CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models
Authors: Weining Fu
Kai Shu
Kui Xu
Qiangfeng Cliff Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: cryo-EM
structural biology
foundation model
JEPA
SCUNet
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling near-atomic-level visualization of biomolecular assemblies. However, the exponential growth in cryo-EM data throughput and complexity, coupled with diverse downstream analytical tasks, necessitates unified computational frameworks that transcend current task-specific deep learning approaches with limited scalability and generalizability. We present CryoLVM, a foundation model that learns rich structural representations from experimental density maps with resolved structures by leveraging the Joint-Embedding Predictive Architecture (JEPA) integrated with a SCUNet-based backbone, enabling rapid adaptation to various downstream tasks. We further introduce a novel histogram-based distribution alignment loss that accelerates convergence and enhances fine-tuning performance. Through comprehensive evaluation across three critical cryo-EM tasks—density map sharpening, density map super-resolution, and missing wedge restoration—CryoLVM consistently outperforms state-of-the-art baselines across multiple density map quality metrics, confirming its potential as a versatile foundation model for a wide spectrum of cryo-EM applications.

Published as a conference paper at ICLR 2026
CRYOLVM: SELF-SUPERVISED LEARNING FROM CRYO-EM DENSITY MAPS WITH LARGE VISION MODELS
Weining Fu
Kai Shu
Kui Xu
Qiangfeng Cliff Zhang!*
State Key Laboratory of Membrane Biology, Beijing Frontier Research Center of Biological Structures, Peingng Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
2 State Key Laboratory of Membrane Biology-Membrane Structure and Artificial Intelligence Biology Branch, Hangzhou, China
3 State Key Laboratory of Membrane Biology, Beijing Tsinghua Institute for Frontier Interdisciplinary Innovation, Beijing, China
1 These authors contributed equally to this work. Correspondence: xukui. 201 66t sinttusngnaasna org en, www.anggttaaannnnngggnee edu
ABSTRACT
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by en- abling near-atomic-level visualization of biomolecular assemblies. However, the exponential growth in cryo-EM data throughput and complexity, coupled with diverse downstream analytical tasks, necessitates unified computational frame- works that transcend current task-specific deep learning approaches with lim- ited scalability and generalizability. We present CryoLVM,, a foundation model that learns rich structural representations from experimental density maps with resolved structures by leveraging the Joint-Embedding Predictive Architecture (JEPA) integrated with SCUNet-based backbone, enabling rapid adaptation various downstream tasks.
further introduce a novel histogram- -aased dis- tribution alignment loss that accelerates convergence and enhances fine-tuning performance. Through comprehensive evaluation across three critical cryo-EM tasks tasks-density map sharpening, density map super-resolution, and missing wedge restoration-Cryone
consistently outperforms state-of-the-arr baselines across multiple density map quality metries, confirming its potential versatile foun- dation model for a wide spectrum of cryo-EM applications.
INTRODUCTION
All biological functions governed by events orchestrated at the molecular level by macromolecu- lar assemblies (Liao et al., 2013 Gestaut et al. 2022). Elucidating their intricate structures not only deepens our understanding of underlying molecular mechanisms, but also lays the groundwork for advancements in fields like drug discovery (Congreve et al. 2020). Cryo-electron microscopy has emerged pivotal technique for resolving macromolecular structures (Kühlbrandt] 2014 Nogales & Mahamid 2024), with deposited density maps in the Electron Microscopy Data Bank (EMDB) (wwPDB Consortium] 2023) growing exponentially (Lawson al. 2016). However, cryo-EM faces several intrinsic pitfalls that complicate accurate structure determmation. Single particle analysis (SPA) cryo-EM suffers from poor signal-to-noise ratio, attenuated high-frequency information, and anisotropic resolution arising from low-dose imaging requirements, sample heterogeneity. and pre- ferred specimen orientation(Rosentle raphy (cryo-ET) encounters additional limitations from limited per-image electron dose, missing wedge artifacts, and substantial noise that obscure fine structural details (Liu et al. [002aa] Tao et al,, 2018). These challenges hinder direct structural interpretation from raw cryo-EM maps, particularly at resolutions lower than 4 A, necessitating extensive processing pipelines with substantial manual intervention and domain expertise (Terwilliger al. 2018a Terashi & Kihara 2018).
& Henderson 2003 Liu et al. 2025). Cryo-electron tomog-

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Building these observations, the cryo-EM community has increasingly embraced machine learn- ing approaches across the entire structural determination pipeline. For map reconstruction, cryo- DRGN (Zhong et al. 202T) employs variational autoencoders learn continuous latent represen- tations conformational heterogeneity, while 3DFlex (Punjani Fleet 2023) models non-rigid motions through coordinate-based neural networks. For postprocessing. DeepEMhancer (Sanchez- Garcia et al., 2021) adopts 3D U-Net architecture trained on pairs of experimental maps and LocScale-sharpened largets for automated masking and local sharpening, while EMReady (He al. 2023) implements Swin-Conv-UNe framework combining residual convolution for local model- ing with Swin transformer for non-local feature extraction. For atomic model building, deep learning approaches have progressed from early methods such as A2-Net (Xu et al. 2019) and DeepTracer (Pfab et al. 2021], J. which perform novo amino acid recognition and backbone tracing from density maps, to more recent advances like ModelAngelo (Jamali et al.) 2024), which employs graph neural networks with iterative refinement and leverages protein language model embeddings to jointly per- form atomic coordinate prediction and sequence assignment, achieving automated model building at expert-level quality. Despite these advances, current deep learning methods in cryo-EM remain predominantly task-specific and rely supervised training paradigms requiring labeled input data, constraining dataset scales and yielding models with limited generalizability.
Protrain
Finctune
Context Encoder
Target Predicdor
Task-speolfi Model
Input density map Random sampling
Context voxels
EMA
Downstream applications
Map sharpening
Prediction
Super-resolution resolution Missing wedge restoration
Target Encoder
Target vowels
Target voxel embeddings
Predicted voxel embeddings
Figure 1: CryoLVM framework. During pretraining, input density maps partitioned into overlapping 3D patches, which are then randomly divided into context and target subsets. The Con- text Encoder processes visible context patches, and the Target Predictor takes the resulting context emheddings along with positional information of masked target patches to predict the corresponding Target Encoder outputs. A regression loss applied between predicted and target voxel embeddings to encourage representational alignment. The weights of the Target Encoder are updated via an exponential moving average (EMA) of the Context Encoder weights. Following pretraining, the pretrained encoder is adapted to three downstream cryo-EM tasks through fine-tuning with task- specific models: density map sharpening, super-resolution and missing wedge restoration.
Foundation models harness self-supervised pretraining large-scale data to develop transferable representations, facilitating efficient adaptation to diverse downstream applications (Wei et al. 2022
Achiam al. 2023 Kaplan al. 2020). Notably, protein language models trained extensive
quence corpora have become transformative tools for structure prediction (Lin et al. 2023), protein design (Verkuil et al., 2022), and function annotation (Hayes et al. (025). While foundation models have recently emerged for 2D cryo-EM image processing tasks (Shen et al. 2024 Zhang et al.) 2025 Yan al. 2024),, they remain largely unexplored in the cryo-EM density map domain. /hhou et al. (024) introduced CryoFM, flow- foundation model for EM density maps that demon- strates versatility as a generative prior across multiple tasks. Nevertheless, CryoFM was trained and evaluated exclusively on curated high-quality density maps, with assessments conducted primar- ily on synthetic noise-corrupted maps. Consequently. its robustness and performance on genuinely noisy, low-resolution experimental maps from real cryo-EM workflows remain to be established.
