CortiLife: A Unified Framework for Cortical Representation Learning across the Lifespan
Authors: Pengcheng Xue
Dong Nie
Jie Luo
Daoqiang Zhang
Xuyun Wen
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-language Pretraining
Cortical surface modeling
Lifespan
Primary area: foundation or frontier models, including LLMs

Abstract: The human cerebral cortex encodes rich neurobiological information that is essential for understanding brain development, aging, and disease. Although various cortical representation learning methods have been proposed, existing models are typically restricted to stage-specific cohorts and lack generalization across the lifespan. While recent vision-language models offer a promising direction, building a unified framework for cortical representation faces three key challenges: (1) the non-Euclidean manifold structure of cortical surfaces, (2) homogenization of individual folding patterns induced by registration, and (3) distribution shifts of cortical features across the lifespan. To address these issues, we present CortiLife, the first unified vision-language framework for lifespan-aware cortical representation learning. Specifically, CortiLife introduces a surface tokenizer that integrates icosahedron-based surface patchification with multi-level patch encoding to transform complex cortical manifolds into compact token representations. The multi-level encoding incorporates three complementary streams that capture local topology, global interactions, and patch-wise distributional patterns, effectively mitigating the challenges of homogenization and distribution shifts. Furthermore, CortiLife integrates masked self-distillation with metadata language prompting, embedding information such as age, sex, health status, and attribution type into the text encoder to better capture individual-specific cortical representations while enabling both age-aware and modality-aware modeling. Extensive experiments on downstream tasks, including two encoder-frozen tasks (age prediction and cortical parcellation) and four encoder fine-tuning tasks (brain disorder diagnosis), demonstrate that CortiLife consistently outperforms state-of-the-art baselines across different age stages and modality types, underscoring its effectiveness and generalization ability.

Published as a conference paper at ICLR 2026
CORTILIFE: A UNIFIED FRAMEWORK FOR CORTICAL REPRESENTATION LEARNING ACROSS THE LIFESPAN
Pengcheng Xue! Dong Nie Jie Luo!:? Daoqiang Zhang' & Xuyun Wen 'College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
"Key Laboratory of BrainnMachine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China 3 Meta Inc., CA, USA
edu. cn, dongnie@cs unc.edu, sz241 011061070@naa www.aaeeeuuuu........... dgzhang@nuaa edu, en, wenxuyunênuas, edu, en
charles1233
ABSTRACT
The human cerebral cortex encodes rich neurobiological information that is es- sential for understanding brain development, aging, and disease. Although vari- ous cortical representation learning methods have been proposed, existing models are typically restricted to stage-specific cohorts and lack generalization across the lifespan. While recent lisoun-language models offer a promising direction, build- ing a unified framework for cortical representation faces three key challenges: (1) the Euclidean manifold structure of cortical surfaces, (2) homogenization of individual folding patterns induced by registration, and (3) distribution shifts of cortical features across the lifespan. To address these issues, we present Cortil.ife, framework for lifespan- aware cortical represen-
the first unified tation learning. Specifically, CortiLife introduces surface tokenizer that inte- grates icosahedron-basee surface patchification with multi-level patch encoding to transform complex cortical manifolds into compact token representations. The multi-level encoding incorporates three complementary streams that capture local topology, global interactions, and patch-wise distributional patterns, effectively mitigating the challenges of homogenization and distribution shifts. Furthermore, CortiLife integrates masked self-distillation with metadata language prompting, embedding information such as age, sex, health status, and attribution type into the text encoder to better capture indiiiuual-specffic cortical representations while enabling both age-aware and modality -aare modeling. Extensive experiments on downstream tasks, including two encoder-frozen tasks (age prediction and cortical parcellation) and four encoder fine-tuning tasks (brain disorder diagnosis), demon- strate that CortiLife consistently outperforms state-of-the-art baselines across dif- ferent age stages and modality types, underscoring its effectiveness and general- ization ability.
INTRODUCTION
Representations of the cerebral cortex, which encode rich neurobiological information in met- rics such as cortical thickness (CT), surface area (SA), and mean curvature (MC), are critical for both cognitive neuroscience and clinical diagnostics [ettwer al. (2022); Storsve al. (2014); de Vareilles et al. (2023). These structural features serve as powerful biomarkers, offering insights into neurodeveloppmental and aging trajectories Dickerson & Wolk (2012); Fjell et al. (2015). as well as indicating pathologies associated with disorders like Autism Spectrum Disorder (ASD) Ecker (2013) and Attention- DeficiifHyyerractiiit Disorder (ADHD) You et al. (2024). Thus, learning effective cortical representations is essential for advancing personalized brain mapping and enabling computer-aided disease detection.
"Corresponding Author

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Published as a conference paper at ICLR 2026
Pard-ty
Cortical thickures
Traittional Purch of raw MRI
Intmary
A
Cortical surface mesh
Sub 01
Sub 02
Complex geometry
(b) Registration-indueee homogenizatinn
(c) Lifespan beterogeneity
Figure I: Challenges in universal cortical representation: (a) Non Non-Euclidean manifold structure; (b)
Registration-indueel homogenization; (c) Distribution shifts across the lifespan.
Recent advances in deep learning have introduced powerful tools for this task. Spherical CNNs Zhao eet al. (2019] 2021) have been developed to respect the spherical topology of the cerebral cortex, while Surface Vision Transformers Dahan al. 2022) have shown success in modeling long-range dependencies across cortical patches. Despite their progress, they share a critical limitation. These models are typically trained on narrow age cohorts, making them unable to account for the profound structural dynamics that the brain exhibits across the human lifespan. Therefore, developing uni- fied framework for learning cortical representations that bridge diverse developmental stages across the lifespan remains a key and unsolved challenge.
Concurrently, vision-language models (VLMs), exemplified by CLIP Radford et al. (2021). have demonstrated remarkable transfer learning capabilities by aligning large-scale image and text rep- resentations. This paradigm has also shown great promise in medical imaging, with models such as BiomedCLIP Zhang al. (2023) and subsequent work Peterssn et al. ); Lai et al. (2024); Cui et al. (2025) achieving state-of-the-art performance by linking visual data with textual medi- cal records. However, extending this success to the cortical surface data is non-trivial, facing the following three challenges: (i) Non-Euclidean manifold structure: As shown in Figure [a], the cerebral cortex is highly folded non-Euclidean manifold with intricate topological and geometric properties. This structure fundamentally differs from conventional 2D grids or 3D volumetric data, making standard convolution- based or grid-based vision models ineffective at directly capturing cortical geometry and local spatial patterns. (ii) Registration-indueer homogenization: Standard preprocessing pipelines typically register individual cortical surfaces to a common template to en- able cross-subject comparability, as shown in Figure [b). However, this registration inevitably reduces the distinctiveness of individual gyral-sulcal folding patterns, resulting in higher similarity of corresponding patches across subjects and thus hindering individualized representation learning. (iii) Distribution shifts across the lifespan: The cerebral cortex undergoes dynamic and complex structural changes across the human lifespan. These developmental variations give rise to diverse distributional shifts in different cortical features (c.g., CT, SA, and MC) at different age stages (as shown in Tigure[e). This poses significant challenges for achieving unified lifespan-aware cortical representation learning.
To address these challenges, propose CortiLife, the first unified vision-language framework for cortical representation learning across the entire lifespan. The core components of CortiL- ife are surface tokenizer and vision-language model (VLM). The surface tokenizer integrates icosahedron-based surface patchification with multi-level patch encoding to transform complex cor- tical manifolds into compact token representations. In particular, the multi-level patch encoding module incorporates three complementary streams that jointly capture local topology, global in- teractions, and patch-wise distributional patterns, thereby mitigating the difficulties of representa- tion learning caused by incutraatoo-inducce homogenization and lifespan-relatee distribution shifts. For vision-language modeling, we adopt masked self-distillation representation learning as the vi- sual backbone and introduce metadata language prompting, embedding information such age, sex, health status, and feature type into the text input. This design allows the model to better cap- ture individual-speciff cortical representations while enabling both development-un aware and feature- aware modeling. Extensive experiments on three primary surface-base tasks, including brain dis- order classification, age prediction, and cortical parcellation, consistently show the state-of-the-ae
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CortiLife
(b) Surface Tokenizer
Input
Surface Tokeniser
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Linaloor
Creems
1
Reeanstraction
FPS
Seode
Corteel loe
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Decodence
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(bl) Lecal Topology Eneeding
(b2) Global Interaction Encoding
Pilcbrry
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Figure 2: Overview of the CortiLife framework, which is composed of three main components: surface tokenizer, masked self-distillation learning, and metadata language prompting.
performance, demonstrating superior generalization across the cortical modalities and the entire lifespan.
2
RELATED WORK
2. CORTICAL REPRESENTATION LEARNING METHOD
The cortical surface is a highly folded non-Euclidean manifold, which conventional 2D and 3D CNNs cannot effectively model. To address this, Spherical U-Net Zhao al.) (2019) introduced convolution and pooling operations resampled cortical meshes, achieving promising results in parcellation and developmental mapping. Its extension, Spherical Deformable U-Net Zhao et al.) (021), further incorporated deformable convolutions to better capture folding variability. More cently, transformer-ban models Dahan et al. )0022); Cho et al. (2022) have leveraged self-attention on the sphere, advancing tasks such infant cortical surface quality assessment and outperforming CNN-based baselines. Nevertheless, these approaches remain limited to specific cohorts or devel- opmental stages, lacking the ability to generalize across the entire lifespan.
