without any additional training. For Tasks 3 and 4, which lack zero-shot variants, we follow the standard protocol and train probes on top of frozen embeddings. For Tasks and 5, where multiple sub tasks exist, we report the mean across the tasks. Detailed results for sub tasks can found the A.3.2
For competing models, we report the values given in the original benchmark, which have been performed on one seed. As shown Table our model achieves the best overall performance on DART-Eval, with the best mean rank (2) across all five tasks. PatchDNA BetchDNA-EEt.eee Entropy is second best on benchmark, highlighting the benefit of patching and the BLT architecture independently of conservation based patching. While other models show strength on individual tasks, such as WT-MS-500 OM on Task 5 or HyenaDNA on Task 3, they do not generalize as broadly.
4.3 BEND
The BEND benchmark MMrrin et al. 2024) combines sequence-level classification tasks, such as chromatin accessibility, histone modification, and CpG methylation, with nucleotide-level classifi-

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Table 1: Performance on the DART-Eval benchmark. Raw task metrics are shown, taking the mean across sub tasks for Task 4 and Task 5. Overall mean rank across all tasks is computed in the final column.
Model
Task Accuracy
Task Accuracy
Task Accuracy
Task
Task 5 AUROC
Mean rank
Spearman R
PatchDNA
0.966
0.725
0.457
0.440
0.555
2.0
PatchDNA- -ntropy
0.965
0.650
0.465
0.400
0.523
3.0
HyenaDNA
0.891
0.645
0.587
0.384
0.515
3.8
GENA-LM-Lan Large
0.947
0,620
0,383
0,472
0,505
4.2
NT-MS-500M
0.745
0.565
0.420
0.422
0.566
4.8
Caduccus-ps
0.971
0.570
0.281
0.297
0,514
5.8
DNABERT2
0.876
0.590
0.371
0.419
0.493
6.0
MistralDNA
0.863
0.625
0.329
0.363
0.498
6.4
cation tasks like gene finding. Gene finding requires multi-class annotation of each base over se- quences up to 14 kbp, making a fine-gras teeegraaded task that depends on both local context and long-range dependencies. Our patching strategy, combined with cross-attention, enables precise nucleotide rep- resentations while flexibly aggregating context.
We follow the original BEND evaluation protocol, by training probe top of frozen embeddings. For competing models, we report the values given the original benchmark, which have been performed
seed. As shown in Table PatchDN: achieves consistently strong performance, outperforming other models 3 out 4 tasks. On the gene finding task, it outperforms larger models such GENA-LM-ILN rge and DNABERT2 ranking second only NT-MS-50 CM; a model with 25 fold greater capacity (500M vs 19.2M parameters), and pre-trained on a substantially larger, multi-species dataset. Results on the remaining tasks BEND are reported in Section A.3.3
Table 2: Performance across BEND short and long range tasks. Gene finding is reported with MCC, while other tasks are reported with AUROC.
Model
Gene finding Chromatin accessibility Histone modification
CpG Methylation AUROC
MCC
AUROC
AUROC
PatchDNA PatchDNA--Entron
0.84
0.79
0.92
0.37
0.83
0.78
0.90
NT-MS-5000
0.64
0.80
0.76
0.91
MIANA-LM-Large
0.52
0.76
0.78
0.91
Caduceus-ph
0.80
0.79
0.90
HyenaDNA
0.35
0.84
0.76
0.91
DNABERT-2
0.43
0.81
0.78
0.90
4.4 CAGE PREDICTION BENCHMARK
To evaluate performance long DNA sequences, we benchmark PatchDNA on CAGE predic- tion (Avsec et al. 202T). CAGE (Cap Analysis of Gene Expression) quantifies gene expression and identifies transcription start sites. The prediction task involves regressing expression values across bins in a 114,688 bp input sequence, leveraging distal regulatory elements that may lie kilobases away from the target gene.
We follow the setup from (Trop et al. 2025). using 50 CAGE tracks and the full 114 kbp context window. We only compare to other DNA language models that can handle such long sequences in one forward pass. For fair comparison, budget of HyenaDNA and Caduceus. All models are fine-tuned for one epoch using an MLP head and evaluated using Pearson correlation the gene, cell, and full-track levels, following the metrics introduced Enformer (Avsec et al. 2021) We give detailed explanations of these metrics in Supplementary A.3.4
the PatchDNA-T1 model to match the parameter
As shown in Table 3 Pat PatchDNa BeDCh-A--11 7M outperforms all baselines across evaluation metrics, achieving the highest gene- and cell-level Pearson correlations. To further boost performance, introduce

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variant that adjusts the patching strategy during fine-tuning by leveraging cCRE annotations (Moore jet al. 2020) to focus attention on known regulatory regions. This modification, which is applied only fine-tuning time, and only be done with Pat leads to additional gains. This demonstrates that our framework can flexibly incorporate biological priors without requiring model retraining changes to the underlying architecture. PatchDNA also offers practical efficiency advantages, finetuning more than 3x faster than HyenaDNA (see Table [8] Section A.3.4), high- lighting the benefit of moving beyond single-nucleotide tokenization. For results on other long range tasks, see Section A.3.6 chDNA outperforms other long sequence models on 6 out of 7 tasks in the Genomics Long Range Benchmark (Trop et al. (025).
Table 3:
Performance on the CAGE prediction task. We report mean Pearson correlation across genes, cells, and full sequence bins. Error bars denote one standard deviation across five seeds.
Model
Gene Pearson
Cell Pearson
Full Pearson
PatchDDA-7M
0.369 = 0.001
0.771 + 0.002
0.471 + 0.002
PatchDNA-7M + cCRE-aware re- re-patchinn
0.373 = 0.001
0.792 + 0.002
0.408 0.004
HyenaDNA
0.362 = 0.001
0.745 = 0.002
0.290 + 0.004
Caduceus-ph
0.362 + 0.001
0.750 + 0.002
0.309 + 0.003
Caduceus-ps
0.365 0.001
0.766 + 0.001
0.420 + 0.006
4.5 CELL TYPE SPECIFIC RE-PATCHING
Because the DNA sequence is invariant across cell types, sequence-only models often struggle with context-specific tasks such as predicting cell-type-specific expression (Patel al. 2024). We show that our model can be adapted to such tasks with minimal modification and without changing the architecture or retraining from scratch. Using the setup in Section (.4, we evaluate performance on CAGE prediction across three cell types: K562, hepatocytes, and neurons. For each task, we predict expression for a single CAGE track corresponding to the target cell type.
Cell-fype-specific epigenetic inputs like DNase-seq data can help provide cellular context by high- lighting regulatory regions of the genome that are accessible and potentially active in transcription (Carter & Zhao 2021). While previous methods like EPInformer (Lin al. (024) and Seq2Exp (Su ctt al. 2025) rely on custom architectures that fuse sequence with epigenetic inputs, we instead only re-patch the DNA using DNase-seq signal from the target cell type. This only alters the patches, preserving the underlying model architecture while focusing computation on regulatory regions in- ferred from chromatin accessibility.
As shown in Table 4 Pat.chDNA outperforms all competing baselines on cell type-specific CAGE prediction. Given that Caduceus-ps outperforms Caduceus-ph in Section 4.4 only compare to Caduceus-ps in this task. Incorporating DNase-aware patching further improves performance across all three cell types, demonstrating that context-specific patching is highly informative for modeling regulatory activity. Table shows that these gains are maximized when the DNase-seq signal used for patching matches the target tissue. contrast, mismatched signals lead consistently lower performance, highlighting the importance of aligning the patching strategy with the underlying cel- lular context. Notably, these improvements are achieved without altering the model architecture or retraining from scratch.
Table 4: Performance cell type-specific CAGE prediction, reported Pearson correlation across cells. Error bars denote one standard deviation across five seeds.
Model
K562
Hepatocyte
Neuron
PatchDNA-7M
0.754 + 0.003
0.717 = 0.002
0.799 + 0.001
PatchDNA-7M + DNase-aware re-patching
0.828 + 0.001
0.727
0.001
0.831 + 0.001
HyenaDNA
0.703 + 0.012
0.667 = 0.006
0.763
+ 0.003
Caduceus-ps
0.732 + 0.006
0.705
0.001
0.798 + 0.002
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Table 5: Performance on DNase-aware cell type-specific CAGE prediction, reported Pearson correlation across cells. Maximum performance is achieved when patching is guided by DNase- seq signal from the corresponding tissue (the diagonal), and applied during fine-tuning. Error bars denote one standard deviation across five seeds.