We present CryoLVM, a foundation model that introduces Joint-Emin Soint-Emedddigg Predictive Architecture (JEPA) Assran et al. 2023) to the cryo-EM density map domain, learning self-supervised struc-
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tural representations that facilitate efficient adaptation across multiple downstream tasks. Unlike reconstruction based pretraining methods such as masked autoencoders that predict raw voxel val- ues, JEPA operates in abstract representation space, naturally filtering the pervasive noise in cryo- EM densities while capturing high-level structural semantics. We employ a SCUNet-based encoder (Zhang et al., (023) within the JEPA framework the backbone architecture, whose hybrid design combining Swin Transformer and residual convolution pathways is well-suited for cryo-EM data, where both local atomic-level features and global cross-regional spatial relationships must be jointly modeled. Through pretraining on experimental Myy-EMM density maps with resolved structures col- lected from the EMDB, followed by fine-tuning with tpsc-ic decoders, CryoLVM attains state- of-the-art results across three critical downstream tasks density map sharpening, super-resolution, and missing wedge restoration - demonstrating robust performance on genuinely noisy experimen- tal maps and its promise as versatile foundation model for broader cryo-EM applications.
The main contributions of this work are summarized as follows:
We propose CryoLVM, the first foundation model to employ JEPA with a SCUNet-based back- bone for self-supervised learning on 3D cryo-EM density maps, effectively combining local atomic-level feature extraction with global spatial context modeling to learn semantically rich structural representations;
We develop novel histogram- bbased distribution alignment loss LHISKL that, when combined with standard reconstruction loss, enhances convergence speed and improves fine-tuning performance;
We conduct comprehensive experiments across three downstream tasks, demonstrating that Cry- oLV consistently outperforms existing methods including DeepEMhancer, EMReady, EM-GAN Subramaniya et al. 2021), and IsoNet (Liu et al. 2022a) on most evaluation metrics.
RELATED WORK
Density map sharpening Cryo-EM density maps suffer from resolution-dependent ampli- tude falloff that attenuates high-frequency contrast, necessitating post-processing methods to restore interpretability. Traditional map sharpening approaches include global methods like phenix.auto sharpen (Terwiligger al. 2018b) and RELION (Scheres 2015) that apply uniform B B-factor correction, and local methods such as LocScale (Jakobi et al. 2017) that perform spatially adaptive enhancement by comparing the local amplitude spectrum the experimental map with that of a reference atomic model. Recent deep learning approaches have advanced this field sig- nificantly: DeepEMManner employs a 3D U-Net architecture to mimie LocScale's local sharpening effects (Sanchez-Garre et al., (021], while EMReady utilizes a 3D Swin-Conv-UND framework with combined smooth LI and structural similarity losses to optimize both local and non-local fea- tures of experimental cryo-EM density maps (He al. 2023).
Density map super-resolution Model building from low-resolution cryo-EM maps remains a ma- jor impediment, current state-of-the-art methods like ModelAngelo exhibit marked performance degradation bevond 4 resolution (Jamali et al., 2024 For protein identification methods like CryoDomain (Dai et al. 2025), their accuracy also deteriorates resolution exceeds 6-8 Á. These limitations motivate the development of super-resolution techniques, which aim to estimate high- resolution maps from their low-resolution counterparts and thereby facilitate downstream structural determination. EM-GAN represents an early deep learning approach in this domain, employing 3D generative adversarial networks to enhance experimental cryo-EM maps in the 3-6 Á resolution range (Subramaniya et al. 2021).
Missing wedge restoration in cryo-ET The missing wedge problem in cryo-ET stems from phys- ical limitations during tilt-series acquisition, where specimen geometry and mechanical constraints restrict image collection to an angular range of typically +060 (Lučié et al.] (2005). Incomplete angular sampling creates characteristic wedge-shaped gaps in Fourier space, leading to anisotropic resolution with severe artifacts along the beam direction, manifesting as structural elongation and distortion that compromise reconstruction fidelity (Wiedemann & Heckel 2024). While classical approaches have implemented iterative reconstruction algorithms including SIRT (Gilbert 1972) and ART (Gordon et al.) 1970, Yan et al. 2019). alongside constrained optimization techniques like ICON (Deng et al." 2016) that impose binary assumptions such as density positivity, these methods offer limited recovery of missing information. Deep learning has revolutionized miss-
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ing wedge restoration through learning complex priors directly from tomographic data. IsoNet, built upon a 3D U-Net architecture, iteratively recovers missing information by training on paired datasets datasets-gennenn generated by rotating subtomograms 20 orientations and imposing additional missing wedge artifacts-to map degraded inputs to less-degraded targets (Liu et al. 2022a).
3
METHODOLOGY
3.1 MODEL ARCHITECTURE
CryoLVM combines Joint-Embedding Predictive Architecture (JEPA) with SCUNet backbone for efficient self-supervised learning on cryo-EM density maps. Our design addresses the unique challenges of volumetric biological data through strategic architectural modifications and domain- specific optimizations.
Context Encoder
Target Predictor
Mask
EMA
Mask
Target Encoder
Pretrained Encoder
Decoder
Output density map
Input density map
Dowmstream
Positional
VIT-3D
Conv Transpose3D
Conv3D
ConvTransBlock
Bottleneck
Linear
Encoding
Block
Figure 2: CryoLVM architecture. The Context Encoder and Target Encoder use hierarchical swin- conv (SC) blocks for multi-scale feature extraction, with outputs converted to patch embeddings and combined with 3D sinusoidal positional encodings. The Target Predictor employs transformer blocks to predict target representations. For downstream tasks, task-specific decoder with upsam- pling SC blocks are jointly fine-tuned with the pretrained encoder.
We adopt JEPA over traditional masked autoencoders for its representation-spaae prediction paradigm. Unlike voxel-level reconstruction that amplifies noise in low-SNR cryo-EM densities, JEPA learns semantic features by predicting masked region representation from visible context. This approach naturally filters noise while preserving structural information critical for downstream tasks. Our pretraining architecture comprises three components: Context Encoder fo., Target En- coder Sg, (stop-gradient), and Target Predictor The training objective minimizes prediction loss:
Cp En., ( StnooessLLoohh (Fcontext). ), 2,) ff, (x:))],
(1)
where M denotes masked patches, z; denotes spatial position information, and SmoothL1, provides robust optimization with reduced sensitivity to outliers compared to L2 loss.
Different from previous studies, we select SCUNet over conventional 3D Vision Transformer the backbone for its hybrid architecture that adeptly combines local and global modeling. SCUNet's swin-conv (SC) blocks partition features into dual paths: Swin Transformers capture oaagerange
dependencies while residual convolutions preserve local details. This architectural choice particu- larly well-suited for cryo-EM applications, where local convolutions extract atomic-level structural

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features and global attention mechanisms model cross cross-regional spatial relationships across multiple scales. Our Context Encoder and Target Encoder consist of three downsampling SC blocks, three 3D convolution blocks and a bottleneck SC block. Within each SC block, imput features are bi- furcated into parallel pathways: the convolution branch employs residual 3 x 3 x 3 convolutions with Filter Response Normalization, while the transformer branch utilizes 3D windowed multi-head
self-attention with window size 4 x 4 x 4. Feature fusion is achieved through 1 x 1 X 1 convo- lutions, enabling simultaneous local feature preservation and global context aggregation. During pretraining, encoder outputs are converted to patch embeddings via linear transformation and incor- porated with 3D sinusoidal positional encodings to preserve spatial relationships within the feature grid. Masks are then applied to these patch embeddings to generate context and target embeddings. The Target Predictor follows the JEPA framework, comprising standard transformer blocks. Final predictions are mapped back to encoder embedding dimension through a linear projection, ensuring compatibility with target outputs for loss computation. For downstream applications, task-specific decoders utilize upsampling SC blocks and 3D transposed convolution blocks, jointly fine-tuned with the pretrained encoder on labeled datasets for each target task.
3.2 Loss FOR DOWNSTREAM TASKS
Across all three downstream tasks, we employ a unified composite loss function that combines standard reconstruction error LMSE with distributional alignment loss LIisKLL
To enforce statistical consistency between predicted and target densities, we design a novel histogram-basse distribution alignment loss, denoted as LHSULL- The idea is to align the predicted density distribution with the ground-truth density distribution via differentiable histogram and divergence measure. Given predicted density X and target density X", we first construct their soft histograms using Gaussian kernel weighting:
N Σ cxp 2 0 )
83 (
h(x); = N
(2)
where oj denotes the center of the j-th bin, a controls the smoothness, and N is the number of total voxels.