2.2 VISION-LANGUAGE. MODEL.
Contrastive Language-Iman Image Pre-training (CLIP) Radford et al. (202I) has established a foundation
for multimodal learning enabling robust image-text alignment and generalization. In medical imaging, CLIP-style pretraining has been adapted to modalities such as X-ray Wang et al. (2022b); You et al. 2023), MRI Avci et al. (2025), and CT You al. 2025), showing promise in tasks like report generation Liu et al. (2024) and zero-shot inference Huang et al. (202T). Large-scale Khatkaaaaa Khattak al. (2024) further extend CLIP diverse modalities, while segmentation-oriental extensions like CLIPSeg Lüddecke & Ecker (2022) and CRIS Wang et al. (2022a) have broadened its applications. Nevertheless, these approaches remain focused on 2D or volumetric images, leaving the non- non-Euclidean cortical surface modeling problem largely unex- plored.
efforts such UniMed-CLIP
3 METHODOLOGY
In this section, describe the proposed CortiLife, with the overall framework illustrated in Figure 2] is composed of three core modules: 1) a surface tokenizer, 2) masked dift-iisiilatiion vision representation learning, and metadata language prompting.

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3.1 SURFACE TOKENIZER
Before constructing the VLM, it is crucial to transform cortical surface data into compact token rep- resentations. Unlike conventional patchification methods for 2D or 3D images in Euclidean space, cortical surfaces lie on a non-Ruclidean manifold, making them incompatible with regular square or cubic partitioning. Furthermore, cortical representation learning faces two major challenges: registration-induced homogenization and distributional shifts across different ages and modalities, which prevent tokenization strategies designed for natural images from producing effective represen- tations. To overcome these issues, we introduce a surface tokenizer composed of two key modules: an icosahedron-base
surface patchification module and a multi-level patch encoding module. In our work, we denote the cortical surface data Th. E IRN,.1, where N, is the number of vertices. The value at each vertex encodes a morphological attribute, such as CT, MC, or SD. cortical map containing specific attribute is referred to cortical modality.
3.1.1 ICOSAHEDRON-BASER SURFACE PATCHIFICATION
Unlike Fuclidean 2D images or 3D volumetric data, the cortical surface map cannot be partitioned into regular square or cubic patches. To address this, we adopt an coosahedron-basee subdivision strategy, which divides the surface into local triangular facets and subsequently aggregates them into regular triangular patches Cho et al. (2022). Specifically, given cortical data with N, vertices, we reorganize all surface vertices such that each patch corresponds to triangular structure containing an equal number of vertices, thereby forming P patches. Through this process, the original cortical map Te E R is partitioned into triangular patch set xp E IR PR*,, where Np represents the number of vertices in each patch. this work, we set = 640, meaning that each hemisphere is partitioned into 320 patches.
3.1.2 MULTI-LEVEL PATCH ENCODING
After patchification, we design a multi-level patch encoding strategy with three complementary streams that capture local topology, global interactions, and patch-wise distributional patterns for each patch, thereby mitigating the challenges of registration-induces homogenization and distribu- tional shifts across the lifespan during the representation learning.
Local Topology Encoding, The local topology encoding aims to focus on capturing fine- tne-grained geometric and morphological cues within each patch, thereby preserving localized cortical details. Such fine-grained representations establish foundation for identifying subtle variations across in- dividuals and developmental stages. Specifically, we employ a spherical convolution- hased surface encoder, termed Spherical ResBlock, to capture localized spatial information within each patch Zhao et al. (2019). This module composed of an initial stem layer, four batch normalization layers, and four spherical convolution layers with residual connections. For each input triangular patch Ip E R RI,,,, it produces the corresponding embedding ef E IR I..M.,, where M, represents the feature dimensions of local topology encoding.
Global Interaction Encoding. Global interaction encoding aims to model long-range dependencies across patches, enabling the representation to integrate broader contextual information across the cortical surface. Specifically, we employ four layers of self-attention followed by four feedforward blocks to capture global interaction representations across all the patches. Finally, this module pro- duces the corresponding global interaction representations for each patch, denoted as eς e I R1*Mp, where Mp represents the feature dimensions of global interaction encoding. These representations provide a holistic perspective for characterizing structural variations of the brain across the entire lifespan and across individuals.
Patch-wind Distribution Encoding, Considering the feature distribution shifts across different age groups and different modalities as illustrated in Figure [cc) we specially design a patch-wise dis- tribution encoding strategy, named adale-adaptive encoder. As shown in (33)). its core idea is to introduce a set of scale bases, each corresponding to a different statistical level of the feature distribution. Patch representations from all ages are projected onto these shared scale bases and then fused adaptively based on their original statistics. This design simultaneously unifies cross-age dis- tribution levels and preserves age-specific distribution characteristics. The detailed implementation process is described as follows. First, it computes statistical descriptors xm,s including the mean (1mm P+1) and standard deviation (xi IR P+1), across all the vertices within each patch, and
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adaptively projects them into scale spaces by using the following formulation:
Zi (Tm) = (x w, + K2 b.), [I....,
Im Tm E [Zyms Fpsl
(1)
where LN denotes Layer Normalization, A; is a predefined scale value, w, and b, are learnable weighting and bias parameters at the i-th scale space. The term k; * by serves as a scale-dependent bias that maps the mean and variance descriptors into distinct scale spaces. Through Equation I obtain the representation of distributional descriptors z, of I' at the i-th scale space. Second, we design a scale- addative weighting mechanism to aggregate these representations across n scales, which is formatted as
log
[2mll Nr.
+
(Tm) = Σ an (()) 2: 2:(2m), where (x(())) 2=1
(2)
Σ}=1 log
[ Ar
+
set of predefined scales (kq...,,k,) }. The weighting function a.(-) integrates the representations z; (x(z)) according to n scales to learn embeddings in multi-scale distribution space, thereby mitigating diverse age-related distribution shifts. In this step, scales that are more compatible with the patch's 's intrinsic distribution are assigned larger weights, enabling the encoder to better preserve the individual statistical patterns. By using the above two steps, we obtain the final patch-level distribution pattern representation ep E IR PMM, where M3 represents the feature dimensions of distribution encoding.
where the weighting function (.). implements gating mechanism
Embedding Fusion. After obtaining patch representations at three different levels, we concatenate them along the channel dimension and pass the concatenated features through linear layer to enable channel-wise interactions, yielding the final output of the surface tokenizer for each patch, denoted In this work, the M.. Mp. and M, are set to 256, 256, and 32,
as Erokeniaer E
respectively.
3.2 MASKED SELF- DISTILLATIIO VISION REPRESENTATION LEARNING
Studies have shown that the cerebral cortex exhibits substantial spatial redundancy Zhao et al. (2023), which motivates our use of an MAE-based strategy for representation learning. However, our goal is not only cortical reconstruction but also semantic alignment with metadata through CLIP-based objective. While random masking is effective for reconstruction, it does not ensure that developmentally informative regions remain visible, which is critical for effective alignment. To overcome this limitation, we adopt a masked self-distillation strategy in a teacher-student frame- work, where the teacher provides semantically enriched attention guidance and the student focuses on patches carrying high-level developmental information. This design allows the model to achieve both high-quality reconstruction and more effective developmental semantic alignment. Specifi- cally, both teacher and student networks share the same 10-layer Transformer architecture, with the teacher updated through the exponential moving average (EMA) of the student. The teacher net- work processes the full set of patch tokens to capture holistic cortical representations, whereas the student network learns from a masked subset of cortical patches. The masked patches in the student network are determined based on self-attention scores computed from the representations generated by the teacher. Concretely, given the learned representations from the teacher network, we compute an attention score for each patch follows:
AttScoree 1 Σ Softmar
Qi K2()) Qc.K.0)) Vd
(3)
where H denotes the total number of attention heads across 10 VIT blocks, Q; is the query vector of the [CLS) token of the i-th attention head, Kę()) represents the key vector of patch j of the i-th head. We use this development-awarc [CLS] (optimized by following metadata language prompting) as a query over patch tokens and retain regions with the highest 25% attention weights, ensuring that the student networks learn from the most informative cortical regions. The global embeddings obtained from the teacher and student networks are denoted as Eteacher and Extudent- For the detailed discussion of the effectiveness of this component, please refer to Appendix A.4
In this module, we design two loss functions, including reconstruction loss Lyecons and alignment loss Lats- The reconstruction loss Tyrcoons is employed ensure that the student network accurately
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recovers the fine-graine grained details of cortical data, and is defined as:
Lrecoms
Σ
i - x.
c; - xi
(4)
TM
iEM
REV
where M and V denote vertices of masked and visible patches.
Alignment loss, Lats. is designed to maintain global semantic consistency between Lteacher and Estudent, since both networks are trained to extract features from the same cortical data. In this work, employ the KL divergence to enforce distributional alignment between the two represen- tations, which is defined as:
Lote -
(5)
where pl and p!*) represent the distribution of Ereacher and Estudents Essuddets respectively.