Model
K562
Hepatocyte
Neuron
PatchDNA-7M DNase-wware (K562)
0.828 + 0.001
0.713 = 0.001
0.807 + 0.002
PatchDNA-7M DNase-aware (Hepatocyte)
0.775 0.002
0.727 #0.001
0.822 0.001
PatchDNA-7M DNase-aware (Neuron)
0.770 + 0.001
0.707 0.001
0.831 #0.001
5 DISCUSSION
We introduce PatchDNA, novel DNA language modeling framework that replaces fixed tok- enization with a dynamic patching mechanism, which improves efficiency, enables models to focus adaptively functionally relevant genomic regions, and provides flexibility through re-patching. By introducing conservation-drive and context-aware patching strategies, PatchDNA allocates model capacity the most informative regions of the genome, without relying on fixed vocabular- ies. Beyond pretraining, PatchDNA introduces re-patching: the ability to redefine patch boundaries post hoc. This property allows our model to use tissue-specific or task-specific signals adapt to downstream tasks, such as cell-type- -elestyp--ppecffi expression prediction, without retraining from scratch. Furthermore, our framework supports re- ee-pacching with alternative strategies when biological signals are unavailable, demonstrating that it is not dependent on such inputs but can flexibly exploit them whenever they are present and informative.
Through extensive benchmarking, we demonstrate that PatchDNA consistently outperforms or matches state-of-the-art models across regulatory element prediction, splicing, and gene expression tasks, while training significantly faster. Complementary ablations further highlight the effectiveness of our approach: conservation-guie guideed patching outperforms entropy-based and fixed-size baselines, well as using PhyloP conservation scores directly (see Oection/A.4) Notably, while conser- vation scores are only weakly correlated with task labels in most benchmarks, conservation based patching still yields substantial gains, demonstrating that PatchDNA extracts richer, functionally grounded representations than conservation scores alone.
5.1 LIMITATIONS AND FUTURE WORK
While PatchDNA offers versatile framework for DNA modeling, several limitations remain. First, the architecture we used is autoregressive and decoder-only, which limits its ability to fully cap- ture the bidirectional context that is often critical in genomics (Schiff al.) 2024). Extending the framework to support bidirectional encoding could further improve performance on context-rich tasks (Schiff et al. 2024 (Schmidinger al. (025).. Second, we currently pretrain only the hu- man reference genome. Incorporating multi-species data (see Section or genetic variation from population-scale datasets could expand the model's applicability and improve generalization to un- seen genomic contexts (Brixi et al.) 2025 Dalla- Torre et al. 2025). Another open direction is the incorporation of reverse-complement (RC) equivariance, which a desirable inductive bias DNA
modeling (Mallet & Vert 2021).
While we demonstrate re-patching on selected tasks, future work should evaluate the generality of this mechanism across broader range of biological applications. including regulatory activity prediction and variant effect interpretation (Avsec al.] [02T]: [Lider et al. 2025). PatchDNA provides a modular foundation to explore these extensions with minimal architectural changes. Fur- thermore, assessing the scaling-law behavior of these models and comparing their performance to existing approaches will be an important avenue for future work (Nguyen et al. 2024).
We hope that our general framework will serve as a foundation for future work, inspiring the de- velopment of new patching strategies and advancing the broader field of DNA language models through task- and biology-awa aware modeling in contrast to the current emphasis on scaling laws Brixi et al. (0025).

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6 REPRODUCIBILITY STATEMENT
We provide detailed hyperparameters and setup for pretraining the PatchDNA models in Section A.2 For downstream tasks, we provide the methodology we use A.3 where we default to established practices in the literature where available. All datasets that we use are publicly available and links are given in each section where we use external datasets (Section A.2] Section (A.3). All baselines
that we use from literature can be downloaded from publicly available sources, with links given in Section A.I
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A APPENDIX
A.1 DETAILS OF PRETRAINED BASELINE MODELS
Table 6: Overview of pretrained DNA language models used in this study, We list HuggingFace
IDs, number of parameters, and species coverage.
Model
HuggingFace ID
Parameters
Species
HyenaDNA
LougSafarthyessdds--rr
waw.ssdddd----------e-e-eeee Im-seqlen-hf
6.6M
Human
Caduceus-ps
beeisboo-groouedddcccsees seqlen-131k model-256 layer-16
Human
Caduccus-ph
kuleshov-group wwwpp//acauccss-pl.sseeeeo
7.7M
Human
DNABERT2
zhibant 996/DNABEKT2221111
117M
Multispecies
GENA LM Base
AIRI Institute/gena Im hert base 12t
10M
Human
GENA-LM-TE arge
336M
Multi-spocies
MistralDNA
16B
Human
NI-MS-50UM
500M
Mulli-species
NTMS-1OUM
hea.aaDDeeAAhnsssliieetttamaaaee htw.:/oooooiieettaaaaaaaneeeeeee 100m species
species
M&DNA
100M
Human
A.2 PRETRAINING DETAILS
ARCHITECTURE HYPERPARAMETERS
Table 7: Architecture hyperparameters for PatchDNA and PatchDNA-7M. The patching threshold is the 95% quantile of all PhyloP scores
Hyperparameter
PatchDNA
PatchDNA-7M1
Num Local Encoder Layers
2
Num Local Decoder Layers
2
Num Global Transformer Layers
8
3
Embedding Dimension
256
256
Context Length
16,000
131,072
Max Patch Length
128
1,024
Number of Global Transformer Heads
Number of Local Encoder Heads
8
4
Number of Local Decoder Heads
PhyloP Patching Threshold
1.5
1.5
Num parameters
19.2M
7.7M
TRAINING HYPERPARAMETERS
We use the same optimizer, learning rate, weight decay, and gradient clipping 2025).
Pagnoni et al.
Table 8: Training hyperparameters for PatchDNA and PatchDNA-7M.
Hyperparameter
PatchDNA
PatchDNA-7M
Learning Rate
0.0004
0.0004
Training Steps
100,000
100,000
Weight Decay
0.1
0.1
Optimizer
AdamW
Adam
Batch Size
64
8
Gradient Clipping
1.0
1.0
Training Time (4xA100 80GB)
18 hours
10 hours
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PATCHING ABLATION CONFIGURATIONS
PatchDNA Entropy: Uses identical hyperparameters to PatchDNA, except it employs a small entropy model for patching with threshold of 1.37 (which is 95% quantile of all scores from the entropy model across the genome). Hyperparameter details for the entropy model are in T
PatchDNA Fixed Patch Size 20: Shares the same hyperparameters as PatchDNA, but uses fixed patch size of 20. i.e., every 20 nucleotides are one patch. We use this because patching threshold of the 95% quantile of all scores gives average patch size approximately 20.
Table 9: Hyperparameters for the entropy model used in PatchDNA Entropy.
Hyperparameter
Value
Number of Layers
8
Embedding Dimension
256
Context Length
8,192
Sliding window
512
Number of Heads
8
Batch size
256
Learning Rate
0.0004
Training Steps
100,000
Weight Decay
0.1
Optimizer
AdamW
Gradient Clipping
1.0
Num parameters
6.8M
DATA
We use the same train and validation splits as HyenaDNA Nguyen et al., 2023)
and Caduceus (Schiff et al. 2024). which originate from Kelley (2020) available at
barnyard/data
We use the PhyloP scores (Siepel al., 2005 Pollard et al. 2010) downloaded from
CODE
Code will be available at https//gggiubbbcoo////aaiinnnnoo
A.3 BENCHMARK TASKS DETAILS
A.3.1 NUCLEOTIDE TRANSFORMER BENCHMARK
We evaluated model performance on the Nucleotide Transformer (NT) benchmark, diverse collec- tion of 18 classification tasks designed to assess the biological utility of pretrained DNA language models. The benchmark was accessed via the HuggingFace Hut and includes pre-defined train and test splits for each task. For each task, we further partitioned the provided training set into 90% training and 10% validation splits. All experiments were repeated across five random seeds, with each seed generating new vain/validation split to evaluate consistency and robustness.