Then, we quantify the distributional divergence between the two histograms p = h(X) and =
h(X+) using JeeeeeennnSnnnooee (IS) divergence, which is based on Kullback- (KL) divergence:
Dis(p ()) Σ k mi
Pk log
Σ k
Tb log
17g
with m = (p + 4)
The proposed histogram KL loss is thus defined as:
SX, = Dís (H( H(X*))
(4)
The final objective combines both loss components:
Lioal LMsE + (1 a:) LHisKL.
(5)
where hyperparameter a E [0, 1] balances reconstruction accuracy and distributional alignment.
4
EXPERIMENTS
4.1
PRETRAINING DATASET
To enable effective learning structural semantics embedded within cryo-EM density maps, we assembled our pretraining dataset by leveraging the training subset of Cryo2StructData (Giri et al.) 2024), currently the most comprehensive publicly accessible repository in this domain. This curated collection comprises 7,392 high-resolution experimental density maps representing proteins and macromolecular complexes with resolutions spanning 1-4 Å. All density maps underwent standard- ized preprocessing procedures: 1) voxel sizes were uniformly resampled to I Á spacing, 2) density
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values were clipped to the 0.01-0.99 percentile range to mitigate outlier effects and subsequently normalized to [0, 1]. To ensure rigorous evaluation and prevent data leakage, we systematically excluded density maps present in the test sets of baseline methods used for downstream task com- parisons, yielding a final pretraining corpus 7,302 density maps. For Cryol.VM input generation, applied random spatial cropping extract volumes of size 48
Table 1: Evalutation metrics of different methods the density map sharpening test set.
Chimera.MaPQ
bo t
CCmask f
CCpeaks
Q-score f
Deposited
0.744
0.788
0.659
0.338
DeepEMhancer
0.695
0.679
0.659
0.323
EMReady
0.878
0.802
0.791
0,424
CryoLVM
0.894
0.821
0.806
0.444
0.9
" 13% 200
0.7
0.6
0.6
0.5
Deposited DeepEMhancer EMReady
Cryoly/
Deposited DeepEMhancer
EMReady
CryotVM
0.8
Phiiit de,
0.6
6 20R 1
d4 b FS 4%%
0.5
Q-score
0.4
0.5
0.3
0.2
Deposited DeepEMhancer EMReady
Deposited DeepEMhancer
EMReady
CryolVM
EMD-10092
Deposited
DespEMhancer
EMReady
CryoLVM
POB: 653K
Resolution: 3.7
Figure 3: Comparative evaluation of density map sharpening performance across baseline and proposed methods. Cross-correlation metries (CCb.x. CCmask. popaa) were calculated via phenix.map-mooee model_cc (Afonine et al.) 22188). which quantify the agreement between density maps and their associated atomic models over different spatial regions. Q-score was computed using Chimera.MapQ (Pintilie et al.] 2020 2025), providing an independent assessment of map quality based on local atom-to-density correlation and atomic resolvability.
4.2 DENSITY MAP SHARPENING
Cryo-EM density maps suffer from resolution-dependen amplitude falloff that attenuates high- frequency contrast, making direct structural interpretation challenging. Density map sharpening

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aims to computationally recover high-frequency information while preserving structural integrity, effectively reversing the B-factor decay that obscures fine molecular details.
Following the established training paradigm of EMReady, adopted supervised learning ap- proach for density map sharpening using paired experimental and target maps. Target density maps were simulated from their corresponding structures using Chimera. molmap (Pettersen al. 2004), with the resolution parameter matched to that of the paired experimental maps.
We evaluated CryoLVM density map sharpening using comprehensive set of quality metrics computed through established cryo-EM analysis tools. As shown in Tab. [ CryoLVM demonstrates superior performance across all metrics, achieving the highest scores in CC (0.894), CCmask (0.821), peaks (0.806), and Q-score (0.444). These improvements indicate enhanced overall quality between sharpened maps, suggesting better preservation of structural features and more ac- curate high frequency signal recovery. The violin plots in Fig. +] also illustrate that CryoLVM consistently achieves tighter distributions with higher median values, indicating robust performance across diverse map types and resolution ranges. Visualization for EMD-10092 in Fig. 3 show- cases CryoLVM's ability to enhance fine structural details such as improved definition of secondary structure elements while maintaining overall molecular topology.
Table 2: Evalutation metrics of different methods on the density map super-resolution test set
CryoRes (A) & FSC-0.143(A) 4 FSC-0.5(A) 4 Resolution(A)
phenix.mtriage
Deposited
3.66
3.39
4.64
3.81
DeepEMhancer
3.27
2.72
4.86
3.47
EMGAN
2.49
2.70
5.46
4.18
CryoLVM
2.33
2.58
4.58
3.39
5.5
4.5
FSC-0.143XA)
4,0
Sp
3.0
2.5
Jog sj
+
1.5
Deposited DeepEMhancer
EMGAN
CryotVM
Deposited
DeepEMhancer
EMGAN
CryoLVM
EMD-20668
Deposited
DeepEMhencer
CryoLVM
POB: 6U7H
Resolution: 3.1 A
Figure 4: Comparison of map super-resolution performance between different methods. FSC-based metrics computed using phenix.mtriage (Afonine et al.) 2018). Local resolution estimates obtained via CryoRes (Dai et al. 2023);; global resolution represents the average of voxel-wise predictions.
4.3 DENSITY MAP SUPER-RESOLUTION
Cryo-electron microscopy (cryo-EM) frequently produces density maps at intermediate resolutions that preclude direct atomic model building. Even modest resolution improvements can greatly ben-

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efit downstream structural analysis. Fine-grained density features indispensible for accurately determining backbone geometry and side-chain conformations.
We developed supervised training protocol for CryoLVM based paired density maps. Training data consists of experimental maps at 4-6 A paired with target maps simulated from their corre- sponding PDB structures. Target maps were generated using Chimera.molmap with the resolution parameter set to 1.8 A. This paired supervision constrains the model to learn a robust mapping from medium-resolution experimental densities near-atomic structural detail, enabling effective super-resolution of cryo-EM maps.
We benchmarked VM for density map super-resolution against DeepEEMaancer and EMGAN. Under the phenix.mtriage (Afonine et al. (2018) metrics (Fig. 4. CryoLVM achieves the best FSC- based resolutions 2..3d,=..333, FSC-0.143 2.58, FSC-0.5 4.58), significantly outperforming both DeepEMhancer and EMGAN. Similarly, when assessed with CryoRes (Dai et al. 2023). Cry- oLVM attains 3.39 versus 3.47 and 18Á for the baselines. Visualizations for EMD-20668 (PDB: 6U7H, 3.1 A) are shown in Fig. 4 In the two magnified insets, CryoLVM improves back- bone connectivity, and aligns much more closely with ground truth. CryoLVM not only enhances better visual interpretability but also improves quantitative resolution metrics, facilitating more ac- curate downstream structure determination from intermediate-rrssolttin cryo-EM maps.
4.4 MISSING WEDGE RESTORATION
Cryo-electron tomography (cryo-ET) faces critical technical constraints: radiation sensitivity lim- its total electron dose, resulting in low signal-to-noiss ratios, while specimen geometry restricts tilt angles to approximately =60 rather than the ideal +990 As shown in Fig. 5 this limited angu- lar sampling creates characteristic wedge-shaped gaps in Fourier space. Missing wedge produces anisotropic resolution with degraded Z-axis information and structural distortions. We simulated missing wedge artifacts by applying Fourier transform to the map and then masking the frequency domain with a wedge-shaped filter.
Fourier Space
Degros
CryoLVM
IsoNet
Figure 5: Visualization and schematic analysis of the missing wedge problem and its restoration. Left: schematic illustration the missing wedge problem in cryo-electron tomography. Right: case result for missing wedge task. The original density map is EMD-5331 from EMDB and is added wedge-shaped mask for model processing.
dourier-domain wedge masking. Given 3D volume V. let its Fourier transform be ((k) at frequency k E R3. To simulate the missing wedge, we introduce an indicator mask M (k) that preserves only frequencies within the permitted tilt cone:
{
1,
|arctan(ks// V 12 k.2)1 S 0 0max:
((k
0, otherwise.