3.3 METADATA LANGUAGE PROMPTING
To ensure robust generalization across the lifespan and different modalities, we incorporate lifespan- aware metadata (age, sex, health status, and feature type) into the training process, guiding the vision encoder to capture high-level developmental semanties. For semantic modeling, we adopt PubMed- BERT Gu al. (202I). domain- specifcc language encoder pretrained on large-scale biomedical text. By providing a fixed semantic space, PubMedBERT generates discriminative embeddings for similar metadata and enhances the alignment of developmental information. The textual input is for- mulated using the template: "The age of the subject is [age]. The gender of the subject is [gender]. Health status: [status]. Attribute: [feature type].".
For the objective of vision-language modeling, we employed the classical contrastive learning loss function. The loss function defined based on the cosine similarity between vision embedding Ei Estudent and text embedding T,, formulated
St.. where 8i, E Studdntt [[T
Extudent
logits
(6)
The logits will be scaled by a learnable temperature т. And the loss function of image-to-text matching (i.e., Lı2r) and text-to-image matching (i.e., Lr21) are defined as follows.
L12T
1 N
Σ
N
log
exp(logitsL.)
LT2
N Σ log
exp(logitas.))
Σ 1==1 exp(logitsss))
Σ exp(logitsg.)
Finally, we compute the average loss of Li2r and L121-
Letip = (f.2T + fr21)/2
(8)
3.4 TOTAL LOSS FUNCTION
The total loss combines the reconstruction loss Lreeons and the alignment loss Lats from masked self-distillation vision representation learning, and the image-to-text matching loss Latips which are formatted as
L = C= + Lets + Lrecons
4 EXPERIMENTS AND RESULTS
4.1 DATASETS
In this study, we collected large-scale imaging data spanning the entire lifespan for model training and evaluation, comprising nine datasets in total. The overall statistics of each dataset are summa- rized in Table | this study, we used three representative cortical modality data as examples for model generalization evaluation, including cortical thickness (CT), mean curvature (MC), and sulcal depth (SD). After preprocessing, each cortical data set contains 81,924 vertices in total, with 40,962 vertices in each hemisphere. More detailed dataset descriptions are provided in the Appendix A.2

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Table 1: Summary of dataset information.
ID
Dataset
Samples
Age
Geader (M/F)
Diagnosis (casc/contrel)
DHICE Makroponlos et
2018
887
26 weeks
478/409
Q/887
2
CHD
229
1-3 years
123/106
128/101
3
CBC Xu CBCEXu al
2024
252
1-6 years
133/119
0/252
4
CCNI
2024
559
4-18
304/255
0/559
5
ADHD-200BCINN B:C al
2017
972
7-27 years
599/373
388/584
6
ABIDE II
1.114
5-64 yeurs
856/258
521/593
7
ABIDE
1,112
6-64 years
948/1664
539/573
8
HCF HCFVan Van Essen et al
2013
1,206
years
550/656
0/1,
9
DDDK 31
2008
7,597
55-95 years
4,185/3.412
5,438/2,159
10
1061
13,928
26(w)-95(y
8176/5752
7014/6914
Table 2: Performance comparison downstream tasks under the encoder- frozen setting,
Tisk
(a) Age prediction
(b) Cortical parcellation
Methods
CT
MC
SD
CT
MC
SD
CLIP
3.003=0.319
0.22840.341
3.204201119
0.721
0,760=0018
0.919=00077
ACT.IP
3,243+00020 3.1934+0.10 106
3,155+00.52
0.636+00.006
0.672+00.15
0.745--0027
DetailCLIP
3.156+0.105
3.11240..07
3.137+0.091
0.785+0.008
0.804+0.006
0.322=0.014
CARZero
5.682=1.463 5.195118816
5,914213592
CortiLife
3.124=0.078
2.990::01100
3.006:0.119
0.905-200055
0.925=0.003
0.957=0.001
4.2 EXPERIMENTAL SETUP
Environmental setup. For pretraining, CortiLife and all other pretrained bascline models were pretrained using AdamW (Ir=5e-4, weight decay=le-4) with batch size of 64 for 10 epochs on four NVIDIA 3090 GPUs. For the downstream fine-tuning,
employed the stochastic gradient descent (SGD) optimizer with learning rate of 0.001 and batch size of 40, and conducted on single NVIDIA 3090 GPU for 200 epochs. The selection of mask ratio in masked self-distillation framework is shown in the Table S in Appendix.
Encoder Frozen on Downstream Tasks. All methods were pretrained on eight datasets, including DHCP, CHD, CBCP, CCNP, ADHD-200, ABIDE-II, ABIDE-I, and ADNI. HCP was used for the two downstream-task evaluations, including age prediction and cortical parcellation. We kept the vision encoder fully frozen and trained only the MIP head for age prediction and cortical parcella- tion. For the training of MI.P module, we use 80% of the data for training and the remaining 20% for testing. Performance for age prediction cortical parcellation was benchmarked against the DKT-40 ground truth using the DICE coefficient.
evaluated using mean absolute error (MAE), while
Encoder Fine-tuning on Downstream Tasks. We evaluate the model by encoder fine-tuning on four brain disease diagnosis tasks spanning different age groups, including CHD dataset (Conget(Connea heart (iscase(CHH)) Healthy controls), ABIDE (Autism Spectrum Disorder (ASD) Healthy controls), ADHD-2000 Atttenio Deficit/Hyperactiiv Disorder( vs Healthy controls) and ADNI(Alzheimer's disease(AD) Healthy controls). In this experiment, the baselines include both non- pretrarted and pretrained models. For the non-pretrained models, we use 80% of the data for training and the remaining 20% for testing. For the pretrained models, we first pretrain all datasets except the target dataset, and then fine-tune on 80% of the target dataset while reserving the remaining 20% for testing. All models with pretraining share the same setting about pretraining datasets and downstream dataset. Accuracy (ACC) and area under the ROC curve (AUC) are used as evaluation metries. In addition, we evaluate the performance CortiLife with different proportions of data for fine-tuning, including 20% and 40% with results reported in Table 1ale9iin the Appendix.
4.3 ENCODER FROZEN ON DOWNSTREAM TASKS
In this section, we evaluate the performance of CortiLife on downstream tasks under frozen-vision encoder settings. We benchmark our framework against four ffate-o--the-at baselines, including CLIP Radford et al. (2021). ACLIP Yang et al. (2023), DetailCLIP Monsefi et al. (2024), and CARZero Lai et al. (224). Due to issues in CARZero's 's aligmment mechanism, we did not include baseline for the parcellation task. All methods were implemented under the same experimental

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Ground Truth DKT-40
CLIP
ACLIP
DetailCLIP
CortiLife
MC
SD
Figure 3: Visualization comparison of cortical parcellation. Our method produces results that are clearly superior to other approaches in both regional accuracy (e.g., the cingulate gyrus highlighted in yellow circles) and boundary details (e.g., the inferior temporal gyrus highlighted in red circles).
(a) Age-related distribution
Sex-related distribution
(c) Diagnosis-related distribution
MC
MC
MC
Age
Male
Female
AD
HC
Figure 4: Visualization of representation embeddings from CortiLife in the ADNI dataset.
setup as ours for fair comparison. The results are summarized in Table 2 addition, we further visualize the embedding distributions to assess their effectiveness.
Age Prediction. As shown in Table Aja), CortiLife achieves MAE values 3.124, 2.990, and 3.006 CT, MC, and SD, respectively, surpassing all baselines. These results indicate that the embeddings produced by CortiLife effectively capture structural characteristics associated with age information, thereby providing a faithful representation of the dynamic processes underlying brain changes.
Cortical Parcellation. Table shows that CortiLife achieves DICE scores of 0.905, 0.925, and 0.957 on CT, MC, and SD modalities, respectively, surpassing the second-best models by 120, 0.121, and 0.038. In addition, the visualization results in Figure demonstrate that cortical par- cellations derived from our model's representations exhibit substantially higher accuracy in both regional delineation and boundary localization compared SOTA methods. This result indicates that the embeddings learned by our model capture more fine-grained information and exhibit higher fidelity.
Visualization of Embeddings. For the pretrained model, we obtain the embeddings by feeding only the cortical surface features into the vision encoder, without providing any text prompts. We employ t-SNE to visualize the distribution of representations learned by CortiLife. Using the ADNI dataset as an example, we illustrate the representational differences across different age groups, genders, and disease categories. The results are shown in Figure Figure 4 a) demonstrates that our method successfully captures age-related information for each cortical modality data, exhibiting a smooth gradient along the age continuum. Figures (bb and (c) further indicate that the model not only learns gender-specific heterogeneity in developmental patterns but also extends this heterogeneity to disease states, thereby highlighting sex-specific disease trajectories.
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Table 3: Performance comparison on downstream brain disease diagnosis tasks under fine-tuning setting. CT, MC, and SD represent different cortical modality data.