To ensure fair and consistent evaluation across models, we adopted linear probing protocol. Specif- ically, each pretrained model frozen and used to encode input DNA sequences into latent em- beddings, over which a linear classifier was trained. The input representation dimensionality varied across models: PhDChDNA, Caduceus -phussspphhhh and HyenaDNA, produced 256-dimensional embed- dings, while GENA-LM-Bace and NT-MS -M-M00M yielded 768 and 1024-dimensional embeddings, respectively.
http://wwgnnaaaaacooooossss///ee transformer downstream tasks revised
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All models were evaluated under identical training conditions: batch size of 64, total of 50 train- ing epochs, and optimization using Adam W with learning rate of 5e 4 and weight decay of 0.01. For each model and seed, report performance tion Coefficient (MCC), averaged across all runs. Full per-task results with standard deviations are presented in Supplementary Table TO
the official test set using Matthews Correla-
Table 10: Detailed performance across all 18 tasks in the Nucleotide Transformer Benchmark.
Dataset
promoter_all
promotcr_no.o toot
promoter.tata
enhancers
cnlsincers.typss
splice.sitos
PatchDNA
+ 0.007
0.786 = 0.003
0.853 0.009
+ 0.004
0.441 : 0.005
0.006
PatchDNA Entropy
0.719 : 0.007
0.743 0.003
0,749 0.04
0.454 0.01
0.421 0.008
0,497 0.005
Cadureux ph
0.679 +: 0.001
0.727 +: 0.002
0.67 a: 0.0
D.429 4: 0.002
0.39 =: 0.002
0.448 0.002
HyenaDNA
0.712 0.002
0.729 0.001
0.71 + 0.009
0.414 + 0.005
0.38 + 0.004
0.391 0.009
GENA-LM-Bann M-Baase
0.7 0.008
0,741 + 0.013
0.707 + 0.02
0.488 + 0.01
0.452 0.008
0,54 # 0. 0.7
NT.MS-500M
0.741
0.003
0.005
McDNA
0.729 + 0.006
0.757 0.007
0.759 0.011
0.011
0.458 0.004
0.428 + 0.004
0.57 + 0.004
Datasct
splice.sites
splice doners
H2AFZ
H3K27ac
H3K27mc3
H3K36me3
Model
PatchDNA
0.454 0.018
0.692 0.014
0.396 0.005
0.41 0,022
0.557 = 0,004
0.542 0.004
PatchDNA Entropy
0.311 + 0.011
0.512 + 0.007
0.401 0.007
0.352 + 0.008
0.529 = 0.004
0.498 + 0.006
Caduceus-ph
0.267 0.002
0.455 0.003
0.337 0.002
0.28 0.003
0.476 0.003
0.354 0.003
HyenaDNA
0.258 1 0.008
0,387 1 0.005
0.444 = 0.004
0.375 4 0.003
0.507 = 0.002
0.498 : 0.001
GENA-LM M-BB
NT-MS-500M
0.336 + 0.008
0.509 + 0.004
0.392 0.005
0.398 + 0.004
0,536 = 0,004
0.496 0.006
MxDNA
0.34 0.006
0,585 0.006
0,366 = 0.004
0.363 + 0.004
0.532 = 0.003
0,474 0.006
Dataset
H334mel
H334mm2
H3K4me3
H3K9ac
H3399m33
114K20ee
Model
PatchDNA
0.406 + 0.009
0.459 0.004
0.614 0.006
0.47 0.011
0.393 0.012
0.576 + 0.008
PatchDNA Entropy
0.381 0.009
0.457 0.013
0.583 0.006
0.458 0.023
0,346 0.009
0.554 0.005
Caduccus-ph
0.333 2 0.001
0.403 = 0.005
0.489 : 0.002
0.379 0.005
0,214 0.007
0.513 : 0.004
HyenaDNA
0.387
0.485
0.554
GENA-LM-BE. MM-Basc
0.42 + 0.01
0,486 0.006
0.624 + 0.011
0,5 0,005
0,4 0.013
0.604 + 0.01
TE-MS-500M
0.391 : 0.000
0.47 : 0.005
0.622 0.007
0.514 : 0.005
0,304 0.016
0.561 1 0.001
McDNA
0.387 0.004
0.458 0.003
0.568
0.463 1 0.013
0.36 0.016
0.559 0.003
FINETUNING RESULTS
Using the same finetuning protocol and hyperparameters as QQiao et al. 2024), fully finetune all models across seeds for a maximum of 20 epochs. The results in Table II show that PatchDNA performs strongly across the benchmark, outperforming all other models in 11 out of 18 tasks.
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Table 11: Comparison of finetuning results on NT benchmark.
Dataset
promoter_all
promotcr.no.ket totttat
promoter.taaa
enhancers
cnbaincrs..typs
Model
PatchDNA
0.791 + 0.009
0.788 # 0.005
0.84 =: 0.019
0.528 +. 0.009
0.496 0.008
0.754 = 0.04
PatchDNA Entropy
0.725 + 0.008
0.007
0.785 0.016
0.523 + 0.001
0.488 0.008
0.868 0.015
Caduceus-ps
0.742 + 0.01
0.764 + 0.013
0,761 = 0.028
0.51 + 0.017
0.471 # 0.006
0,765 0.006
HyenaDNA
0.693 0.007
0.724 0.004
0.831 0.057
0.479 0.005
0.45
0.82 0.015
GENA-LM Base
0.738 a: 0.007
0.736 a: 0.025
0.689 =: 0.038
0.483 +: 0.023
0.467 a: 0.012
0.76 a 01000
NT. 100M
0.737 +
0,818
0.001
0.478 0.002
0,952 0.002
MxDNA
0.734 + 0.013
0,755 0.01
0.831 0.038
0.519 + 0.014
0,48 + 0.01
0.812 0.052.
PhyloP
0.405 + 0.002
0.393 # 0.006
0.469 = 0.006
0.181 +: 0.007
0.167 + 0.002
0.543 0.001
Dataset
splice_sites_all
splice_sitssdnnors
H2AF7.
H3K27ac
H3K27mea
H3K36me3
Model
PatchDNA
0.76 + 0.019
0.706 0.026
0.523 = 0.01
0.486 = 0.015
0.607
0.008
0.621 = 0.007
PacchDNA Entropy
0.884 0.013
0.654 0.016
0.009
0.035
0.595
0.584 0.02
Caduceus- ps
0.796 0.021
0,771 + 0.013
0,507 + 0.007
0.475 = 0.021
0.591 0.009
0.607 + 0.008
HyenaDNA
0.849 0.006
0.84 0.029
0,481 0.005
0.44 0.003
0.554 0.014
0.549 + 0.002
GENA-LM-Base
0.764 0.013
0,781 0.004
0,466 0.035
0.495 0.01
0.588 0.004
0.602 : 0.021
NT-MS-100M
0.966 0.0
0,962 2 0.003
0.501 0.009
0.496 = 0.009
0.599 0.009
0.617 4: 0.004
McDNA
0.86 + 0.007
0,931 0.021
0.512 0.003
0.489 0.031
0.599 0.0155
0.618 + 0.002
PhyloP
0.283 0.004
0.547 + 0.001
-0.017 + 0.062
0,105 = 0.028
0.233 + 0.032
0,304 + 0.003
Datasel
11344mel
113K4me2
113K4me3
H3399cc
113K9me3
204e2
Model
PatchDNA
0.48 0,003
0,573 + 0.004
0.634 :: 0.005
0.569 a: 0.01
0.47 + 0.017
0.637 a: 0.007
PatchDNA Entropy
0.472 0.011
0.021
0.546 0.009
0.473 0.019
0.626
Caduccus-ps
0.471 + 0.014
0,565 0.008
0.617 0.009
0.526 + 0.009
0,435 1.5:=0000 0.015
0.639 + 0.009
HyenaDNA
0.438 + 0.007
0.523 + 0.025
0.618 + 0.0m
0.497 + 0.014
0.371 + 0.026
0.617 + 0.008
GENA-LM-Bass
0,465 0.014
0.538 0.027
0.61 0.055
0.525 0,007
0.44 0.009
0.644 0.011
NT-MS--10M
0.487 0.01
0,551 = 0.005
0.624 : 0.003
0.531 : 0.002
0.469 = 0.006
0.646 : 0.01
McDNA
0.497
0.012
0.627 0.017
0.015
0.023
PhyloP
0.006 + 0.041
.0.02 + 0.058
0.009 0.084
0.026 + 0.041
0.072 0.035
0.059 + 0.103
On splice sites, (Lindsey et al., 2025) show that model trained with single nucleotide tokenization significantly outperforms an equivalent BPE based model. They also postulate that consistent token size facilitates the model's ss learning of specific distances for these tasks. Inspired by this, we re- re-patch the model after pretraining, using single nucleotide patching with the same base model. Table 12 shows the expected improvement demonstrated by prior work. Although single-nuclcotide patch- ing introduces additional computational overhead during fine-tuning. re-patching avoids the costly requirement of pretraining from scratch at this resolution.