The corrupted spectrum is then
((k) M(k) (kk)
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and the simulated tomogram with missing wedge effect is obtained by inverse Fourier transform,
Viw (x) = =F (V)))
Here, max denotes the experimental tilt limit (commonly around 60%).
We evaluated restoration performance via phenix.mtriag (Afonine al. 2018 and report the map-model FSC-based resolution metrics dmai (A) at two cutoff thresholds, FSC 0.143 and FSC 0.5. Lower dmin values indicate higher resolution and thus better restoration quality. Our results demonstrate that CryoLVM consistently outperforms IsoNet baseline across all evaluation metrics(Tab.. 3): at FSC = 0.143, CryoLVM reduces dmin from 10.448 Á to 10.094 Á (~ 3.39%); at FSC 0.5, CryoLVM improves from 12.361 Á to 11.447 A (~ 7.39%). These quantitative improvements in dmnin reflect Crys greater ability to recover high-frequency structural infor- mation that is typically lost due to missing wedge artifacts.
Fig. depicts a missing-wedge case, where CryoLVM is able to reconstruct fine-scale, high- frequency features that IsoNet fails maintain. The magnified insets highlight critical difference: while CryoLVM correctly reconstructs continuous pore-like channels, IsoNet produces fragmented or occluded segments that result in topologically incorrect representations of these hollow structures. The FSC curves comparing reconstructed maps to reference models show that CryoLVM maintains a higher correlation across mid-to-high spatial frequencies and reaches established cutoff thresholds at higher spatial frequencies than IsoNet (Fig. G indicating better resolution restoration.
Table 3: Performance of different methods in missing wedge restoration task. The score is computed between two half maps (predicted map and ground truth map). Additional results are in Appendix G.6
phenix.mtriage
018
Method
FSC-0.143 & FSC-0.5
IsoNet 10.448 10.094
12.361
CryoLVM
11.447
Figure 6: FSC score versus spatial frequency for predicted maps of IsoNet and CryoLVM (EMD- 5331).
4.5 ABLATION & DISCUSSION
To validate our design choices and understand the contribution of individual components, we con- ducted ablation studies examining key architectural decisions, pretraining strategies, loss configura- tions, and postprocessing methods. In this section, we briefly present the key findings.
SCUNet-based models achieve better performance than their VIT counterparts across all eval- uated downstream tasks. To demonstrate that adopting SCUNet as the backbone over ViT yields consistently better performance across all downstream tasks, we compared models with different backbones trained using identical hyperparameters: detailed results are presented in Appendix G.I
Composite loss of MSE and HistKL leads to accelerated convergence and enhanced down- stream performance. To validate the contribution of the proposed histogram KI. loss, we compared fine-tuning Vr on the density map super-resolution task with the composite loss against with MSE alone; extended results are provided in Appendix G.2
JEPA pretraining yields transferable representations that improve downstream performance. To assess the effectiveness of our pretraining approach, we compared the model against a model trained from scratch, and further compared JEPA against masked autoen- coder (MAE) pretraining the map sharpening task. The pretrained model demonstrates consis- tent improvements across all metrics, and JEPA consistently outperforms MAE; extended results are presented in Appendix G.3

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5 CONCLUSION
In this paper, we present CryoLVM, the first foundation model for cryo-EM density maps that em- ploys Joint-Embedding Predictive Architecture with SCUNet-based backbone learn rich structural representations. We introduce a novel histogram-based distribution alignment loss that accelerates convergence and enhances fine-tuning performance. CryoLVM consistently outperforms established baselines across three critical downstream tasks, showing robust performance on genuinely noisy, low-resolution experimental maps encountered in real-world cryo-EM workflows. We envision that this foundation model paradigm will facilitate broader adoption of unified Al-driven approaches in cryo-EM and contribute to accelerating structural biology discoveries.
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URL https:
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A STATEMENT OF THE USE OF LARGE LANGUAGE MODELS
Large language models were used solely to polish the academic writing of this paper. They were not involved in research ideation, experimental design, data analysis, or any other substantive aspects of the work. The authors take full responsibility for the content of the manucript.
B ADDITIONAL RELATED WORK
B. FOUNDATION MODELS FOR CRYO-EM
The emergence of foundation models has begun transforming the cryo-EM data processing land- scape, with recent works addressing distinct stages of the cryo-EM structural determination pipeline. DRACO (Shen et al. 2024) introduced a sennosing-reconstrueee 270,000 cryo-EM movies micrographs, demonstrating strong generalization capabilities across micrograph- tasks including denoising, micrograph curation, and particle picking. CryoFastAR (hang et al. (025) pioneered geometric foundation modeling for cryo-EM by directly predicting particle poses for unordered, noisy 2D projection images, facilitating acceleration in ab initio recon- struction compared to traditional iterative optimization approaches. Cryo-IEF Yan al. 2024) pre-
autoencoder pretrained over
sented comprehensive foundation model pretrained on approximately 65 million particle images and showed excellent performance in tasks such as classifying particles from different structures, clustering particles by pose, and assessing image quality. These works operate on 2D) cryo-FM images from the data acquisition and reconstruction stages and complement CryoLVM address complete cryo-EM workflow from particle image processing through density map post-processing and analysis.
B.2 JEPA APPLICATIONS IN BIOLOGICAL CONTEXT
Joint-Embedding Predictive Architecture (JEPA), first introduced by Assran et al. 2023). with JEPA, has gained significant traction in computational biology due to its ability to learn semantic representations through prediction struction. Brain-JEPA (Dong et al. 2024) provides the most direct precedent for CryoLVM by cessfully applying JEPA to 3D spatiotemporal biological data using functional Magnetic Resonance Imaging (fMRI). This pioneering brain dynamics foundation model incorporates innovative
abstract embedding space rather than raw pixel-level recon-
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techniques tailored to volumetric biological data: Brain Gradient Positioning (BGP), which estab- lishes functional coordinate system for brain functional parcellation and enhances the positional encoding of different Regions of Interest (ROI), and Spatiotemporal Masking, which is designed for the unique characteristics of fMRI data to tackle with heterogeneous time-series patches. Brain- JEPA achieves state-of-the-an art performance in dempgraphic prediction, disease diagnosis, and trait prediction, demonstrating JEPA's effectiveness for complex 3D biological volumes with temporal dependencies.
B.3 SCUNET APPLICATIONS IMAGE DENOISING
The SCUNet framework represents breakthrough in hybrid architecture for image denoising, ef- fectively combining global modeling capabilities Swin Transformers (Liu et al. 2021 with local feature extraction advantages of convolutional neural networks. [Zhan et al. (2023) introduced SCUNet in their seminal work, proposing Swin-Conv (blocks) as the core building components of UNet backbone architecture, where each SC block processes input through tion followed by evenly splitting feature maps into two groups that are respectively fed into Swin Transformer blocks and residual convolutional layers. Subsequent developments further validated SCUNet's effectiveness across diverse denoising applications: SUNet (Fan et al. 2022) applies Swin Transformer layers as basic building blocks within UNet architecture for image denoising, achieving significant performance improvements
convolu-
fixed-size input denoising tasks, while SCNet (Lin al. 2025) proposes a dual-branch fusion network that combines Swin Transformer branches with Con- vNext (Liu al., 2022b) branches, employing Feature Fusion Blocks with joint spatial and channel attention for adaptive output merging, demonstrating robustness under severe noise conditions and proving effective in real-world applications like mural image denoising.
C ADDITIONAL DETAILS OF CRYOLVM
C. IMPLEMENTATION DETAILS
Fig. illustrates the detailed architecture of CryoL Context Encoder and Target Encoder. Both encoders share an identical hierarchical design, with Target Encoder parameters updated via expo- nential moving average (EMA) of Context Encoder during pretraining. The encoder architecture begins with an initial 3 x x convolution applied to the input density, followed by three consecu- tive down- samplig stages. Each stage consists of Swin-Conv Block and 2 x 2 convolution. Within each Swin-Conv Block, feature map is first passed through 1 1 1 convolution, and then split evenly into feature map groups, each of which is then fed into swin transformer (Swin')) block and a residual x 3 x 3 convolution (RConv) block separately; after that, feature maps are concatenated and passed through final x x1x convolution integrate local and global modeling.