ADHD
AD (aging))
Modality
Methods
Pretrain
(Iulancy)
(Adlnlesoemoe&
ACC
AUC
ACC
AUC
ACC
AUC
SphericalCNN
K
0.7861000028
0,78010.030
0.674110.077
0.688.100221
0.92610.0122
0.9561000009
Spherical U-Ner
X
0.787-0/21
0.789-0.029
0.66++0.01X
0.668-00018
0917-0.009
0.965+0.007
WSSADN
0,792=0.021
0.81::0.045
0.81#0.010
0.669=0.027
0.926=0.008
0.006
NeuroExplainer
0,760=0.047
0.812+0.000
0.666+0.027
0.679+00021
0.32-00.122
0.885+0.024
60 CT
Surfface
0.785:10.44
0.796:0.011
0.61110.010
0.673:000200
0.19::0.017
0.87710.023
CLIP
0.79010.052
0814100010
0.601000100
0.61100016
0.92.00,005
0,97810.012
ACIJP
0,720-00012
0.795-00.019
0.62240.015
0.648+00055
0.922-0.011
0.975+00007
DetailCLIP
0.696::0.099
0.82140.020
0.33740.024
0.62
0910=00.110
CARZero
0.777.0.001
0.79710.006
0.62810.006
0.60110.027
0,00810.025
0,97910.000
Comtilite
0.801-0.001
0.823-41016
0.977-0.055
0.730-00.077
0.928
0.981
SphericalCNN
0.622:00012
0.665
0.616:10.009
0.607.000039
0.928:000013
0.957.10.014
Spherical U-Ner
X
0.641-00037
0603--0.060
0,228-0.018
0.616+0.220
0.27-+0.005
0.960+0.007
WSSADN
0.642=00060
0,721=0.0664
0.63010.029
0.617=0.052
0.921=0.010
0.955
NeuroEsphaine
0.569.0.027
0.605
0.628.000100
0.755 0.06
0.80510.062
(b) MC
SurfaceVsstnnrrmnitmeer
0.647:0002
0.722-40.03
0623360.003
0.582+00015
0831-00022
0.000
CLIP
0.576:00037
0.555:00002
0.59510.021
0.586:000223
0.18:00.010
0,97310.008
ACLJP
0.578=00044
0.09900.055
0.585+0.058
0.601+0.000
0.920-0.005
0.968+0.004
DetailCLIP
0.614100.06
0.665
0.61110.010
0.606:10.003
0916::0.015
CARZero
0.57810.022
0.66010.055
0.60910.005
0,536.00037
0.928-0.005
0,98510.001
Comtilit
0.667-0.031
0.776-41014
0.632+0.005
0.618+00018
0.939-0.011
0.973+0.002
SphericalCNN
0.672::0.055
0.719:0.053
0.626:10.011
0.592:0.024
0.925
0.958:00.11
Spherical U-Net
X
0.682=0.049
0.732=00.344
0.22900.012
0.580=0
0933-=0.010
0.969
WSSADN
0.721
0.73910.003
0.62110.012
0.595
0.935
0.975
NeuroExphaine
0.629.10.058
0.792.10.016
0.641000111
0.627.10.023
0.753.0.001
0.827.10.001
(c)
SurfaceVistonnmmnttmmer
X
0.623-00039
0653-+0.061
0,582+0.015
0.545-0.025
0831-00.046
+0,01
CLIP
0.614200003
0.694:0.026
0.643:000233
0.653=0.007
0.956=0.005
0.985
ACLIP
0.62910.025
0.61410.016
0.59210.062
0.621100033
0911::0.016
0.986.10.007
DetaiCCLPP
0.696=00055
0.732-00.04
0.61400.022
0.616+00066
0.98-00.011
0.989=0.001
CARZaro
0.656:00047
0.78:10.022
0.607:10005
0,526:10.033
0.41::0.017
0,967:10.01
Comtilife
0.739-0.033
0.799-40073
0.651+00010
0.657-0.017
0.972-0.001
0.991
4.4 ENCODER FINB-TUNING ON DOWNSTREAM TASKS
We further evaluate the fine-tuning performance of CortiLife using disease classification as a case study. Experiments are conducted across different age groups (infants, adolescents, adult and elderly subjects) and morphological modalities (cortical thickness, mean curvature, and sulcal depth). We compare framework against nine state-of- the-art baselines. Among them, five are non non-pretrained methods, including Spherical CNN iZhao et al. (0019). Spherical U-Net /Zhao et al. (2019). WS- SADN Xue al. (0224), Surface Vision Transformer Dahan et al. (2022), and Neurooxplaaine Xue itt al. (2023); while four are pretrained + fine-tuning methods, including M Monsefi al. (2024), and CARZero Lai et al. (2024). All methods were implemented under the same experimental setup as ours for a fair comparison. Re- sults for CHD, ADHD, and AD diagnosis are given in Table and results for ASD diagnosis are shown in Table in the Appendix.
et al. (2021).
ACLIP Yang al. (2023), DetailCLIP
Cortical Thickness. Table (a) reports the results of disease classification based on CT. As shown, CortiLife consistently outperforms all competing methods across datasets and tasks. It achieves improvements of 1.4%, 1.6%, and 0.2% in classification accuracy over the strongest baselines, re- spectively.
Mean Curvature. bbble presents the disease classification results based on MC. As shown, our proposed method achieves superior performance, reaching accuracies of 66.7%, 77.6%, and 93.9% on the CHD, ADHD, and ADNI datasets, respectively.
Sulcal Depth. Table shows the results of disease classification based on SD. As shown, com- pared to the second-best model, CortiLife achieves an improvement 1.8%, 0.7%, and 1.6% in accuracy, with showing the best AUCs for 79.9%, 65.7% and 99.1% % in CHD, ADHD, and ADNI dataset, respectively.
The results demonstrate that model achieves state-of-the art performance across all lifespan stages and modalities, highlighting its superior generalization capability across both the entire lifes- pan and cortical modalities.

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Published as a conference paper at ICLR 2026
Table 4: Maximum Mean Discrepancy before vs. after patch-wise distribution encoding.
Maximum Mean Discrepancy
Condition
1-3 (ys) va 6-66 (ys)
6-64 (ys) vs 55-95 (ys)
1-3 (ys) vs 55-95 (ys)
Before scale-adaptive encoder After scale-adaptive encoder
0.852
0.592
1,029
0.473
0.271
0.933
Table 5: Results of ablation study in brain disorder diagnosis task.
CT
MC CHD
SD
local
global
statistical
ACC
AUC
ACC
AUC
ACC
AUC
0.738=00455
0810:00046
0.64300021
0.67719.024
0.703:20.025
0,694-10,046
0.740-0012
0.798=0.017
0,509+0:122
0.61++0:244
0.718=0.034
0.740=0.002
0,792-0034
0.211500024
0.622::0022
0.71840.411
0.14410.057
0.730
0.806_0.001
0.026
0.66710.031
0.776100014
0.73910.003
0.799.0,003
X
0.633=0.051
0.661=00010
0.617=00.11
0.604:0:022
0.598=0.032
0.595=0.0199
0.659_00024
0.67610.018
0.62710.011
0.65110.008
0.60110.013
0.578.10.016
0615=00008
0.600000017
0.589100221
0.61:00017
0.588:0.032
0.585:0.045
0.697=0.015
0.730=0.007
0.632=0.005
0.618=4.018
0.651=0.010
0.657=00.17
0.918=0.002
0.978=00005
0.928=00007
0.900+0.005
0:63==0.007
0:90000000
0.925
0.975
0.918:10.020
0.972:1.004
0.955:10.012
0.977.0.004
0.910-00.08
0.96010.003
0,915100017
0.96310.00M
0,9511000010
0,98010.002
0.928
0.981-0.001
0.939-0.011
0,973-40002
0.972-40002
0,991-00.002
4.5 VALIDATION OF PATCH-WISE DISTRIBUTION ENCODING
To further assess the scale-adaptive encoder, we compared feature discrepancies across age groups before and after applying it, using Maximum Mean Discrepancy (MMD) as a metric (Table The consistently lower MMD values indicate reduced distributional shifts between age groups, showing that the encoder better aligns patch-wise feature distributions across the lifespan and yields more stable cortical representations. We also visualize the embedding distributions in Figure [ in the Appendix. Finally, to verify that the benefits of scale-adaptive encoding are not simply due to adding statistical features, replaced it with several alternative encoders and evaluated them the ADHD classification task, with results reported in Section A.3
4.6 ABLATION STUDY
We designed experiments to evaluate the effectiveness of different-levell encoders within the pro- posed surface tokenizer. The evaluation was conducted under a pretraining + fine-tuning setting across classification tasks involving three cortical modalities and multiple age groups. The exper- imental setup and evaluation metrics follow the same settings as in Section 4.2] The results are presented in Table 5 Additionally, mance decline, highlighting the effectiveness of each component. Notably, in the ADHD and ADNI classification tasks, excluding the statistical-level encoder resulted in pronounced drop accuracy. This can be attributed to the loss of regional statistical features, which impairs the model's 's ability to jointly capture heterogeneous cortical morphological characteristies, thereby degrading the quality of the learned representations.
observed that removing any encoder level leads to a perfor-
5 CONCLUSION
We proposed CortiLife, the first unified framework for lifespan ceeessan-consistet cortical surface modeling. By introducing a multi-level Surface Tokenizer, our approach addresses three central challenges in cortical analysis: complex geometry of cortical surfaces, registration-induced cortical homogeniza- tion and lifespan-related heterogeneity. Experimental evaluations across multiple datasets show that CortiLife consistently outperforms existing methods brain disorder classification, age prediction and cortical surface parcellation. Most importantly, CortiLife provides a unified framework that exhibits strong generalization across the entire lifespan, diverse modalities, and multiple tasks.