Table 12: Comparison of PatchDNA vs PatchDNA with single nucleotide re- re-patching on splice site tasks.
PatchDNA
PatchDNA re-patch size I
Pre-training patching
PhyloP PhyloP
PhyloP Single Nucleotide
Finetuning Patching
splice sites acceptors
0.754 + 0.040
0.946 + 0.002
splice_sites sites_an all
0.760 - 0.019
0.953 + 0.006
splice_sites.de -iotors
0.706 + 0.026
0.948 + 0.002
A.3.2 DART-EVAL
We evaluated our model's performance by adding it to each task using the original evaluation code provided by the authors at maintained the original experimental setup and report the published results for all other baseline models directly from the original paper (Patel et al. 2024). We use A100 80GB GPU for each task.
To ensure consistency, we
For Task and Task 2, we use the zero-shot likelihoods formulation, while for Task 5, we apply the zero-shot embeddings approach. When both likelihoods and embeddings could be used, we choose
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between them based the relative performance of models across tasks. For example, in Task 2, embeddings from all DNA models perform significantly worse than likelihoods, making the latter the preferred choice. For Task 2, we report median accuracy.
For Task and Task 4, where no zero-shot formulation exists, a lightweight probe is trained on top of frozen model embeddings.
For Task 2, no conservation scores are available, so we re- re-patch to single nucleotide scores at infer- ence time, showing the flexibility of our modeling approach when conservation scores are unavail- able.
VISUALIZATION OF OVERALL PERFORMANCE
Task
PatchDNA
Task
85k
HyenaDNA
GENA-LM-Large
NT-MS-500M
Caduceus-ps
DNABERTZ
MistralDNA
Task
Bisk 3
Figure 3: Radar plot showing normalized performance across all five tasks, with the best and worst performance for each task scaled to and 0 respectively. Each axis corresponds to a different task, and larger enclosed area indicates stronger overall performance.
EXTENDED RESULTS
We present extended results for Tasks 3, 4 and 5 in Tables 13 15 14 In the main results in the paper, we report the Overall Accuracy for Task 3, the mean Spearman across the cell types for Task 4, and the mean AUROC for Task 5.
Table 13: Accuracy and AUROC across different cell types for Task 3 in DART-Eval
Model
Overall Accuracy
GM12878
HIESC
HEPG2
IMR90
K562
PatchDNA
0.457
0.740
0.817
0,806
0.783
0.710
Caduceus
0.281
0.535
0.622
0.680
0.576
0.587
DNABERT2
0.371
0.652
0.757
0.762
0.691
0.691
GENA-LM-Large
0,383
0.627
0.787
0.773
0.714
0.693
HyenaDNA
0.587
0.849
0.889
0.862
0.882
0.799
Mistral-DNA
0.329
0.582
0.678
0.723
0.643
0.646
NT-MS-500M
0.420
0.744
0.795
0.783
0.779
0.711
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Table 14: Zero-shot AUROC performance using embedding- predictions for African and
Yoruban datasets for Task 5 in DART-Eval
Model
African AUROC
Yoruban AUROC
PatchDNA
0.545
0.564
Caduceus
0.519
0.508
DNABERT2
0.480
0.505
GENA-LM-
0.508
0.501
HyenaDNA
0,515
0.515
Mistral-DNA
0.520
0.475
NT-MS-500M
0.519
0.613
Table 15: Spearman among positives across five cell types for Task 4 in DART-Eval
Model
GM12878
HIESC
HEPG2
IMR90
K562
PatchDNA
0,434
0.636
0.400
0.319
0.412
Caduceus
0.251
0.371
0.312
0.149
0.401
DNABERT2
0.395
0.584
0.357
0.275
0.483
GENA-LM-Large
0.490
0.678
0.401
0.329
0.461
HyenaDNA
0.362
0.538
0.345
0.237
0.438
Mistral-DNA
0,293
0.500
0.349
0.244
0.431
NT-MS-500M
0.410
0.595
0.337
0.270
0.499
A.3.3 BEND BENCHMARK
This section reports on the remaining BEND benchmark Marin et al. 2024). Table focuses short- and medium-range tasks, whereas the BEND enhancer task requires 100 kbp inputs. We evaluate this task only for models that can process such long sequences in a single forward pass. Specifically, we compare PatchDNA-7M (131 kbp context) with HyenaDNA Large and Caduceus- ph (Table The enhancer task is a binary classification task that predicts whether a 128 bp region lies within an enhancer, using a 100 kbp surrounding context.
We additionally report zero-shot performance on the BEND Variant Effect Prediction (VEP) tasks, see Table As highlighted by Dart-Eval (Patel al., (024). these tasks do not account for linkage disequilibrium, leading to potentially noisy labels and reduced reliability. For this reason, we include the results for completeness but exclude them from the primary comparisons.
Table 16: Enhancer annotation task performance. These results compare models capable of handling 100kbp sequence length inputs in a single pass. We report AUPRC across 10 cross validation folds.
Model
AUPRC
PatchDNA (131k)
0.037 + 0.026
HyenaDNA Large
0.031 + 0.019
Caduceus-ph
0.032 + 0.020
Table 17: Zero-shot variant effect prediction (VEP) performance terms of AUROC.
Model
eQTL AUROC
Disease AUROC
PatchDNA
0.49
0.82
HyenaDNA
0.51
0.45
GENA-LM Large
0.49
0.55
NT-MS-500M
0.48
0.48
DNABERT-2
0.49
0.51
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A.3.4 CAGE PREDICTION BENCHMARK
We use the CAGE dataset from hups://huggingfacee http://wwggniiaacc.ccoottssssttt coldtraet//nssaDep//lggnnmmm ronger benchmark, consisting of 50 CAGE tracks selected from the original 638 in the Basenji dataset.
Each model receives a sequence of 1 14,688 single nucleotides. We extract per-nucleotide embed- dings and pass them through a two-layer MLP. where the hidden dimension is set to twice the embedding size and the output dimension is 50, following the setup in Brixi et al. (2025). The MLP outputs are mean-pooled over non-overlapping windows of 128 nucleotides, resulting in final out- put of shape 896x50.
Training is performed using the Poisson negative log-likelihoode loss, in Enformer (Avsec et al. 2021]. We fully finetune each model for one epoch, consistent with Brixi al. (2025). We use the Adam optimizer, with a learning rate of 5e 5 and total batch size of 8.
For baseline models, HyenaDNA, Caduceus-ps and Caduceus-ph we use the pretrained weights available via Hugging Face, with model identifiers listed in T
For regulatory element based patching, we use annotations from Moore et al. (2020), creating a score function, 9p that assigns value of to nucleotides in these regions, and 0 otherwise. We then use patching threshold, Pp. of 0.99.
All experiments are repeated with five random seeds. We report the mean and standard deviation of performance on the test set, using the same metrics as Avsec et al. (202I). described Scction A. 10 Finetuning runtimes for one epoch are reported in Table 18
Table 18: One epoch finetuning time and FLOPS for various models, using 4 A100 80GB GPUs on CAGE prediction benchmark. The peak VRAM usage is normalised by batch size.