Target
Encoder
SwinT Block
xx0ra Conv
2xbx2 Conv
Spint
RCenv Block
Input density
Donsity embedding
Figure 7: Detailed architecture of CryoLVM's Context Encoder and Target Encoder.
Fig. 8 illustrates the complete architecture of CryoLVM including pretrained encoder and task- specific decoder for downstream applications. The modular design enables effective transfer learn- ing, where the encoder captures generalizable density map representations in the pretraining stage, while the decoder specializes these features for particular cryo-EM tasks. The mask-specific decoder employs symmetric up-sampling strategy to progressively reconstruct high-quality density maps from the encoded density embeddings. starts with three consecutive up-sampling stages, each comprising 2 x 2 x 2 transposed convolution followed by Swin-Conv Block. The transposed
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convolution performs spatial up sampling while reducing feature dimensionalityy and the Swin-Com Block refines the upsampled features. critical component is the skip connections between corre- sponding encoder and decoder layers. Specifically, skip connections link the convolution layers of encoder with the transposed convolution layers decoder. These connections preserve multi-scale structural information that might otherwise be lost. After that, feature map is passed through final 3 x 3 X 3 convolution to produce output density.
Contect Target Encoder
Task-spaciiic Decoder
Sein Cony Block
Con- Block
3dd Come
Com
2xbe CenvT
3-2-2 Come
Salt Batr-Coal
Input denstly
Output densily
Figure 8: Detailed implementation pretrained encoder and task-specific decoder for downstream application.
D DATA CURATION AND PROCESSING
D.1 PREPROCESSING FOR TRAINING ACCELERATION AND MEMORY REDUCTION
Pretraining To construct high-quality pretraining dataset of cryo-EM density maps with high- quality structural information for CryoLVM, focused relatively high-resolution experimen- tal density maps with corresponding resolved PDBmBnrman et al.] 2000) structures deposited in EMDB (wwPDB Consortium 2023). We utilized Cryo2StructDatta currently the most compre- hensive publicly available depository this domain, containing 7,600 cryo-EM density maps with associated structural annotations. We assembled our pretraining dataset from the training subset of Cryo2StructData, which comprises 7,392 density maps representing proteins and macromolecular complexes with resolutions spanning 1-4 Å. We first excluded density maps present in the sets of baseline methods used for downstream task comparisons to guarantee meticulous evaluation and prevent data leakage, resulting final pretraining corpus of 7,302 density maps. Next, we pre- processed the pretraining density maps as follows: 1) voxel sizes were uniformly resampled to Á spacing, 2) density values were clipped to the 0.01-0.99 percentile range to mitigate outlier effects and subsequently normalized to [0, 3) density maps converted to Pytorch tensor format with bFloat16 precision, providing computational speedup and memory reduction.
Density map sharpening We first constructed our test set by selecting 50 cryo-EM density maps from the established EMReady's benchmark, ensuring direct comparison with prior methods. To build our training and validation sets, we collected additional single-particle crvo-EM maps at 3-6
resolutions and their corresponding atomic structures from EMDB (wwPDB Consortium 2023)
and PDB (Berman et al. 2000). We applied stringent quality control criteria to filter map-model pairs: I) do not contain backbone atoms only, 2) do not include unknown residues, 3) do not include missing chain, have orthogonal map axis, resolution is given by the FSC-0. 143 threshold, 6)
any chain in the model does not share >30% sequence identity with any chain in the test set models. This yielded total of 400 pairs of map and model as the training set, and 70 pairs of map and model as the validation set. Target density maps were simulated from their corresponding PDB structures using Chimera.molmap (Pettersen et al., 2004) with the resolution parameter matched to that of the paired experimental maps. We preprocessed the paired maps as follows: 1) voxel sizes were uniformly resampled to spacing, 2) density values were clipped to the 0.01-0.99 percentile range to mitigate outlier effects and subsequently normalized to [0. 3) density maps converted to Pytorch tensor format with bFloat16 precision, providing computational speedup and memory reduction. During training, employed sliding window sampling strategy with stride 24 to generate 3 input volumes and augmented training data through random flips and random rotations.
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Density map super-resolution We constructed test consisting of 40 single-particle cryo- EM density maps, a training set with 400 maps and a validation set with 50 maps. All density maps exhibit resolutions ranging from 2.3-6 A and satisfy the quality control criteria described above for our density map sharpening experiment. Target density maps were simulated from their corresponding PDB structures using Chimera.molman with the resolution parameter uniformly set to 1.8 A. We preprocessed the paired maps following the same procedure from the density map sharpening experiment.
Missing wedge restoration In the procedure for restoring missing wedge, input density maps come from Cryo-ET, which typically exhibit resolutions worse than 8 Á, even after subtomogram averaging. Thus, we chose maps with resolutions lower than 8 Å and applied angular masks to mimic the missing wedge effect. Given constraints from the Nyquist sampling theorem and computational efficiency, voxel size of 4 Á was used instead of 1 Á, which was used for the density map super- resolution and sharpening tasks. total, we obtained 400 training samples, 50 validation samples, and 40 test samples. The resolution distributions of the training and test sets are shown in Fig. 9
0.125
0.14
0.130
0.125
0.40
Pababiiity
0.000
0.02
0.075
0.00
0.04
0.000
0.02
0.025
0.00
0.000
Resolution (A)
Resolutton (A)
Figure 9: Resolution Distribution of Missing Wedge Restoration Task. (Left) Training Set. (Right)
Test Set.
Algorithm 1: Gaussian weighted Grussian-weighted fusion of patch predictions into full density map
Input: Overlapping patch predictions (yx 18 their voxel coordinates {Px J box size B, stride small > 0
Output: Reconstructed volume M
Require: 3D Gaussian weight kernel W ker E Bx with entries
Wi.f cxp( ( normalized to [1, (center larger, border smaller);
zero-initialized accumulators V, sized to the (padded) volume. (Implementation mirrors the sliding-wing window accumulation with per-voxel weights and final normalization.)
for k 1, 2, ... do Extract the region-to-update Rę C V starting at Pa with size B x Bx Weighted accumulation
prediction and weights
ViR+] + V[R + ÿı O Wker
S(Ra] < R + Wker
end
// Element- wise normalization to resolve overlaps
M V max
Crop M to the original (unpadded) spatial extent to obtain M return M
D.2 POSTPROCESSING AFTER PREDICTION
We performed post-processing on the predicted density maps ensure consistency, reduce artifacts, and improve interpretability of reconstructed volumes. Given patch-banc based predictions, overlapping regions were fused utilizing Gaussian Gaussian-weighted scheme (See in Algo. [.. which balances contri- butions between central and boundary voxels to promote continuity. This blending step reduces dis- continuities patch boundaries while maintaining local structural details. After weighted fusion,
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applied normalization to maintain consistent voxel intensity distributions across the reconstructed volume. This step corrects for potential intensity variations arising from the overlapping patch-based prediction strategy, where edge regions of patches may exhibit different contrast characteristics than central regions due to reduced spatial context during inference. The final step involved cropping out padded regions introduced during the patch-wise sliding-window prediction step. Together, these steps generate a clean, continuous and interpretable density map suitable for model building, reso- lution evaluation, and quantitative validation. The Tab. 12 shows the post processing ablation study for missing wedge restoration task.
E EXPERIMENTAL SETTINGS
For downstream training, used the AdamW optimizer with initial learning rate of 3 x 10-5 weight decay of x 10-3, and a RRonllttan (factor 0.9, patience 5). All experiments for 500 epochs using batch size of 32, mixed precision (hfloat16), and DDP (Distributed Data Parallel). Input density maps were cropped into 48 -oxel volumes, with a stride 24 during training and 48 during validation. To boost model robustness, we applied random flips along all three axes and random rotations as data augmentation. The backbone architecture was based on SCUNet, which takes a single input channel, uses base dimension of 32 with head dimension 16, and consists of seven stages configured as [2,2,2,,,22..22]. Swin-Conv Blocks with local attention window of 3 and zero drop-path rate were used; the output was one-channel density map. This design strikes balance between local convolutional features and global attention, enabling efficient training on large multi-GPU clusters while preserving structural detail. The specific hyperparameters for downstream task training are listed in Tab. and further architectural details are availble in Tab.