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ACKNOWLEDGMENTS
This research was supported by the National Natural Science Foundation of China(62476129) and the STI 2030-Major Projects (2022ZD0209000)
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A APPENDIX
A.1 CODE AVAILABILITY
Our codes are available in the link below.
A.2 DATASETS
A.2.1 DHCP
total of 887 participants (478 males and 409 females) were enrolled in the Developing Hu- man Connectome Project (DHCP), recruited at single research site; the imaging hardware in- cluded Philips scanners, predominantly operating at magnetic field strength of 3 T, with rep- resentative model such the Philips Achieva Т. For structural imaging. T2-weighted (T2w) and inversion-recone recovery -l-weihtted (TIw) multi-slice Fast Spin-Echo (FSE) images were each ac- quired in sagittal and axial slice stacks, with an in in-plane resolution of 0.80 0.80 mm² and 1.60 mm slices overlapped by 0.80 mm (except for Tlw sagittal, which used 0.74 mm overlap); key se- quence parameters were as follows: T2w, TR/TE 12000/156 ms with SENSE factors 2.11 (axial) and 2.60 (sagittal); Tlw (IR-FSE), TR/TI/TE 4795/1740/8.1 ms with SENSE factors 2.27 (ax- ial) and 2.66 (sagittal). In addition, 3D MPRAGE images were acquired with a native resolu- tion of approximately 0.80 mm²; typical parameters were TR/TI/TE 11/1400/4.6 ms with a SENSE factor of 1.2 (right-left). Regarding image processing, the official DHCP struc- tural pipeline and surface reconstruction results were adopted in this study, with additional pro- cessing performed; for additional details, please refer to https:t///// www. www.oiiiiiiiioo. iersiinoring/////eeee.......r journa l 8 www.:euuurccccooooo encieeneeeean /ttp//rtiic.. warriccle/iiie /sss100 100 3389 fni 2022. 88 w672fful
A.2.2 CHD
A total of 229 participants (123 males and 106 females) were enrolled in the Congenital Heart Disease (CHD) study, all recruited from a single research site. The imaging hardware consisted of Philips scanners, predominantly operating at magnetic field strength of T. with the Philips Ingenia 3 T as the representative model. For structural imaging, the 3D T1-TFE sequence was utilized, with typical native resolution of approximately 0.50x 0.50x mm3 The key sequence parameters were set as follows: TR 7.90 ms, TE 3.50 ms, and FA 8 The image processing workflow was as follows: N3 intensity inhomogeneity correction, multi-atlas skull stripping, and tissue segmentation via multi-atlas label fusion; additionally, topological repair and reconstruction of the inner and outer cortical surfaces were performed. Subsequently, the spherical surfaces were aligned to the UNC 4D neonatal/infant cortical template, and surface parcellation was completed in accordance with the Desikan Desikan-Killiany Killiany atlas. Quality control (QC) checks were performed, and only data that passed QC were retained for subsequent analyses.
A.2.3 CBCP
A total of 252 participants (133 males and 119 females) were enrolled in the Chinese baby connec- tome CBC)) The imaging hardware consisted of Philips scanners, predominantly operating magnetic field strength of 3 T, with the Philips Ingenia as the representative model. For structural imaging, the 3D T1-TFE sequence was utilized, with a typical native resolution of ap- proximately 1.8000.800x.000 mm3 The key sequence parameters were set as follows: TR 6.50 ms, TE 2.30 ms, and FA 8 The image processing workflow was follows: N3 intensity inhomo- geneity correction, multi-atlas skull stripping, and tissue segmentation via multi-atlas label fusion; additionally, topological repair and reconstruction of the inner and outer cortical surfaces were per- formed. Subsequently, the spherical surfaces were aligned to the UNC 4D neonatal/infant cortical template, and surface parcellation completed in accordance with the Desikan Desikan-Killiany atlas. Quality control (QC) checks were performed, and only data that passed QC were retained for sub- sequent analyses.
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A.2.4 CCNP
A total of 559 participants (304 males and 255 females) were enrolled in the Chinese Color Nest Pro))) the imaging hardware comprised Siemens scanners, predominantly operating at a magnetic field strength of with the Siemens Tim Trio 3 T as the representative model. For structural imaging, the MPRAGE sequence was utilized, with typical native resolution of approximately 1.00x x.00 1.00 mm The key sequence parameters were set as follows: TR 2600 ms, TE 3.02 ms, TI 900 ms, and FA 8 For image processing, cortical reconstruction and segmentation were performed using the recon-all 1 pipeline of FreeSurfer 7 (FS7) with default parameters, and only data that passed quality control were retained for subsequent analy- ses; for additional details, please refer ottpec////attts::ooooooooo . cn c81 10e
A.2.5 ADHD-200
A total of 972 participants (599 males and 373 females) with Attention Deficit Hyperactivity Disorder-200 (ADHD-200 dataset) were enrolled in the study, recruited from 8 research sites; the imaging hardware included Siemens and Philips scanners, predominantly operating at a magnetic field strength of 1.5-3 T, with representative models such as the Siemens Tim Trio T, Philips 3 T, and Siemens Avanto 1.5 T. For structural imaging, the MPRAGE sequence was utilized, with typical native resolutions of approximately 1.00x 1.00 1.00 mm and 1.30 1.00x 1.30 mm² and key sequence parameters as follows: TR 2100-3500 ms, TE 2.95-3.70 ms, TI 900-1100 ms, and FA 7-10P. For image processing, cortical reconstruction and segmentation were performed us- ing the recon-all pipeline of FreeSurfer 7 (FS7) with default parameters, and only data that passed quality control were retained for subsequent analyses; for additional details, please refer to https: http:/://ppmmddddee ncbi. ml l.m nih. gtw.gooo.2252222
A.2.6 ABIDE II
A total of 1,114 participants (856 males and 258 females) were enrolled in the Autism Brain Imaging Data Exchange (ABIDE II) study, recruited from 19 research sites; the imaging hardware included scanners from Philips, General Electric (GE), and Siemens, predominantly operating at a magnetic field strength of 1.5-3 T, with representative models such as the Siemens Tim Trio 3 T, GE MR750, and Philips Achieva 3 T. For structural imaging, the primary sequences utilized were MPRAGE, 3D FFE, and FSPGR, with typical native resolutions of approximately 1.00 1. 1.00 1.30 mm and 1.30x x.00 1.30 mm the key sequence parameters ranged as follows: TR 2500-3000 ms, TE 2.30-8.30 ms, TI 553-1100 ms, and FA 7-10%. Regarding image processing, cortical reconstruc- tion and segmentation were performed using the recon-al pipeline of FreeSurfer 7 (FS7) with default parameters, and only data that passed quality control were retained for subsequent analyses; for additional details, please refer to https:: /www///wwww///// nature. com/ wtt.o///artiiceeemmm111111
A.2.7 ABIDE I
A total of 1,112 participants (948 males and 164 females) were enrolled in the Autism Brain Imaging Data Exchange (ABIDE 1) study, recruited from 20 research sites; the imaging hardware included scanners from Siemens. Philips, and General Electric (GE), predominantly operating at a magnetic field strength of T. with representative models such as the Siemens Tim Trio T. Siemens Al- legra 3 T, and GE Signa. For structural imaging, the primary sequences utilized were MPRAGE, 3D FFE, and FSPGR, with typical native resolutions of approximately 1.00 1.00x 1 1.00 mm3 and 1.00x 1.00 1.20 mm3 the key sequence parameters ranged as follows: TR 1230-2530 ms, TE 1.73-4.60 ms, TI 624-1100 ms, and FA 7-10º. Regarding image processing, cortical reconstruc- tion and segmentation were performed using the recon all pipeline of FreeSurfer 7 (FS7) with default parameters, and only data that passed quality control were retained for subsequent analyses; for additional details, please refer https: wwwww//www.// nature. com/ articcessmpp21333
A.2.8 HCP
A total of 1,206 participants (550 males and 656 females) were enrolled in the Human Connectome Project (HCP), recruited at single research site; the imaging hardware included Siemens scanners, predominantly operating at magnetic field strength of 3 T, with representative model such as the
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Table 6: Comparison of different distribution-eeccdiing methods ADHDD-20 dataset.