Model
Time (minutes)
FWD FLOPS (G)
Peak VRAM usage (GB)
PatchDNA
22.4
678.60
14.1
HyenaDNA
76.6
1493.96
20.4
Caduceus-ph
99.2
3142.71
18.1
Caduceus- 238.3
6285.42
36.2
A.3.5 CELL TYPE SPECIFIC RE-PATCHING
We pick paired CAGE-DNase tracks from the Basenji dataset (Kelley 20220), focusing on Neurons, Hepatocytes and K562. The ids for the tracks that we used are in Table 19 We keep the same train/validation/eest splits. For each cell type we follow the same protocol outlined in Section A.3.4 where instead of predicting 50 tracks we predict only 1 track. Since only track is predicted, we opt to focus on cell correlation.
DNase patching details The DNase-seq data used for patching were obtained from the ENCODE Project portal (https: www. encodepro ject. org/) using the ENCODE ids in Table 19 We use patching threshold, 0p. of 0.99 for all DNase sources.
Table 19: Dataset identifiers for paired DNase-seq and CAGE expression tracks used in the cell- type-specific prediction task.
Cell Type
DNase ENCODE ID
CAGE FANTOM5 ID
K562
ENCFF413AHU
CNhs I 1250
Hepatocyte Neuron
ENCFF136YOI ENCFF399ISP
CNhs I 12338
CNhs I 12338
A.3.6 THE GENOMICS LONG RANGE BENCHMARK
To evaluate performance additional long-range prediction tasks, the Genomics Long Range Benchmark (Trop et al. 2025). We use a sequence length of 131 kbp. and restrict com- parisons architectures capable of processing this full context in single forward pass, namely
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HyenaDNA and Caduceus-ph (Caduceus-ps is excluded as it is much slower to run, Table 18
Following the benchmark protocol, we adopt the authors' recommended hyperparameters and re- port the mean performance across five random seeds on the designated held-out test set (Table 20 Across these tasks, PatchDNN-77M delivers competitive or superior results, outperforming baseline models on 6 out of 7 tasks.
We further show results on the zeroshot tasks in Table 21 We use the same protocol for extracting zero shot scores detailed in (Trop al. 2025) for masked language models (Caduceus-ph), and autoregressive models (PatchDNA, HyenaDNA). On Pathogenic Clinvar, our model outperforms HyenaDNA and Caduceus-ph. On the Causal eQTL and OMIM tasks, all models perform close to random, consistent with the reported difficulties that DNA language models have in the zero shot setting on these tasks (Trop et al. 2025).
Table 20: Performance on Genomics Long Range Benchmark on all finetuning tasks. Results are reported across 5 seeds the held out test
Model
Causal eQTL.
Pathogenic ClinVar
Bulk RNA
Histone
Accessibility
Promoter
Enhuneer
AURor
Attane:
8²2
PaRchDNA-7M
0.714 = 0.005
0.796
0.009
0,500
0.005
0,289
0.005
0,179 1 0,012
0.783 E 6.021
0,835
0,002
HyenaDNA
0.704
0.037
Caduceass ph
0.717 0.010
0.699 # 0.010
0,491
0.014
0.215
0.004
0.118 # 0.005
0.734
0.025
0.822 0.002
Table 21: Performance on Genomics Long Range Benchmark zeroshot tasks.
Model
Causal eQTL
Pathogenic Clin Var
OMIM
AUROC
AUROC
AUPRC
PatchDNA-7M
0.487
0.586
0.00208
Caduceus-ph ph HyenaDNA
0.479
0.501 0.494
0.00177
0.481
0.00187
A.4 ABLATIONS
We present ablations to assess (i) how conservation score only baseline performs, (ii) the effective- ness of conservation based patching versus entropy and fixed size patching, and (iii) the contribution of patching and the BLT architecture itself.
Pat ChhDNA-Enttoon and PatchDNA -ec.DANA--ixedP22222 and fixed- patching matched efficiency conservation- basdd patching (see Section [A.2] hyper- parameters). For reference, we also include NT-MS--00M the largest baseline in our benchmarks (500M parameters, multi-species).
xedPS20 are pretrained and evaluated with entropy-
We also construct a PhyloP baseline. For sequence-level tasks, PhyloP scores are pooled across the sequence by summation. For binary classification, we report AUROC or MCC depending on the established metric in literature for the benchmark. In the case of MCC, we fit a small linear probe the scores to obtain binary predictions. For multiclass classification, we train probe PhyloP scores. For regression, we report direct correlation between scores and labels. For variant effect prediction, we follow BBixi et al. (2025) by taking the PhyloP score at the variant site and computing AUROC against effect/nn-effect labels.
Across benchmarks, conservation-Be based patching outperforms entropy and fixed patching, high- lighting the advantage of a biologically informed patching strategy. Furthermore, entropy and fixed patching remain strong competitors patching and the BLT architecture itself in DNA modeling. We also directly examine the relationship between conservation scores and task labels. We find that conservation scores alone cannot repro- duce model performance. In most tasks, there is weak still outperforms baselines. In tasks where conservation is more predictive, conservation based patching achieves substantial gains over the PhyloP baseline.
comparison to WT-MS-500M highlighting the strength of
correlation to labels, and P Patch ChDNA
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Table 22: Performance across NT benchmark, with linear probe on top of model embeddings. All results are using MCC. PhyloP baseline uses linear probe.
PatchDNA
MilchDAA-Entropg
PatchDNA-FixedPS5
NT-MSS500M
PhyloP
H2AFZ
0,396 + 0.005
0.401 = 0.007
0.405 0.005
0.392 + 0.005
-0.017 0.062
HSK27ac
0.386
4:
H3K27me3
0,557 + 0.004
0.529 0.004
0.522 0.008
0.536 + 0.004
0.233 4 0.032
H3K36me3
0.542 + 0.004
0.498 0.006
0.486 0.017
0.496 4: 0.006
0.304 + 0.003
H3K4mel
0.009
H3K4mm22
0,459 + 0.004
0.457 0.013
0.489 0.012
0,470 + 0.005
-0.020 = 0.058
H3K4me3
0.614 + 0.006
0.583 = 0.0066
0.592 0.010
0.622 + 0.007
0.009 + 0.084
H3K9uc
+
0.514
H3K9mel7
0.393 + 0.012
0.346 0.009
0.350 0.013
0.304 + 0.016
0.072 1 0.035
H4K20mee
0.576 +: 0.008
0.554 0.005
0,563 0.005
0.561 + 0.001
0.059 4: 0.103
0.004
0.454
0,448
0.485 + 0.003
0.181 + 0.007
enhancers types
0.441 + 0.005
0.421 0.008
0.413 0.014
0,445 + 0.003
0.167 + 0.002
promoter
0.779 +: 0.007
0.719 0.007
0.719 0.005
0.718 + 0.003
0.405 1 0.002
promoter atttta
0.786 0.003
0.743
0.751
0.393 0.006
promoterr Jala
0.853 + 0.009
0.749 0.040
0.765 0.018
0.683 + 0.032
0.469 1 0.006
splice sites acceptors
+ 0.006
0.497 0.005
0,512 0.012
0.468 + 0.005
0,543 4 0.001
splice sites all
0.454 0.018
0.311
0.310 0.013
0.336 - 0.005
0.283 : 0.004
splice sites donors
0.692 1 0.014
0.512 0.007
0,521 0.019
0.509 1 0.004
0.547 1 0.001
Table 23: Performance on DART-Eval, Task 3 a way classification task, where random perfor-
mance is approximately 0.200. Task 4 is a regression task, Task 5 is variant effect prediction task.
Model
Task Accuracy
Task 2
Task 3
Task 4
Task 5
Accuracy
Accuncy
Spearman
AUROC
PatchDNA
0.966
0.725
0.457
0.440
0.555
0.967
0.675
0.477
0.417
0.539
PatchDNA-Entropy
0.965
0.650
0.465
0.400
0.523
NT-MS-5000
0.745
0.565
0.420
0.422
0.566
PhyloP
N/A
N/A
0.260
0.027
0.536
Table 24: Performance across BEND short and long range tasks. Gene finding is multi class classification task, reported with MCC, while other tasks are binary classification.
