XIL: Cross-Expanding Incremental Learning
Authors: Heayoun Choi
Hyundong Jin
Eunwoo Kim
Venue: ICLR 2026 Poster (2026)
Keywords: Class-incremental learning
Continual learning
Image Classification
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Class-Incremental Learning (CIL) traditionally assumes that all tasks share a similar domain distribution, limiting its applicability in real-world scenarios where data arrive from evolving environments. 
We introduce a new problem setting, Cross-Expanding Incremental Learning (XIL), which extends CIL by requiring models to handle class-incremental data across distinct domains and to expand class-domain associations bidirectionally.
In this setting, new classes should be integrated into previously seen domains, while earlier classes are extended to newly encountered ones, a capability we refer to as bidirectional domain transferability (BiDoT).
To address XIL, we present a new framework, Semantic Expansion through Evolving Domains (XEED), which leverages domain-specialized prompts, residual-guided representation modulation, and evolving prototype embeddings to expand class semantics across previously encountered domains.
We further introduce the BiDoT Score, a novel metric for quantifying the degree of BiDoT.
Extensive experiments on benchmark datasets with significant domain shifts demonstrate that XEED outperforms existing CIL baselines by a large margin in both standard accuracy and BiDoT scores, establishing a strong foundation for realistic continual learning under domain-evolving conditions.

problem setting, Published as a conference paper at ICLR 2026
XIL: CROSS-EXPANDIN INCREMENTAL LEARNING
Heayoun Choi, Hyundong Jin, Eunwoo Kim
Chung-Ang University
heayounchoi@cau.ccm ac..boooocc.............. wisgusehdegmaill com, Sunwoo@cau.ac.
ABSTRACT
Class- Insssennremmntal Learning (CIL) traditionally assumes that all tasks share sim- ilar domain distribution, limiting its applicability in real-world scenarios where data arrive from evolving environments. We introduce Cross-Expanding Incremental Learning (XIL), which extends CIL. by requiring models to handle class-incremental data across distinct domains and to expand class-domain associations bidirectionally. In this setting, new classes should be integrated into previously seen domains, while earlier classes are extended newly encountered ones, capability we refer to bidirectional domain trans- ferability (BiDoT). To address XIL, we present new framework, Semantic Ex- pansion through Evolving Domains (XEED), which leverages comain-speccaliiie prompts, residual-guided representation modulation, and evolving prototype em- beddings to expand class semantics across previously encountered domains. We further introduce the BiDoT Score, novel metric for quantifying the degree BiDoT. Extensive experiments shifts demonstrate that XEED outperforms existing CIL baselines by a large mar- gin in both standard accuracy and BiDoT scores, establishing strong foundation for realistic continual learning under domain- conditions.
benchmark datasets with significant domain
INTRODUCTION
Class-incremental learning (CIL) aims to enable models to incre- mentally learn new classes while retaining previously acquired knowl- edge (Zhou et al.) (024b). To address this challenge, wide range of learn- ing strategies have been proposed (Li & Hoiem 2017; Rebuffi et al. 2017
C
Training
D
Incremental
Evelustion of current and previous tasks
Inference
Kirkpatrick et al. [2017] Zenke et al.
2017
Aljundi |l.[|22.001 20188 Yan al.
2021 Douillard et al.] 2022), and
prompt tuning approaches (Wang 2022d c Smith et al.] 2023
maass-nncrementll
Cross Dosss-Expnding Incremental Learning (XIL)
Learning (CIL)
eet al.
Figure Comparison of training and inference phases be- tween conventional class-incremental learning (CII.) and our proposed cross-expanding incremental learning (XII.). While CIL. assumes shared domain across all incremen- tal tasks, XIL considers distinct domains and requires bidi- rectional semantic expansion, extending new classes to past domains and past classes to new ones.
Gao al. 2024 et al. 2005) have
attracted increasing attention for their ability leverage pre-trained models and adapt them to new tasks with minimal computational overhead, often surpassing full fine-tuning in performance.