CT
MC
SD
Setting
ACC
AUC
ACC
AUC
ACC
AUC
Directly concatenation
0.670=0.034
0.681=0.022
0.604=0.015
0.603=0.008
0.628=0.00
0.619=0.010
BatchNomm-Lines
0.645=0.049
0.66440.022
0.597=0.021
0.595=0.057
0.591=0.039
0.603=0.037
LayerNom==L.inel
0.626#0.018
0.629#0.043
0.611
0.599=0.045
0.622+0.033
0.61840.034
Ours
0.697=0.015
0.730=0.007
0.632:0.005
0.618=0.018
0.651=0.010
0.657=0.017
Siemens Skyra T. The common native resolution was approximately 8.70x0.70x0.... mm', and
the typical parameters were: TR 2400 ms. TE 2. 14 ms. TI 1000 ms, and FA 8 Regarding image processing, the Minimal Preprocessing Pipelines (HCP-MPP) provided by the HCP and FreeSurfer reconstruction results were adopted, with no additional processing performed on this basis; for ad- ditional details, please refer to hnttps://pubree ncbi nlm. nih gcv/g266897///////
A.2.9 ADNI
A total of 7,597 participants (4.185 males and 3,412 females) were enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, recruited from 62 research sites; a mixed range of imaging devices was utilized, with representative models including 1. 1.5-3 T scanners from Siemens, General Electric (GE), and Philips that are compatible with Magnetic Resonance Imaging (MRI). For structural imaging, the ADNI-specific MPRAGE sequence was the primary choice, with typ- ical native resolution of approximately 1.20 1.20x 1.20 mm For image processing, cortical re- construction and segmentation were performed using the recon-all pipeline of FreeSurfer (FS7) with default parameters, and only data that passed quality control were retained for sub- sequent analyses; for additional details, please refer to https::/// :/op::///n// inee librarrrrr...........rr Sbrrayywwllyy. com/doi /ful 1/10. 1002/ (002///r.... 221049
A.3 EFFECTIVENESS VALIDATION OF PATCH-WISH DISTRIBUTION ENCODING
A.3.1 COMPARISON WITH STATISTICAL-FRATII INTEGRATION METHODS
To verify that the effectiveness of the acale-adaptive encoding does not simply arise from adding statistical features, we replaced the scale-adaptive encoder with some baselines and evaluated it on the ADHD classification task. The baselines include: 1) directly concatena- tion(i.e. concat(mes mean, std]); (LLyyrroomm(conca(/in Norm(concaa([meaa std])+inearr); 3) (onteaNooomcoonammmeaaaa std]) with learnable weights(i.e., BatchNorm (mean, std))+Linear). The results are shown the Table 6) Our method still achieves the best performance. Notably, introducing LayerNorm leads to clear degradation. The primary reason is that LayerNorm forces the statistics within each sample to lie on a similar scale, thereby suppressing the genuine distributional differences across samples and retaining only the rel- ative within-sample relationships. In contrast, when performing weighting across multiple scales, our adaptiv encoder adopts an adaptive weighting scheme based on the original distribution. It treats each patch's ' own statistics as the principal component and uses statistics from other scales as auxiliary cues. This design preserves key distributional characteristies and avoids the issue where normalization operations inadvertently remove meaningful lifespan-relevant variability.
std]) with learnable weights(i.e., Layer-
A.3.2 VISUALIZATION OF FEATURE DISTRIBUTIONS
We provide visual comparisons of the feature distributions before and after applying the scale- adaptive encoding, as shown in Figure After incorporating the scale-adaptive encoder, the feature distributions across different age ranges and modalities become substantially more aligned, demon- strating that the proposed design effectively mitigates distribution shifts arising from both age and modality differences.
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Published as a conference paper at ICLR 2026
Before encoding
Before encoding
Before encoding
5
MC
(ys))
6-64
55-95 (yrs)
1-3 (yrs)
(yrs)
55-15
1-3 (ys)
55-95
After encoding
After encoding
After encoding
14
5
MC
1-3 lyn))
646 (yr)
55-95 (yss)
1-3 1-30099)
6-64 (ya)
55-95 (yrs)
1-3 (yrs)
6-64 (yrs)
55-05 (yr)
Figure 5: Feature distribution visualization before and after scale-adaptive encoding across age groups.
A.4 EFFECTIVENESS VALIDATION OF TEACHER-STUDEN ARCHITECTURE
A.4.1 ANALYSIS OF MASK RATIO
We conducted the experiments to determine the mask ratio using the ADHD-200 dataset as an exam- ple. As shown in Table the masking ratio has clear influence on model performance across CT, MC, and SD. Among all the tested settings, the ratio of 0.75 consistently achieves the best results, yielding the highest ACC and AUC in CT (0.697/0.730) and SD (0.651/0.657), and the best ACC in MC (0.632). Although a ratio of 0.9 slightly improves the AUC for MC, it leads to noticeable drop ACC, indicating an imbalanced trade-off. Overall, 0.75 provides the most stable and superior performance across all modalities, confirming it the optimal masking ratio for our model.
A.4.2 COMPARISON WITH RANDOM MASKING STRATEGY
To assess the contribution of the teacher-student architecture representation learning, ducted an ablation study in which the teacher-student mechanism was removed and replaced with random-masking strategy. We performed experiments the ADHD-200 dataset across three cor- tical modalities (CT, MC, and SD), and the results are presented in Table g As shown, removing the teacher-student architecture leads consistent degradation in accuracy and AUC across all modalities. Additionally, as the random mask ratio increases, the model performance on all met- rics drops sharply. indicating that under high masking ratios a purely random strategy struggles to retain patches that are truly related to developmental semantics, which in turn leads to clear degradation in the quality of the pre-trained representations. In contrast, when we adjust the mask ratio within the same range under the teacher-student architecture, although the performance shows some fluctuations, the overall decline much smaller. This observation further suggests that, com- pared with relying only random masking, the teacher-student architecture more robustly learn high-quality representations aligned with developmental semantics under high masking conditions, thanks to the semantic guidance strategy introduced in framework.
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Published as a conference paper at ICLR 2026
Table 7: Selection of mask ratio using
ADHD-200 dataset example.
CT
MC
SD
Mask ratio
ACC
AUC
ACC
AUC
ACC
AUC
0.5
00.81
0.697=0.031
0.621=0.018
0.632=0.034
0.631
0.648#0.029
0.683=0.028
0.706=0.011
0.630=0.009
0.619#0.025
0.617#0.021
0.607+0.01
0.8
0.67140.034
0.708=0.053
0.628+0.008
0.658+0.010
0.628+0.030
0.604#0.046
0.9
0.667=0.027
0.713=0.011
0.618=0.037
0.687=0.012
0.598+0.018
0.609+0.036
0.75(Ours)
0.697=0.015
0.730=0.007
0.632#0.005
0.618
0.651=0.010
0.657=0.017
Table 8: Comparison of model performance with and without teacher-student architecture ADHD-200 datset.
CT
MC
SD
Strategy
Ratio
ACC
AUC
ACC
AUC
ACC
AUC
0.75
0.680=0.022
0.677=0.022
0.622=0.002
0.63420.016
0.621=0.008
0.620=0.00 022
0.8
0.655=0.036
0.643=0.028
0.62420.025
0.572=0.057
0.630#0.021
0.58820.000 0099
Random Masking
0.85
0.65110.021
0.63840.02
0.617+0.021
0.610#0.015
0.575+0.021
0.536a0.00 016
0.9
0.631+0.009
0.630=0.035
0.619=0.018
0.614+0.021
0.527+0.071
0.568#0.00
0.6
0.683#0.028
0.706:20011
0.630=0.009
0.619#0.025
0.61740.021
0.607=0.014
Teacher-student
0.8
0..03
0.708
0.628=0.008
0.658+0.010
0.62820.030
0.60420,046
0.9
0.667=0.027
0.713=0.011
0.618=0.037
0.687=0.012
0.598+0.018
0.609=0.036
0,75
0.697=0.015
0.730=0.007
0.632=0.005
0.618+0.018
0.651+0.010
0.657=0.017
A.4.3 VISUALIZATION OF REGIONS FROM MASKED SELF-DISTILLATIO FRAMEWORK
We visualize the visible patches at different age stages selected by the masked self-distillation frame- work. The results are shown the Figure 6 The visualization demonstrates that, in infancy and early childhood, key brain developmental regions mainly appear in the superior frontal gyrus, pre- central, paracentral, and other motor-related brain regions, as well as some visual regions including the lateral occipital; adolescence, the core regions that mainly selected are distributed in the prefrontal and temporal lobes; in adulthood, the main regions show distribution pattern similar that in adolescence; and in the elderly stage, we do observe relatively concentrated regions corresponding to aging, which suggests that there may already exist global aging phenomenon elderly individuals. The above visualization results indicate that teacher-guided masking indeed focuses more on cortical regions that are closely related to developmental semantics.
A.5 FEW-SHOT PERFORMANCE EVALUATION ON BRAIN DISORDER DIAGNOSIS
To assess few-shot performance, we trained CortiLife with only 20% and 40% of the training data, reserving the rest for testing. As shown in Table CortiLife outperforms baselines even with limited data. For example, on CHD with mean curvature features, it achieved 65.1% accuracy using 40% of the data, surpassing all basclines; on ADNI with sulcal depth, it reached 95.4%. nearly matching the second-best model (95.6%). These results highlight the strong generalization ability of Cortilife under data-scarce conditions.
Table 9: Evaluation of few-shot learning performance of CortiLife on brain disease classification tasks.