Model
Gene finding Chromatin accessibility Histone modification CpG Methylation
MCC
AUROC
AUROC
AUROC
PatchDNA 0.58
0.84
0.79
0.92
BacchDNA-FixedPS.
0.38
0.83
0.78
0.90
PatchDNA-EEme Entrrop
0.37
0.83
0.78
0.90
NT-MS-500M
0.64
0.80
0.76
0.91
PhyloP
0.19
0.54
0.51
0.49
A.5 SCALING PATCHDNA TO MULTIPLE SPECIES AND TO MULTIPLE GENOMES WITHIN THE SAME SPECIES
PhyloP scores quantify conservation at each genomic position using multi-specier alignments. By leveraging evolutionary constraints, biologically relevant indicators of functional importance across species, the conservation-bast patching approach is conceptually robust. Since PhyloP tracks are available for many organisms and genome assemblies, extending our model to multi-species frame- work is straightforward.
We constructed a mouse version the CAGE prediction task, by selecting 50 mouse CAGE tracks from the Basenji dataset. We applied PatchDNA using PhyloP conservation scores from the 60- way multi-species alignment for mouse (mm1 0. 60way phylop 60way .bw. The setup was the same in Section 4.4; all models were fine-tuned for epoch using MLP head. Due to time constraints, we report only the Full Pearson correlation between predicted and observed CAGE signal across gene-cell pairs (computing gene-wise and cell-wise correlations required incorporating
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transcription start site annotations for the mouse genome, which plan include in the final version). Despite being trained on the human genome, PatchDNA achieves strong performance on this task, outperforming HyenaDNA. This result highlights PatchDNA's ability to generalize across species, leveraging evolutionary priors without retraining.
Results reported the test set, averaged across 6 random seeds.
Model
Full Pearson
HyenaDNA- Im-----l-eeeen
0.219 = 0.004
PatchDNA-7M
0.338 - 0.004
Table 25: Mouse CAGE prediction results using conservation-based PatchDNA.
A.6 PSEUDOCODE FOR RE- RE-PATCHING
We also provide simplified algorithm for establishing the patch boundaries below:
Algorithm DetectPatchBoundaries
Require: Input byte sequence input of length L; genome scores genome_scores of length L; threshold -
Ensure: List houndariess
1: Initialize empty list patch_boundarie +-
2: append-front
patch boundaries
3: append-front () to patch boundaries
4: for + T- do
5: if' genome_scoresqi > then
6: append i patch_boundaries
7: end if
8: end for
9: return patch. bounddriessss
Integrating re-patching is straightforward. The patchDNA backhone accepts patch ng mode argument specifying the patching strategy, which dynamically defines the patch boundaries. These boundaries are used by the local encoder and decoders determine how patches interact via cross- attention. This method is entirely data driven and does not require retraining. Below is a simple example:
model PatchDNA, data_cig =
("best.ckph) ckpt.) Trained with Phylop
"genome_scoze score, in": Sinase_k5621 instead of phylop
other cig items
dataset Dataset. from_config (data_offff
model www.itecttueeeeecee...... patcher. wwatchhhr..rreshhhhe threshcld 0.99
aadellaarchhtecctur.ppptceee..e mode "
inference or finetune loop
preds trainer. validate (model, datamolle)
A.7 ALTERNATIVE CONSERVATION SCORES AND SENSITIVITY TO THRESHOLDS
PhastCons is an alternative conservation scoring method, but we deprioritized using it due to its Hindow-based smoothing which results in lack of single nucleotide granularity. We present results in Table 27 showing that underperforms compared to PhyloP on out of the tasks.
We pick the 95% threshold for efficiency reasons, this allows to easily train models long sequences. Lower thresholds result more number of patches, on average, increasing the computa- tional cost. However, investigate performance other thresholds, we' ve thresholddsensiiiviity
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analyses for Dart-eval the 7araammmterr PatchDNA using less stringent cutoffs (Task 4 was omitted due to increased computational costs), We highlight that performance does not change significantly between various thresholds (Table26).
Table 26: Performance comparison of PatchDNA-7M variants subset of Dart-eval tasks
Model
Avg. Patch Size
Task
Task 2
Task 3
Task 5
Accuracy
Accuracy
Accuracy
AUROC
PatchDNA-7M 75%
4
0,938
0.645
0.343
0.524
PatchDNA-7M 90%
10
0.940
0.650
0.357
0.525
PatchDNA-7M 95%
20
0.950
0.650
0.380
0.539
Table 27: Performance comparison of PatchDNA-7M with PhastCon subset of Dart-eval tasks
Model
Avg. Patch Size
Task 1
Task 2
Task
Task 5
Accuracy
Accuracy
Accuracy
AUROC
PatchDNA- 75% PhastCon
0.882
0.615
0.332
0.534
PatchDNA-7MM 90% PhastCon
10
0.932
0.645
0.326
0.542
PatchDNA-7M 95% PhastCon
20
0.943
0.640
0.333
0.549
We also investigate the impact of varying the threshold across broad of downstream tasks via re-patching. We use the main PatchDNA model, which has been trained using PhyloP threshold which results in average patch size of 20. We then identify thresholds corresponding average patch sizes of 4, 10, 20, 40, 60, and 80, and evaluate the model with these alternative thresholds on both short-range and long-range tasks (Table 28 and Table 29
In Table 28 we observe that smaller average patch sizes generally yield the best performance, al- though the improvements are modest for the regulatory element and chromatin profile tasks (Figure 4p. For the splice-site tasks, the differences are more pronounced, which is expected given that these tasks benefit from finer-grained sequence resolution (see Table
Section A.3.I).
Performance declines gradually as the average patch size increases, but the drop is modest: even at the largest patch size of 80, PatchDNA still outperforms the second-best model in the benchmark (Caduceus-ps).
A similar trend appears for the long-range CAGE task (Table 29
These results suggest that while finer patching can provide advantages, particularly for tasks requir- ing high-resolution sequence information, the model remains broadly robust across a wide range of patch sizes. Notably, smaller patch sizes incur higher computational cost, as they increase the number of patches that must be processed. In conventional tokenization schemes, modifying analogous parameter (such as the k-mer tokenization) would necessitate training a new founda- tion model from scratch. In contrast, our framework enables users re re-patch post hoc to achieve smaller effective patch sizes, avoiding the substantial computational expense of pretraining a new model.
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Table 28: Results on the finetuned Nucleotide Transformer benchmark using PatchDNA model pretrained with conservation scores and re- re-patched at different average patch sizes. Test MCC is shown and averaged across 3 seeds with reported standard deviations.