Table 4: Hyperparameters for downstream task training
Parameter
Value
Description
optimizer
Adam
Optimizer type
learning rate
3 10-5
Initial LR for Adam W
beight_decap
1 10-3
Weight decay coefficient
epochs
500
Number of training epochs
batch_size
35
Batch size per process Reconstruction loss (f2)
loss
a LMSE + (1 a:) LHiSKI, E [0,1] RedlaeLlROnPlateau
Ir_scheduler
Factor 0.9, patience 5 (min mode) autocast enabled
mixed precision
b
distributed
DDP (NCCL)
Multi-GPU data parallel training
input size
483
Input/output volume size Sliding-window stride (training)
train stride
24
val stride
48
Sliding-window stride (validation)
augmentation
RandomFilp/RanddRRRutt
3D flips and 90 rotations
num_ workers
10
DataLoader workers per process Page-locked host memory
pin memory
True
F EVALUATION METRICS
Fourier Shell Correlation (FSC). The Fourier Shell Correlation (FSC) is a standard metric for measuring the resolution of reconstructed density maps in cryo-EM. It is computed between two independently reconstructed half-maps (ik)) and F2(k), as the normalized cross-correlation of their Fourier coefficients within frequency shell S:
Σ Fi(k (5(k) KES
FSC(S)
Σ KES
F (k)
Σ [F2))
KES
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Table 5: Model architecture of SCUNet
Component
Setting
Description
backbone
SCUNet
Swin-Conv U-Net for volumetric data
input channels
in.nc
stages / blocks
[2, 2, 2, 2, 2,
Blocks per stage (oonfig)
base dim
32
Channel width first stage (dim)
head dim
16
Attention head dimension
window_sine
Local attention window (3x3x3)
input_size
48
Input size for model
drop-pati pat-hatthrtteee rate
Stochastic depth disabled
activation output
(inherited by SCUNet) channel
Follows SCUNet default
Reconstructed high frequency density
where (kk denotes the Fourier transform of the i-th half-map, indicates complex conjugation, and the summation is performed over all Fourier coefficients within the shell S. The FSC curve typically decays with increasing frequency, and the resolution is conventionally defined the spatial frequency where FSC drops below a certain threshold (e.g., 0.143 or 0.5).
model
The dmustel metric serves as an effective resolution indicator that quantifies the level of structural detail present in cryo-EM maps. It measures the effective resolution at which the atomic model agrees with the experimental density, providing a practical evaluation of density map's s inter- pretability.
Map-Model Correlation Coefficients (Phenix). The Phenix validation suite provides several correlation coefficients (CCs) to quantify the agreement between an atomic model and a cryo-EM density map (Afonine et al., 2018):
CCbox: correlation computed over the entire map volume;
CC correlation restricted voxels inside molecular mask;
CCpeaks- correlation evaluated at the strongest density peaks;
CCyolume: correlation calculated for a user-specified molecular volume, typically repre- senting the region occupied by the model.
These metrics probe complementary aspects of map- map-model agreement: global consistency, localized fit within the molecular envelope, and correspondence at prominent density features, and agreement within the molecular volume of interest respectively.
Q-score The Q-score quantifies atomic resolvability in cryo-EM maps (Pintilie et al. 2020). For each atom, the local density profile extracted from the map and compared against reference Gaussian profile, with the correlation the two serving as the Q-score A higher Q-score indicates that the atomic density is well resolved and closely resembles the expected Gaussian profile, while lower values reflect poorer local resolvability. We report the average Q-score over all atoms in the model, as implemented in Chimera's 's MapQ plugin, to evaluate overall map quality.
CryoRes CryoRes (Dai et al. 2023) is deep learning-based method for estimating local reso- lution from single cryo-EM density map. Unlike traditional approaches, it does not require half- maps or multiple reconstructions, making it broadly applicable in scenarios where only a final map is available. CryoRes predicts voxel-wise resolution estimates that highlight spatial variability in map quality. These local resolution annotations provide complementary information to global metrics, offering more fine-grained assessment of map reliability.
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G ADDITIONAL EXPERIMENT RESULTS
G.1 ABLATION STUDY SCUNET-BASED BACKBONE VS VIT-BASED BACKBONE
The ablation results validate the effectiveness of SCUNet-based backbones relative ViT back- bones across density map sharpening, super-resolution, and missing wedge restoration. As shown Tab. and g as well as the radar plots, SCUNet consistently outperforms ViT on both correlation- and resolution-based metrics. For comparability, metrics where lower values indicate better performance (e.g., FSC-based metrics) were negated so that all scores follow a higher-is-better convention, consistent with CCbox and related measures. The unified radar plot (Fig (0) summarizes these results, providing a holistic view of backbone performance across evaluation metrics.
Table 6: Evalutation metrics of SCUNet-based and ViT-based backbone models density map sharpening task.
phenix.map model cc
Chimera.MapQ
CCbox 1
CCmastc 1
CCpeaks f
Q-score f
CryoLVM (ViT)
0.826
0.733
0.739
0.387
CryoLVM (SCUNet)
0.894
0.821
0.806
0.444
Table 7: Evalutation metrics of SCUNer-hased and ViT-hased backbone models on density map super-resolution task.
phenix.mtriage
CryoRes
dmode +
FSC-0.143(A)
FSC-0.5(A) + Resolution(A) &
CryoLVM (ViT) 2.35
2.65
4.73
3.41
CryoLVM (SCUNet)
2.33
2.58
4.58
3.39
Table 8: Evalutation metrics of SCUNet-based and ViT-based backbone models on missing wedge restoration task.
mttriae
FSC-0.143(A)
FSC-O.5(A) 4
CCbox
CCunakk
CCvolume
CryoLVM (ViT) CryoLVM (SCUNet)
16.51 10.09
22.15
0.355
0.277 0.391
0.241 0.348
11.45
0.391
Crasto DET1
Cyyto (sCUNet)
5
-PAC-
Coulli
Dasere
eesolation
W/
Density map sharpening
(b) Density map super-resolution
(c) Missing wedge restoration
Figure 10: Radar plots comparing CryoLVM performance with SCUNet versus ViT backbones across three tasks.
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G.2 ABLATION STUDY OF COMPOSITE Loss OF MSE AND HISTKL
The experimental results summarized in Tab. 9 demonstrate that the proposed HistKL loss, when used in conjunction with the standard MSE loss, consistently outperforms the baseline MSB-only approach across all evaluation metrics in the density map super-resolution task. Beyond the im- provement of density map quality, the incorporation of HistKL loss also accelerates the training convergence. Our analysis reveals that the composite loss configuration reaches optimal validation loss epoch 107, whereas the MSE-only baseline requires 279 epochs to achieve its best validation loss repressnnnnn 2.6x reduction in training time. These results highlight the effectiveness and efficiency of the HistKL term guiding model optimization.
Table 9: Impact of different training losses on model performance.
phenix.
CryoRes
Resolution(A)
FSC-0.143(A) 4
FSC-0.5(() 4
Resolution(A) Å) &
CryoLVM (MSE)
2.36
2.63
4.61
3.45
CryoLVM (Composite) 2.33
2.58
4.58
3.39
G.3 ABLATION STUDY OF PRETRAINING
To validate the effectiveness of our JEPA-based pretraining approach, we conducted two comple- mentary ablation studies on the density map sharpening task. First, compared our pretrained- then-finetuned model against a model trained from scratch using identical architectures and hy- perparameters (Table IOP. The pretrained model demonstrates consistent improvements across all metrics. These results confirm that self-supervised pretraining on high-quality cryo-EM density maps enables the model to learn transferable structural representations that enhance downstream task performance. Second, we evaluated JEPA against the widely-used PaakeedAutoencodee (MAE) pretraining approach (Table [I]. While both methods benefit from pretraining, JEPA consistently outperforms MAE across all metrics.