CHD MC
Methods
Training Percentage
CT
SD
ACC
AUC
ACC
ACC
AUC
0.2
0.626-10.005
0.602+-00023
0.631-40.036
0.632-0.040
0.63140.019
0.683+00016
CortiLife
0.4
0.701=10.008
0.693110018
0.65110.010
0.703-0.011
0.65411.023
0.702-10.036
0.8
0.806:00001
0.823+0.026
0.667420031
0.776=0.014
0.739=0.033
0.799-10.073
ADHD
0.2
0.60140:007
0.606-00002
0.60810.001
0.561=0006
0.604200004
0.605:40:002
Comillife
0.4
0.641+0.013
0.667
0.623++0.010
0.628-0.009
0.618+10.015
0.611+0.018
0.8
0.697440015
0.730-10.007
0.632-+0.005
0.618=00188
0.651+00010
0.657400017
AD
0.2
0.856+40.004
0.931+00001
+0.01-
0.947-0.001
0.86+00003
0.926-F0.001
ConiLife
0.4
0.89410.002
0.961110.044
0.92410.010
0,971=0.002
0.954110.011
0.988:10.002
0.8
0.928+10.022
0.981-100001
0.93910.011
0.973=0.002
0.972=40.002
0.991100002
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Published as a conference paper at ICLR 2026
Infancy (21 month)
Adolescence (14 years)
Adult (28 years)
Elder (78 years)
Figure 6: This image illustrates the visible patches selected by masked self-distillation framework. The process selectively eliminates less relevant tokens, retaining only those with strong semantic significance.
Table 10: Results of brain disorder diagnosis on ABIDE I dataset.
ASD MC
Method
CT
SD
ACC
AUC
ACC
AUC
ACC
AUC
SphericalCNN
0.563+00.16
0.582=0.020
0.57240.008
0.572=0.007
0.574+0.022
0.564=0.035
Spherical U-Net
0.561
0,553+0.00
0.56440.003
0,573=0.034
0.571+0.009
0.577#0.00
WSSADN
0.566#0.021
0.573=0.021
0,564+0.010
0.547=0.026
0.560+0.025
0.567#0.006
NeuroExplainer
0.55840.019
0.577=0.012
0.571+0.007
0.574=0.016
0.554+0.007
0.567#0.002
Surface Tranoner
0.552+0.027
0.547=0.024
0.561+0.011
0.542=0.031
0.542+0.032
0.528=0.017
CLIP
0.572
0.570=0.017
0.573+0.007
0.571=0.003
0.593+0.007
0.592=0.011
ACLIP
0.569+0.027
0.575=0.021
0.573+0.015
0.570=0.008
0.56940.005
0.557+0.028
DetailCLIP
0.574+0.012
0.581#0.016
0.541#0.012
0.539=0.017
0.604+0.022
0.604+0.027
CARZero
0.577+0.003
0.58420.017
0.568+0.005
0.568=0.012
0.599:0.015
0.597=0.008
Cortilife
0.587+0.004
0.574=0.009
0.584+0.006
0.57620.015
0.623+0.010
0.63620.025
A.6 COMPARISON WITH SOTA METHODS ON ABIDE DATASET
Table I0 summarizes the ASD classification performance on the ABIDE dataset across three cor- tical modalities (CT, MC, and SD). Overall, CortiLife achieves the most competitive results among all comparison methods, demonstrating consistently strong performance across both ACC and AUC metrics.
For the CT modality, CortiLife attains the highest accuracy (0.587+0.004) outperforming all base- lines, while maintaining comparable AUC the best-perf model. For the MC modality, CortiLife achieves the best accuracy (0.584+0.006) and also records one of the strongest AUC values (0.576+00.015) indicating that the learned representations capture more discriminative curvature- related patterns associated with ASD. Notably, on the SD modality, CortiLife delivers the best per- formance on both metrics, with an accuracy 0.623+0.010 and AUC of 0.636+0.025. signifi- cantly surpassing all competing approaches. These results highlight the robustness and generaliza- tion ability of CortiLife across heterogeneous cortical attributes.
We further conducted classification experiments on three age ranges in ABIDE I: 6-18 years, 18- 30 years, and 30-55 years, with the results shown in the Table [1] We found that our model still exhibited consistent and strong performance across all age-stratified stages. In the 6-18, 18-30, and 30-55 age ranges, the ACC reached up to 0.612, 0.694, and 0.778, respectively, and the AUC reached up to 0.608, 0.768, and 0.773, respectively. It is worth noting that some age ranges include relatively few subjects, so performance on these stratified subsets may not fully reflect the overall accuracy the full dataset. Even under this data limited setting, CortiLife still shows consistently superior performance across all age ranges, indicating strong generalization ability within different age groups
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Published as a conference paper at ICLR 2026
Table 11: Results of brain disorder diagnosis age-stratified ABIDE dataset.
6-18 years
18-30 years
30-55 years
Method
ACC
AUC
ACC
AUC
ACC
AUC
SphericalCNN
0.593400014
0.603=0.019
0.629#0.057
0.631=0.047
0.74240.073
0.58340.169
Spherical U-NET
0.588+0.01
0.576=0.002
0.649=0.015
0.591=0.027
0.656+0.014
0.629=0,147
WSSADN
0.593+0.008
0.579=0.014
0.657=0.052
0.661=0.062
0.701+0.043
0.673=0.0558
NeproExp
0.593+0.015
0.602+0.023
0.638+0.039
0.661#0.045
0.704+0.088
0.699
Surface Vision Transfomrrrr
0.577=0.025
0.580=0.005
0.646#0.041
0.602=0.077
0.721+0.078
0.679=0.11
CLIP
0.58620.016
0.58
0.639#0.063
0.601=0.027
0..11
0.728=0.085
ACLIP
0.586+0.043
0.589=0.023
0.652+0.064
0.640=0.076
0.758+0.045
0.684 14
DetailCLIP
+0.012
0.604=0.034
0.659#0.042
0.707=0.017
0.757+0.040
0.730=0.095
CARZero
0.602+0.018
0.606=0.012
0.662#0.033
0.712=0.052
0.744+0.045
0.728+0.083
Cortilife
0.61240.008
0.608=0.011
0.694+0.032
0.76820.043
0.778+0.033
0.773 098
Table 12: Comparison of model performance with and without metadata language prompting on ADHD-200 dataset.
CT
MC
SD
Setting
ACC
AUC
ACC
AUC
ACC
AUC
w/o LOLP
0.630=00.005
0.603+0.008
0.585#0.011 0.609#0.013
0.605#0.013
0.566
w/ LOLIP
0.697#0.015
0.730+0.007
0.632+0.005
0.618#0.018
0.651#0.010
0.657=0.017
A.7 EFFECTIVENESS VALIDATION OF METADATA LANGUAGE PROMPTING
We conducted the experiments validate the effectiveness of the LCUp loss using the ADHD-200 dataset as an example. We removed this loss term(i.e., disabling metadata language prompting) to examine its role in guiding the vision encoder learn lifespan-aware representations. As shown in the Table [2] eliminating LCLi leads to substantial drops across all evaluation metrics, underscor- ing the critical importance of metadata language prompting for achieving semantically aligned and developmentally informative cortical embeddings.
Additionally, we conducted the experiments to validate the effectiveness of prompts(i.e., age and sex) using the ADHD-200 dataset as an example. As shown in Table 13] removing either the age prompt or the sex prompt leads consistent drops in accuracy and AUC across all three modalities. The degradation is more pronounced when the age prompt is removed, further underscoring its critical role in enabling the model to learn lifespan-aware information. In comparison, the sex prompt has a smaller yet complementary effect, providing additional developmental cues that further refine the learned reprexentations.
Table 13: Results of disease classification ADHD- 200
dataset in different architecture.