Task
Average Patch Size
60
80
H2AFZ
0.517
0.511 0.01
0.523
0.014
E 0.015
0.009
H3K27ac
0,526 = 0.017
0.510 0.020
0.486
0..015
0.495
0.036
0.487 1 0.045
0.486 4: 0.032
H33K27me3
0.616 + 0.011
0.614 0.016
0.607
-0.08
0.590
0.014
0.588 + 0.026
0.596 + 0.011
H3K36me3
0.632
0.009
H3K4mel
0,489 + 0.004
0,475 = 0.009
0.480
0.003
0.011 0.476
0.474 + 0.002
0,473 0.010
H334mm22
0,570 + 0.003
0,581 = 0.017
0.573
0.004
0.004 0.570
0.570 + 0.007
0.575 + 0.003
H3K4me3
H3K9ac
0,589 + 0.012
0.572 = 0.007
0.569
0.010
0.011 0.565
0.567 + 0.015
0.556 + 0.007
H3K9mm3
0.485 + 0.021
0.480 = 0.018
0.470
0.017
0.039 0.495
0.473 + 0.032
0.475 + 0.027
H4K20mel
0.670 + 0.008
0.650 0.006
0.637
0.007
0.003 0.635
0.626 + 0.015
0.627 + 0.009
enhancers
0,554 0.005
0.536 0.013
0.528
0.532 0.001
0.524 + 0.005
enhancers types
0.519 + 0.012
0.501 + 0.014
0.496 0.008
0.484 + 0.021
0.492 + 0.004
0.497 + 0.007
promoter.all
0.781 + 0.012
0.792 0.012
0.791
0.009
0.781 0.003
0.791 + 0.008
0.783 + 0.010
promoter_no.tan
0.797 0.008
0.795 0.012
0.788
0.796 0.012
0.794 0.006
0.783 0.008
promoter_ tata
0.875 + 0.013
0.829 + 0.150
0.840 0.019
0.847 + 0.024
0.843 + 0.011
0,830 + 0.010
splice sites acceptors
0.868 + 0.029
0.741 = 0.026
0.754 0.040
0.748 = 0.044
0.778 + 0.040
0.746 +: 0.040
splice sites all
0.849 0.004
0.789 0,008
0.760
0.019
0.016
0.803 0.083
0.778 : 0.084
splice_sites.dee site__stts_doorr
0.744 + 0.005
0.721 + 0.024
0.706
0.026
0.714 0.029
0,705 + 0.029
0.690 + 0.012
Average patch size
Patch
Patch 20
Patch 40
0.8
Patch 80
Mcan Test MCC
0.7
0.4
0.3
Regulatory elements
Splice sites
Chromatin profiles
Figure 4:
Results on finetuned Nucleotide Transformer benchmark, grouping tasks by category Using PatchDNA model pretrained with conservation scores and re- re-patched at different
(Table 28
average patch sizes
Table 29: Results on CAGE benchmark using a PatchDNA model pretrained with conservation and re-patched different average patch sizes. Note that we exclude average patch size 4, as this would be inefficient at a long sequence of < 100kbp. Results are averaged across seeds, with reported standard deviations.
Average Patch Size
Gene Corr
Cell Corr
Full Pearson
Forward FLOPS
10
0.369 = 0.001
0.773 # 0.0007
0.482 + 0.000
855,64
20
0.369 # 0.001
0.772 # 0.0018
0.471 0.002
678.55
40
0.368 = 0.000
0.770 # 0.0015
0.454 + 0.005
627.88
60
0.367 = 0.002
0.767 + 0.0005
0.440 0.003
616.61
80
0.367 = 0.002
0.769 # 0.0018
0.425 0.004
612.02
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Published as a conference paper at ICLR 2026
A.8 INTERPRETABILITY QUANTITATIVE ANALYSIS OF PATCH ALIGNMENT WITH CCRES
We believe that interpretability, particularly the alignment of patches with known functional genomic elements, important. To address this, implemented an additional quantitative analysis compar- ing the enrichment of -derivedd patches specifically within cCRE versus non-cCRE genomic regions. We used 5 independent random seeds, each with 5000 sampled genomic intervals of length 350 bp. For regulatory regions, we centered the windows on known cCREs (from ENCODE), while control intervals were drawn from the genome avoid any overlap with cCRE annotations. Using Mann-Whitney U test, we found that there were significantly more PhyloP-derive patches (median difference: 32, Cliff's = 0.618, p < 0.001) within cCRE regions relative to randomly sampled non-cCRE genomic windows.
Further, we compared the number of patches identified by entropy and PhyloP scores within cCRE regions using the Wilcoxon signed-rank test. PhyloP-dae -eerived patches consistently identified signifi- cantly more patches per region than entropy-derived patches (median difference: 12 patches, Cliff's 5 0.155, Wilcoxon < 0.001). While this effect is statistically robust across seeds, the effect sizes are smaller than those observed in the cCRE vs. control comparisons.
A.9 COMPUTATIONAL. EFFICIENCY OF PATCHING AND RE-PATCHING
The re-patching itself incurs no additional computational overhead: the local encoder and decoder already expect a patch-baseds layout, which be swapped in without changing the architecture. The patch size distribution will have direct effect on computations. The computational complexity of marking patch boundaries an (L) operation (with L being the sequence length); make single pass over the sequence, inserting boundaries whenever pre-established threshold is reached. In our implementation this step runs on the CPU, though an entropy-bar -based patching strategy would necessitate executing small model on the GPU and will have different computational complexity considerations. clarify this further, we present the theoretical computational cost (in GFLOPs) in Table comparing P PatchDNA directly against its Angle-nucleotice baseline, where the patch size fixed at These theoretical estimates were calculated using the formulas described the BLT paper, as the BLT implementation uses Altennttn (which Pytorch FLOP profilers don't 't support).
Table 30: Forward FL.OPs comparison across models different sequence lengths.
Model
511 bp FWD (G)
16 kbp FWD FLOPS (G)
PatchDNA (19.2 M)
5.64
179.07
Single-nueleotide baseline (19.2 M)
11.80
1384.53
PatchDNA (7.7 M)
2.79
88.6
Single-nucleotide baseline (7.7 M)
5.36
548.62
A.10 METRICS
Matthews Correlation Coefficient (MCC) The Matthews Correlation Coefficient is robust sta- tistical rate which takes into account true and false positives and negatives and is regarded as a balanced measure that can be used even if the classes are of very different sizes.
TP x TN FP FN V(PP+FP)TTP FN)(TN + FP) + FN)
MCC
where TP, TN, FP, and FN are the numbers of true positives, true negatives, false positives, and false negatives, respectively.
A.10.1 ENFORMER EVAL UATION METRICS
To assess model performance in predicting gene expression, we follow Pearson correlation evalu- ation strategies proposed in the Enformer manuscript (Avsec al. 2021). The following three metrics are used to evaluate model predictions: gene correlation, cell correlation, and full correla- tion.
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Published as a conference paper at ICLR 2026
Let W R and W R denote the predicted and observed CAGE matrices across the genome, where B is the number of genomic bins (each spanning 128 base pairs) and is the number of cell types.
To obtain gene-level predictions, extract the row of W and W corresponding to the bin that contains the transcription start site (TSS) of each gene. This gives the predicted and observed gene expression matrices Y, Y E RGCC where G the number of genes.
Gene Correlation Gene correlation evaluates how well the model captures cell sype-specifie ex- pression patterns for each gene. Prior to computing this metric, both predicted and observed gene expression values are log-transformed as:
Y + log(Y 1), Y + log(Y +1
For each gene g E {l...., G}, we compute the Pearson correlation across all cell types:
rgge
corr(Y, g.: Yg:))
The final gene correlation score is the average over all genes:
G
gene
gene
9=1
Cell Correlation Cell correlation evaluates how well the model predicts gene expression patterss across genes within each cell type. As with gene correlation, tagtransffomation is applied to all input values before computing correlation.
For each cell type e E {1,...,C) we compute the Pearson correlation across genes:
yelll corr(Y,,:: Y..))
The final cell correlation score is the average over all cell types:
pelll
sell
Full Correlation Full correlation measures how well the model predicts CAGE signal profile across the genome. For each cell type e E {I...., C}, we compute the Pearson correlation across bins:
c
corr(W.... :es W..)
The final full correlation score is the average over all the cell types
full
full
A.11 LLM USAGE
We have used LLMs to improve grammar and wording throughout the manuscript.