Table 10: Evalutation metrics of pretrainedd-the--fnneeuee and trained-from-seratch models on den- sity map sharpening task.
phenix.may maa_mmdell.cmmmmmmmmmmmmoommmmmm
Chimera.MapQ
CC f
CCmask t
CC CCpeaks
Q-score t
CryoLVM (Scratch) 0.878
0.808
0.786
0.437
CryoLVM (Pretrain) 0.894
0.821
0.806
0.444
Table 11: Evalutation metrics of JEPA Pretrrrrt and MAE-pretraines models on density map sharpening task.
phenix.map.model.cc model_cc
Chimera.MapQ
CChox f CCmask
CCpeaks t
Q-score
CryoLVM (MAE)
0.881
0.813
0.791 0.806
0.441
CryoLVM (JEPA) 0.894
0.821
0.444
G.4 ABLATION STUDY POSTPROCESSING METHOD
During inference, CryoLVM processes density maps using sliding window approach with over- lapping patches, necessitating an effective fusion strategy to merge predictions from overlapping regions into a coherent output volume. We conducted an ablation study comparing two postprocess- ing methods: mean-wert weightee fusion, which assigns equal weight to all overlapping predictions, and Graussianwwiighted fusion, which assigns higher weights central regions of each patch and lower
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Published as a conference paper at ICLR 2026
weights to boundary regions (as detailed in Alg. D. To better differentiate between these two ap- proaches and amplify their differences, we adopted a larger stride of 24 compared to the previously used value of 12.
As shown in Tab. Gaussian-weighted fusion consistently outperforms mean-weighted fusion across all evaluation metrics for the missing wedge restoration task.
Table 12: Comparison of postprocessing strategies
phenix.mtriage
phenix.map-modeer
FSC-0.143(A)
FSC-0.5(A)
CCbox t
CCmask t
CCvolume
CryoLVM (Mean) 10.66
13.07
0.390
0.388
0.345
VM (Gaussian) 9.92
11.47
0.391
0.391
0.348
G.5 ADDITIONAL RESULTS OF SUPER RESOLUTION TASKS
This subsection presents extra results regarding the density map super-resolution task. As shown in Fig. [1 CryoLVM achieves state-of-the-art performance overall, outperforming all baselines in both the FSC-0.5 and the CryoRes metrics. Fig. 12 visualizes improvement local resolution for the EMD-0023 example: the deposited map spans local resolution range of 3.39 Á to 4.12 Å, whereas CryoLVM's predicted map compresses that range to 2.31 to 3.45 A. This illustrates that Cr not only enhances global resolution measures but also recovers finer structural detail in regions where the original map is weaker.
5.0
Resolution
22 3 od
FSC-0.5IA) & in
4.0
3.0
Deposited
DeapENhancer
EMGAN
Figure 11: Comparison of CryoRes estimates and FSC-0.5 for density map super-resolution task.
EMD-0023 PDB: 6GKH Resolution: 4.06 À
Deposited
EMGAN
DeepEMMancer
CryoLVM
Figure 12: Comparison of local resolution maps between different methods on case EMDD-0023. Local resolution maps calculated via CryoRes and visualized through Chimera.
G.6 ADDITIONAL RESULTS OF MISSING WEDGE RESTORATION
To provide thorough evaluation of missing wedge restoration performance, we present additional quantitative metrics and visualizations beyond the main results reported in Section 4.4. As shown
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in Tab. 13 CryoLVM achieves higher correlation coefficients in three complementary assesment criteria: 1) box. which measures global structural coherence by evaluating correlation across the entire reconstruction volume; 2) CCmaas. which quantifies reconstruction accuracy within a maksed region; and 3) Covolume- which assesses volumetric fidelity through voxel-wise correlation analysis of the complete density distribution. These improvements. indicate that CryoLVM not only recovers missing angular information more accurately but also maintains superior structural consistency. Fig. 14 presents the FSC-based resolution metrics (FSC-0. 143 and FSC-0.5), where CryoLVM exhibits significantly better resolution distributions, indicating more effective recovery of high-frequeng structural information lost due to missing wedge effects. Fig. [3 provides an additional visualization case for EMD-5106.
Table 13: Addition CC results for missing wedge restoration task. phenix.map mamnxx.map_modee mcap_lcdel_cc..
Metrics computed using
mhenix.map model
CC t
CCmask f
CCvolume t
IsoNet CryoLVM
0.364
0.371
0.328
0.391
0.391
0.348
85
Ieciet
DrytlM
Ietket
Crytow
balket
CoytoM
Figure 13: CC-based performance evaluation for missing wedge restoration task.
25.0
30
22.5
20.0
25
17.5
FSC-0.510
15.0
20
12.5
15
10
IsoNet
CryolVM
CryolVM
Figure 14: FSC-based performance evaluation for missing wedge restoration task.
G.7 VALIDATION OF IMPACT ON AUTOMATED MODEL BUILDING
To demonstrate the practical impact of CryoLVM processing on downstream structural biology workflows, we conducted a case study using ModelAngelo, statc-of-the-art automated model build- ing tool, on EMD-6656 (PDB: 5H30, resolution: 3.5 A). As shown Fig. 16 we compared atomic structures built from the deposited experimental map versus the M-soVy-shhrppeeed map. The -rocesed map yielded quantifiable improvements in model quality: RMSD the refer- ence structure decreased from 0.68 Á to 0.58 Á, and sequence match increased from 92% to 94.3%. These gains show that enhanced map quality directly translates to more accurate and complete auto- mated model building. reducing manual intervention requirements in structural determination work- flows.
G.8 PROTEIN SECONDARY STRUCTURE CLASSIFICATION
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Published as a conference paper at ICLR 2026
EMD-5106 PDB: 3IY1
Resolution: 18 Á
Input (with missing wedge)
IsoNet
CryoLVM
Ground Truth
Figure 15: Additional visualization case for missing wedge task. The original density map is EMD- 5106 from EMDB and added wedge-shaped mask for model processing.
EMD-6856 PDB: 5H30 Resolution: 3.5
Deposited ModelAngelo
CryoLVMM processed map ModelAngelo
RMSD: 0.68 A Sequence Match: 92%
RMSD: 0.58 A Sequence Match: 94.3%
Figure 16: Comparative model building quality assessment using ModelAngelo. Atomic structures were built automatically from (Left) the deposited experimental map EMD-6656 (resolution: 3.5 Á) and (Right) the same map after CryoLVM sharpening.
NURCC
0.4
ROC curve dlass JAUC - 0.55
ROC curve of daas JAUC 70)
ROC curve 035s JAUC 74)
RDC curve choc JAUC 62)
False Posttive Rate
Marly Alghe Manky
O-Mainly Alpha
1-Mainly Beta
2-Alpha Beta
3-Few Secondary Structure
Figure 17: Protein secondary structure classification and representation visualization results.