Scale-adaptive encoder
Age promeeing
Sex prompeing
ACC
MC
SD
AUC
X
0.604e0.005
0.621+0.002
0.59540.015
0.60660.166
0,59150.007
0.602=0.002
0.660s0.013
0.67920.014
0.60110.000
0.60510.008
0.614100009
0.56510.024
0.687:0.005
0,703:0.006
0.621100199
0.662:0.021
0.62910:036
0.635x0.344
0.69720.015
0.73010.007
0.63210.005
0.651:0.010
0.65720.017
20


Reviews:
[{"review_id": "zB5iSJoj4n", "reviewer": "Reviewer_gb7Z", "summary": "The authors present a method called CortiLife, a unified vision-language framework for lifespan-aware cortical representation learning. They design a surface tokenizer to capture local topology, global interactions, and patch-wise distributional patterns. During training, CortiLife uses masked self-distillation to avoid substantial information redundancy and metadata language prompting to embed extra information such as age, sex and so on.", "questions": "Three encoding types in Figure 2 can be drawn more specifically to facilitate readers' understanding", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The method can handle data from any lifespan.\n- The authors build a surface tokenizer and training framework tailored to cortical data.\n- The method outperforms existing methods, indicating potential practical application value.", "weaknesses": "- In Table 2, CARZero’s DICE performance appears surprisingly poor—can authors provide an explanation?\n- The “zero-shot” experiments seem to require additional training; doesn’t this resemble “pretraining + linear probe” rather than conventional zero-shot generalization?\n- The authors could consider to add more experiments on ablation study—for example, varying the masking ratio and whether to include $L_{CLIP}$—to more comprehensively show each module’s marginal contribution.", "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": "xWRdsXgymS", "reviewer": "Reviewer_o3ur", "summary": "This paper presents a framework for extracting information from cortical mesh data. Among the information, there is the reconstruction of the cortical surface and metadata about the subject. The experiments are done using several classical datasets in neuroscience.", "questions": "See the weakness section for the questions.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "The framework extract much information from surface cortical mesh with very important metadata. The text is clear and the model well presented. The experiments show interesting results. While the model is not always the best, it is the most reliable. The potential impact of such in the neuroscience community is great since the cortical surface important information about the health of the subject.", "weaknesses": "I see several weaknesses in such paper.\n\n- There exists strong fairness issues with cortical data, especially on the gender. Do such biases affect the model?\n- I am not sure of the impact of such in the ICLR community. While such framework can have a large audience in the neuroscience community, it is not clear that the classical machine learning community will be intereted. Especially there exists now several frameworks of this kind in medical sciences (see [1]).\n- Extracting the age and gender using a LLM seems a little overkill and misleading. Why not using a regression model?\n- Similar frameworks have been proposed for other kind of medical imaging (for example, see [1, 2, 3]). What is the position of this model compared to these in terms of capabilities?\n\n### References\n\n- [1] Khan, W., Leem, S., See, K. B., Wong, J. K., Zhang, S., & Fang, R. (2025). A comprehensive survey of foundation models in medicine. IEEE Reviews in Biomedical Engineering.\n- [2] Lu, M. Y., Chen, B., Williamson, D. F., Chen, R. J., Liang, I., Ding, T., ... & Mahmood, F. (2024). A visual-language foundation model for computational pathology. Nature medicine, 30(3), 863-874.\n- [3] Zhang, K., Zhou, R., Adhikarla, E., Yan, Z., Liu, Y., Yu, J., ... & Sun, L. (2024). A generalist vision–language foundation model for diverse biomedical tasks. Nature Medicine, 30(11), 3129-3141.", "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": "6JqIIkdLTC", "reviewer": "Reviewer_Hj8o", "summary": "This paper addresses cortical representation learning across the lifespan, collecting an impressive dataset spanning 26 weeks to 95 years (13,928 subjects) and proposing CortiLife with multi-level surface tokenization. The experimental scope is comprehensive and results show competitive performance.\n\nHowever, I have significant concerns about the claimed contribution. The paper positions itself as \"the first lifespan-aware framework,\" but the evidence primarily demonstrates strong performance from CLIP trained on diverse age data rather than architectural innovation for cross-age generalization. Critical issues include: (1) Figure 4's circular reasoning (age/gender embeddings reflect explicit text inputs, not learned capability), (2) unclear whether baselines used the same pretraining data (unfair comparison?), (3) no direct evidence that scale-adaptive encoding addresses distribution shift vs. adding features, and (4) evaluation gaps (no adults 30-55, no per-age-group breakdown). The paper needs to clarify what specifically makes this lifespan-aware beyond data aggregation and provide ablations isolating architectural contributions. With revisions addressing these fundamental questions, this could become a solid contribution. Currently, the novelty and validity of claims are insufficient.", "questions": "**Q1.** What specific architectural components enable lifespan generalization beyond simply training on multi-age datasets? Can you provide ablation studies showing performance when age information is removed from text prompts?\n\n**Q2.** Were baseline methods (CLIP, ACLIP, DetailCLIP, CARZero) pretrained on the same 8 datasets as CortiLife? If not, how is this a fair comparison?\n\n**Q3.** Figure 4 shows embeddings clustered by attributes that were explicitly input. How does this demonstrate learned capability rather than input reflection? Can you show embeddings on data where age/gender were NOT provided as text input?\n\n**Q4.** How does the scale-adaptive encoder (Eq. 1-2) specifically address distribution shift? Can you visualize embedding distributions across age groups to show alignment?\n\n**Q5.** Why is fine-tuning only performed on 3 datasets spanning limited age ranges? What about performance on young/middle-aged adults (30-55 years)?\n\n**Q6.** Table 3 shows many baseline methods with comparable performance. For example, in Table 3c (SD), WSSADN achieves 0.935 vs your 0.972 on ADNI - but WSSADN had no pretraining. How much gain is from pretraining vs. lifespan-aware architecture?\n\n**Q7.** In zero-shot evaluation (Table 2), CARZero performs catastrophically poorly (DICE ~0.01). Was this implementation correct? This seems like an implementation error.\n\n**Q8.** What is the specific purpose of the teacher-student masked self-distillation framework? The paper employs this design masking 75% of patches, but the motivation is unclear. Is this for computational efficiency (if so, where are the speedup metrics?), or for learning better representations (if so, where is the ablation comparing single-model vs. teacher-student)? Table 4's ablation study does not include this comparison. The paper cites \"information redundancy\" in cortical data, but provides no evidence that this design specifically addresses it. This appears to be standard MAE-style masking - what makes this cortex-specific?\n\n**Q9.** Where is the computational cost analysis? If the student processes only 25% of patches during training, what is the actual speedup in training time, inference time, and memory consumption compared to processing all patches? Does this enable scaling to higher-resolution cortical meshes? Without efficiency metrics, the teacher-student design appears unmotivated - if there's no computational benefit and no representation quality benefit (per Q8), why use it?\n\n**Q10.** Why specifically 75% masking ratio? Section 2.2 states \"Patches with the lowest 75% attention values are masked out,\" but no justification or ablation study for this choice is provided. Was this borrowed from MAE (He et al., 2022) which used 75% for natural images? Cortical surfaces have fundamentally different properties - high spatial autocorrelation, fixed anatomical structure (motor cortex, visual cortex locations), and critical geometric features. Shouldn't the masking ratio be validated for this domain? Can you provide ablations testing 50%, 60%, 80%, or 90% masking to show 75% is optimal for cortical surfaces?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- Significant data collection effort (9 datasets, 13,928 subjects across lifespan)\n- Comprehensive experimental scope (multiple tasks, modalities, settings)\n- Extensive baseline comparisons\n- Generally competitive empirical performance\n- Technically sound surface patchification approach", "weaknesses": "**W1. Unclear Contribution Beyond SOTA Performance**\nThe paper claims to be \"the first unified vision-language framework for lifespan-aware cortical representation learning,\" but the actual technical novelty separating this from standard multi-dataset training is unclear. The key question remains: Is this truly lifespan-aware architecture innovation, or simply a CLIP-based model trained on diverse age datasets? The performance gaps over baselines are often marginal (e.g., Table 3a: 0.806 vs 0.792 for CHD), raising concerns about whether the architectural components specifically enable lifespan generalization or if aggregate data is the primary factor.\n\n**W2. Circular Reasoning in Figure 4**\nFigure 4 visualizes embeddings separated by age, gender, and diagnosis - but these exact attributes are explicitly provided as text inputs via PubMedBERT (Section 2.3: \"The age of the subject is [age]. The gender of the subject is [gender]...\"). This is not evidence of learned representation quality but rather a trivial reflection of the input. The paper presents this as validation of the model's capability, which is misleading.\n\n**W3. Distribution Shift Solution Lacks Validation**\nThe paper identifies distribution shifts across lifespan as a key challenge (Figure 1c) and proposes scale-adaptive encoding (Eq. 1-2) as the solution. However:\n\n- The ablation study (Table 4) only shows that removing \"statistical\" encoding reduces performance\n- No evidence that this specifically addresses distribution shift vs. simply adding more features\n- No analysis showing that embeddings from different age groups are actually aligned in the representation space\n\n**W4. Inconsistent Experimental Setup**\n\n- Baseline models' pretraining data is not specified - did they use the same 8 datasets?\n- Fine-tuning only evaluates 3 specific age groups (infancy, adolescence, elderly), missing adult ages 30-55\n- Table 1 shows HCP covers 22-36 years, but it's only used for zero-shot evaluation, not fine-tuning\n- The claim of \"across the lifespan\" is not systematically validated across all age ranges\n\n**W5. Poor Presentation Quality**\n\n- Related Work relegated to Appendix without justification (highly unusual)\n- Inconsistent terminology: \"ADHD\" (disease) vs \"ADNI\" (dataset) mixed in Table 3 headers\n- Acronyms (CHD, ADHD, ADNI) undefined until Appendix A.3, first appearing in main text without explanation\n- Figure 2 is overly complex and difficult to parse\n\n**W6. Unmotivated and Underexplored Teacher-Student Design**\nThe paper employs masked self-distillation with teacher-student framework (Section 2.2), masking 75% of patches based on attention scores. However:\n\n- **No ablation study** comparing with/without this design (Table 4 doesn't include it)\n- **No computational efficiency metrics** reported (training time, inference speed, memory)\n- **No justification for 75% masking ratio** - why not 50%, 60%, or 80%? This appears borrowed from MAE without domain-specific validation\n- The claimed motivation (\"information redundancy\") is not validated\n- Appears to be standard MAE-style masking without cortex-specific justification\n\nThis is concerning because:\n\n1. If efficiency is the goal → no speedup metrics provided\n2. If better representation is the goal → no ablation showing improvement\n3. The 75% ratio may be suboptimal for cortical surfaces where spatial anatomical structure matters", "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)