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Reviews:
[{"review_id": "D9wwrf9dl3", "reviewer": "Reviewer_rPgf", "summary": "This paper introduces PatchDNA, which draws on the ideas of the Byte Latent Transformer (BLT) to shift DNA sequence modeling from traditional tokenization to \"patching.\" The authors further introduce patch boundary selection based on conservation signals and a \"re-patching\" mechanism that allows for the redefinition of patch strategies after pre-training, thereby enhancing flexibility and efficiency.\n\nCore Contribution: By leveraging concepts from computer vision (CV) and BLT, the approach transforms the tokenization problem into a \"patching\" problem, completely eliminating the need for a fixed vocabulary. It utilizes external biological signals, such as conservation scores (PhyloP), to guide the delineation of patch boundaries.", "questions": "please refer to the weaknesses part", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The idea is novel, completely eliminating the limitations of a fixed vocabulary and circumventing the rigidity of k-mers and the statistical constraints of BPE. \n- By guiding patch boundaries with conservation scores, it provides a biological inductive bias. \n- Experimental results show performance that is comparable to or even surpasses that of large-scale models, while being more computationally efficient.", "weaknesses": "- Non-End-to-End: The patch boundaries rely entirely on external signals (conservation, entropy), and the model itself does not learn the importance of the regions.\n\n- Insufficient Empirical Support: The performance improvements from re-patching are not adequately quantified, remaining largely at the conceptual level.\n\n- Limited Interpretability: There is a lack of in-depth analysis of the relationship between the representations learned by the model and biological functions.\n\n- High Implementation Complexity: While being \"vocabulary-free\" offers flexibility, it results in poor standardization and reproducibility.\n\n**Improvement Suggestions:**\n\n- Introduce a trainable boundary scoring network to jointly optimize patch segmentation and representation learning.\n- Conduct a quantitative analysis of the actual impact of re-patching on performance and efficiency.\n- Increase the analysis of the overlap between patches and functional regions (conserved regions, regulatory elements).\n- Publicly disclose memory usage and computational load comparisons to ensure the claimed efficiency is credible.\n\n\nBesides, this paper seems to have been previously presented at a NeurIPS Workshop (https://openreview.net/group?id=NeurIPS.cc/2025/Workshop/AI4D3&referrer=%5BHomepage%5D(%2F)#tab-accept-oral); however, my comments do not take this into consideration. I leave it to the AC to make a judgment on this matter.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6T09QqsXzY", "reviewer": "Reviewer_gt3n", "summary": "This paper introduces PatchDNA, a new framework for modeling DNA sequences that replaces traditional fixed tokenization methods with a flexible, dynamic \"patching\" mechanism. The core idea is inspired by the Byte Latent Transformer (BLT) and involves segmenting a DNA sequence into variable-length patches, where the patching strategy is biologically-informed.", "questions": "1. The choice of the patching threshold $θ_p$ seems critical. Could the authors provide more intuition or analysis on the trade-off it governs? For instance, how does the average number of patches and downstream performance change across a wider range of thresholds? Is there a risk of \"over-patching\" (losing too much resolution) or \"under-patching\" (losing efficiency gains) on certain tasks?\n2. In Table 3, the cCRE-aware re-patching improves Gene and Cell Pearson scores but significantly degrades the Full Pearson score (from 0.471 to 0.408),  which is counter-intuitive.  Could the authors provide an explanation for this situation?  \n3. This work demonstrates remarkable parameter efficiency, which is a key goal in model design. Another critical property of successful architectures like the Transformer is their adherence to scaling laws. Have the authors investigated whether the PatchDNA framework exhibits predictable scaling behavior? Specifically, if the authors increase model parameters and training data, how does the method behave in contrast to standard tokenization methods?\n4. In the context of building a general biological foundation model, we might encounter diverse or completely unknown input types, rendering task-specific re-patching impossible. In such a scenario, the choice of the scoring function $g_p$ becomes crucial. How would PatchDNA handle a task where the important regions are anti-correlated with evolutionary conservation? In such a scenario, does the framework offer a way to dynamically adapt the scoring function if a mismatch with the downstream task is detected?\n5. The recently proposed Evo model is state-of-the-art on many genomic benchmarks. Although it is a much larger model, a comparison would be valuable. Have the authors considered benchmarking PatchDNA against Evo on any tasks?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The design of re-patching is biologically logical, which provides alternative tokenization to DNA modeling.\n2. The paper provides extensive experimental results to support its claim.", "weaknesses": "1. Though the application in DNA domain seems to be successful, the methodological contributions are incremental to Byte Latent Transformer (BLT).\n2. The additional experiments are needed to validate the efficiency of the proposed method.   \n2.1 The method's performance depends on a predefined threshold $θ_p$ to create patches. The paper adopts a fixed percentile, but a deeper analysis of how this choice impacts different tasks is missing. Although the appendix (Table 22) shows that performance varies with the threshold, indicating this is a non-trivial parameter to tune, a more comprehensive discussion is needed.  \n2.2 The current models are pre-trained exclusively on the human reference genome. While this is common practice, it limits the model's utility for genomics research on other species. Although Appendix A.5 shows promising zero-shot transfer to mouse, a truly foundational model for genomics should ideally be trained on a multi-species corpus to learn more universal biological principles.   \n2.3 There are missing baselines such as Evo and Evo2.\n3. The model's performance is contributed by the inductive bias that evolutionary conservation is a universal proxy for functional importance. This reliance might become a weakness for tasks where functionally important regions are not conserved. When re-patching with a better signal is not an option (as in the case of a truly novel task), the model might be fundamentally disadvantaged by its pre-training, as the patching strategy itself could be misaligned with the task's biology.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lp6L6WKbvA", "reviewer": "Reviewer_6uT1", "summary": "The paper proposes PatchDNA, a DNA language modeling framework that replaces traditional tokenization with a dynamic, vocabulary-free patching mechanism inspired by the Byte Latent Transformer. Patches are determined by biologically informed scoring functions such as evolutionary conservation, allowing the model to focus computational resources on functionally relevant regions. The framework further introduces re-patching, enabling modification of patch boundaries after pretraining for task or tissue specific adaptation without retraining. Experiments across multiple genomic benchmarks demonstrate improved efficiency, and competitive or superior performance compared to much reported baselines.", "questions": "- Can the authors conduct in-silico mutagenesis to confirm that patch boundaries capture causal biological signals?\n\n- Can the authors report statistical significance for benchmark results and explore (or provide discussion on the potential of) PatchDNA’s performance on unsupervised tasks?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- Innovative representation: Replacing fixed tokenization with dynamic, vocabulary-free patching tailored to DNA sequences is innovative and is a domain (biology) inspired approach.\n\n- Biological grounding: The proposed approach and biological prior grounded. Conservation-guided patch boundaries align model focus with functionally relevant genomic regions.\n\n- Re-patching flexibility: The approach enables changing segmentation (in downstream/post-training) for new cell type or tissue contexts without the need of retraining. This makes is a practically useful one.\n\n- Efficiency: The proposed method has reported to be cable of handles very large context (>100kb) contexts with small models. THis would reduce FLOPs compared to single-nucleotide models.\n\n- Empirical breadth: The authors evaluated PatchDNA on four major genomic benchmarks where the PatchDNA achieve SoTA or near-SoTA results.\n\n- Interpretability: Patch boundaries and conservation scores offer intuitive biological interpretability of learned representations.\n\n- Technical rigor: Clear formulation, hyperparameters (essential for reproducibility), details of the baselines are provided.", "weaknesses": "- Limited testing: I would suggest the author conduct in-silico mutagenesis or perturbation analysis to verify that patches capture causal biological signals. Conducting in-silico mutagenesis is often not that expensive to run with limited computing resources.\n\n- Statistical significance testing: Significance testing or variance reporting is missing. Adding this will clarify the performance gains.\n\n- [Minor] Evaluation paradigms: All the benchmarks are supervised only. It would be interesting to see the PatchDNA's performance on unsupervised or generative task validation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UO6mSCaY2g", "reviewer": "Reviewer_3uWp", "summary": "This paper proposes PatchDNA, a patch‐based alternative to tokenization for DNA sequence modeling. PatchDNA segments the nucleotide sequence into variable-length patches determined by a scoring function.", "questions": "Please see Weaknesses.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The local–global–local stack is well matched to genomics: local attention preserves base-level detail and global attention over patches amortizes long-range reasoning.\n\n2. The paper conducts a broad set of ablation studies, which makes the empirical story more credible and helps clarify what actually drives the gains.", "weaknesses": "1. An advantage is retaining single-base resolution, yet there is limited coverage of base-level benchmarks where this matters most, variant effect prediction (VEP) .\n\n2. The method is positioned as long-context friendly, but long-range evaluation remains thin and not fully controlled. Missing or underdeveloped: Bend or LRB benchmark.\n\n\n3. Ablations need to show how average patch length affects long-range performance.\n\n\n4. Claiming “conservation as boundary is better than conservation as feature” lacks causal evidence and visualization. The paper asserts a strong conclusion without systematic attribution. It is unclear why boundaries confer benefits beyond adding conservation as an auxiliary input.\n\n5. Need to visualize how boundary placement changes long-range motif cooperation.\n\n6. Despite the elegant design, the method often trails or only matches strong baselines on core tasks, suggesting modest practical effectiveness until stronger results are shown under matched settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)