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Reviews:
[{"review_id": "Cm7f7XMGEo", "reviewer": "Reviewer_kuwW", "summary": "This paper presented CryoLVM, a pretrained foundation model trained on a large scale of cryo-EM density maps, using JEPA and a SCUNet-based backbone architecture. The base model was further finetuned on three different training pairs in a supervised manner, and compare to the baselines for different downstream tasks.", "questions": "- I believe that the same model architecture can be applied to task-specific training from scratch, instead of fine-tuning from a pretrained base model, using the same training pairs in the finetuning. Could the authors show the ablation study of this?\n\n- In postprocess (Appendix D.2), what does the normalization \"to remove biases resulting from uneven sampling\" mean (L806-807)? For at least one task (maybe sharpening), could the authors ablate the postprocess (e.g. replace Gaussian-weighted fusion with a simple overlapping average, and if possible, remove the additional normalization step)?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The application of JEPA in cryo-EM is novel and intuitive. Furthermore, although not the first pretrained model on cryo-EM density maps, it makes great sense to leverage the large scale of cryo-EM maps to help various downstream tasks.\n\n- The paper is well written and most of the presentations are easy to understand and interpret.", "weaknesses": "My main critiques of this paper are mostly on the downstream tasks and evaluations.\n\n- DeepEMhancer and EMReady used different training targets. CryoLVM used the EMReady approach, in which the training targets are simulated maps from atomic models. Strictly speaking, this should not be called as \"sharpening\" anymore, since the training targets are not sharpen maps but simulated maps.\n\n- It seems that the training set used in the sharpening and super-resolution task also differs from that of the baselines. The authors should at least provide a comparison with EMReady, using the exact same training data to finetune the pretrained base model, and compare the results with EMReady.\n\n- Additionally, I do not believe that \"super-resolution\" should be considered as an distinct job type. The goal of map postprocess, whether sharpening or not, is all to help model building. The \"super-resolution\" term here and in the EM-GAN paper, is actually to make the \"resolution\" higher when generating the simulated maps, which is very similar to that of EMReady. Therefore, I do not see the great difference between the two tasks.\n\n- About the missing wedge task: IsoNet was introduced to be applied on the tomograms, but it seems the experiments were performed on a \"subtomogram-like\" setting. Also in Fig. 5, the example is not very clear and the difference seems subtle. Could the authors share more visual examples, showing the input (with the missing wedge artifact), and the outputs from both methods?\n\n- Since I think both \"sharpening\" and \"super-resolution\" do not have much difference and should be both under the same umbrella of map postprocessing, it becomes a bit of overclaim for CryoLVM to be a foundation model.", "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": "NpZ4SpwXfN", "reviewer": "Reviewer_GTqs", "summary": "This paper presents CryoLVM, a foundation model for cryo-electron microscopy (cryo-EM) 3D density maps. Thanks to the leverage of the Joint-Embedding Predictive Architecture (JEPA) for self-supervised representation learning, it eliminates reliance on hand-crafted augmentations while retaining high-level semantic information. The model employs a SCUNet-based backbone to learn semantically rich representations of cryo-EM density maps. CryoLVM is pretrained on ~7K experimental density maps and evaluated across three major downstream applications: density map sharpening, density map super-resolution, and missing wedge restoration. The results demonstrate consistent performance improvements, with qualitative results showing clearer structural detail recovery and improved interpretability in experimental cryo-EM maps.", "questions": "1. In Figure 1, the “context” and “target voxels” are visualized using 2D patches. I am assuming JEPA here actually operates on 3D volumetric patches. Could the authors clarify?\n\n2. The input generation section mentions cropping to 48³ volumes, while Table 4 lists “patch size = 48” and “voxel box size for input/output.” Could the authors clarify the distinction between the input volume resolution and patch granularity used?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The adoption of JEPA for volumetric cryo-EM data is a forward-looking design choice. It effectively removes the dependency on task-specific or hand-crafted data augmentations, allowing the model to learn semantic, noise-robust representations directly from experimental density maps.\n\n2. CryoLVM is among the first to systematically explore a foundation modeling approach for cryo-EM 3D density maps. This area previously lacked large-scale pretraining paradigms despite the existence of related efforts for cryo-EM images (e.g., DRACO, CryoFastAR).\n\n3. The authors conduct both quantitative and qualitative analyses on three post-processing tasks, density map sharpening, super-resolution, and missing wedge restoration, demonstrating the adaptability and robustness of the pretrained model.", "weaknesses": "1. While CryoLVM focuses on 3D density maps, related models such as DRACO (Shen et al., NeurIPS 2024) and CryoFastAR (Zhang et al., ICCV 2025) have introduced foundation frameworks for 2D cryo-EM images and pose estimation, respectively. Although these works address distinct aspects of cryo-EM analysis, they fall under the broader category of “foundation models for cryo-EM” and should be acknowledged and positioned as complementary.\n\n2. Designing a foundation model for 3D volumetric data poses significant computational challenges compared to 2D image models due to cubic complexity. The current implementation appears to rely on relatively small input volumes (48³ voxels), which may limit representational granularity. It would be valuable for the authors to discuss how CryoLVM scales to larger 3D contexts or whether insights from other 3D foundation model architectures (e.g., medical imaging) can be leveraged to mitigate this issue.\n\n3. The pretraining corpus includes only ~7K experimental density maps, which is orders of magnitude smaller than typical datasets used to train modern foundation models in vision or language domains. Such a limited dataset raises significant concerns about whether CryoLVM truly demonstrates foundation-level generalization versus task-specific overfitting. The manuscript does not discuss dataset diversity (e.g., coverage of molecular weights, symmetry classes, or reconstruction quality), nor any data augmentation or synthetic data generation strategies to potentially compensate for the scale limitation. \n\n4. Although the reported results are quantitatively promising, these tasks are not convincingly linked to practical biological or structural analysis workflows. It remains unclear how these improvements translate into measurable benefits for end users such as structural biologists. The authors should provide a more explicit discussion or demonstration of how CryoLVM outputs improve real-world cryo-EM analysis, or consider including more impactful downstream tasks.", "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": "UmDytU0S6q", "reviewer": "Reviewer_j5hK", "summary": "This paper introduces CryoLVM, the first self-supervised foundation model for cryoEM. By adopting JEPA, the model learns to predict masked regions from surrounding context, thereby training a powerful and generalizable encoder. In addition, the authors propose a histogram-based distribution alignment loss to accelerate convergence and enhance fine-tuning performance. Comprehensive experiments across three downstream tasks demonstrate that CryoLVM achieves state-of-the-art performance, outperforming established baselines such as DeepEMhancer, EMReady, EMGAN, and IsoNet.", "questions": "(1) In the JEPA paper, the authors highlight its scalability. Can CryoLVM continues scaling with more data and more parameters? This would be an interesting topic both for cryoEM and JEPA. \n\n(2) The motivation of utilizing JEPA for cryoEM is not very clear. Can you show some case study to illustrate what JEPA brings to building foundation models for cryoEM.\n\n(3) What is the motivation of the histogram loss? I understand that it would help calibrate the distribution of density values, but I think it is not very clear how it benefits the map quality, or does it hacks the evaluation metric?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The application of JEPA to volumetric cryo-EM data is novel and well-justified. The histogram-based loss is an intuitive addition that addresses data distribution mismatches.\n\n(2) Comprehensive evaluations are conducted across three downstream tasks using multiple cryo-EM quality metrics, and the results consistently surpass previous state-of-the-art baselines.", "weaknesses": "(1) While the paper provides strong evidence that JEPA is effective for cryo-EM representation learning, it would be more convincing to include comparisons with other self-supervised or generative pretraining methods (e.g., VAE- or MAE-based encoders) under identical data settings.\n\n(2) Since the study mainly focuses on pre-training a density encoder, the results would be more solid if the authors compare CryoLVM with a model trained entirely from scratch. This will directly quantify the contribution of the pretraining stage and validate its effectiveness.", "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": "kBI0rk4hMO", "reviewer": "Reviewer_v93h", "summary": "This paper introduces CryoLVM, a pretrained model leveraging JEPA self-supervised pretraining. The pretrained model can be further finetuned for downstream tasks of map sharpening, super-resolution, and missing wedge restoration. Experiments on these three tasks show that CryoLVM surpasses baseline methods.", "questions": "- It’s not clear how the loss for downstream tasks is applied to each of the three tasks. Do they use exactly the same loss or any other choices?\n- Have you compared the JEPA training with the MAE training? This could validate the claim of low-SNR protein density.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well-written and easy to follow.\n- The usage of distributional alignment loss is novel and appears to be effective in the ablation study.\n- The performance of CryoLVM on all downstream tasks is better than baseline methods, showing the effectiveness of the method.", "weaknesses": "- While the paper claims that the adoption of JEPA, which operates in feature space, is to avoid the low-SNR density. However, the CryoLVM model is trained on Cryo2StructData that consists of densities spanning 1-4 Å. With the high resolution, these densities should not contain much noise.\n- While the composite loss is better, the improvement to the MSE loss is marginal, as shown in Table 9.", "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)