However, most CIL methods rely the assumption that the training and test data share simi- lar domain distribution, making them susceptible to performance degradation under domain shifts (Kundu et al. 2020). While number of approaches (Shi & Wang 2023 Kundu et al.) 2020 Simon et al. 2022] Cho et al. [2023] Kishida et al., 202T) ) have been introduced to alleviate the domain gap mismatch between incremental tasks by incorporating perspectives from domain adaptation Gannin & Lempitsky| 2015) and domain generalization (Zhou et al. (022), they remain limited in scope. In
this paper, systematically compare existing tasks and propose a novel task that has not yet been explored in the literature, together with simple baseline framework.
"Coreeponding author

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Published as a conference paper at ICLR 2026
Accuracy Gap
0-
PACS
-10-
Office 31
-30
DomainNet
40
Joint-FT
LwF
EWC S-Prompts CODA-P CPromptSimplecIL XEED
Figure 2: Gap between the accuracy after the final task in XIL and in CIL for full fine-tuning (Joint-FT), recent state-of-the-ar methods, and the proposed framework (XEED) on datasets with substantial domain shifts. A more negative gap indicates poorer generalization across domains.
Problem settings tackling the domain gap mismatch in incremental scenarios generally assume a fully or partially shared label space across domains, enabling the transfer of attributes common to classes. [Sh & Wang (2023) consider a setting where the label space remains fixed across tasks, requiring the integration of newly encountered domain knowledge over time in supervised manner. Kundu et al. (2020) study a semi-supervised scenario in which model must adapt to a test domain with few labeled examples, while Carvalho al. (2024); Weng al. 2024) consider settings that rely on unlabeled target-domain examples, [Simon et al. (2022) investigate multi multi-domain training setup where the goal is to learn invariant features that generalize to unseen test domains. Similarly, Cho et al. (2023) tackle the joint challenge of domain adaptation and domain generalization to unseen distributions. Moreover, Kishida et al. (202T) investigate scenarios with unknown classes, in which the model is tasked with detecting novel objects and simultancously adapting to known classes across domains.
In practice, data availability varies significantly across domains and classes, and there may be no data at all for learning shared attributes of classes across domains. For example, consider an industrial robot trained to recognize components using high-quality images collected in controlled factory environment. When deployed in different facilities or adapted to new tasks, the robot may need to learn additional parts from operator-provided data, such as smartphone photos, technical diagrams, or even hand-drawn sketches. In such cases, previously learned components may not appear in the new environment at all. Morcover, the robot's working environment can change fiequeetly through relocation or reverting to previous settings, requiring it to recognize previously learned components across all encountered domains, a capability that lies beyond the scope of conventional CIL settings.
Therefore, we introduce a novel problem setting, Cross- Expanding Incremental Learning (XIL). As illustrated in Figure []] XIL requires a model not only to retain previously learned knowledge as new classes emerge from different domains over time, but also to bidirectionally transfer domain knowledge across learned classes. It should generalize to previously encountered domains where direct supervision was unavailable for certain classes. We refer to this capability as bidirectional domain transferability (BiDoT), and to evaluate it, we introduce a new metric, BiDoT Score.
The central question we raise is whether current models are capable of transferring domain knowl- edge across classes when class distributions are not shared across domains. Using the accuracy gap between XIL and CIL, we empirically show that neither full fine-tuning
recent state-of of the-art methods (Wang et al.. [l.22 c Smith et al. [2023] Gao et al. 2024) exhibit inherent bidirectional do- main transferability. As shown Figure 2 performance drops significantly when the model is faced with unseen class-domain combinations, indicating that such generalization is not naturally sup- ported by current architectures or training protocols. To address this, we propose a new framework, Semantic Expansion through Evolving Domains (XEED), inspired by generative replay-based meth- ods using synthetic images. XEED leverages generative models transfer domain characteristics to class-specific attributes by exploiting semantic relationships between classes and domain features. Specifically, XEED first learns domain-spectii specialized prompts via an auxiliary classifier. then employs prototype embeddings to progressively expand the semantic representation space, en- abling bidirectional integration of domain knowledge from synthetic samples.
cemaan--spen specific
Since the XII. task is novel and no directly comparable baselines exist, we conduct experiments using the strongest and most relevant prompt-based learning methods, selected after a comprehen-
sive consideration of CIL variations (Zhou et al. 2023 Kundu et al. 20200 Simon et al. 2022 Cho
et al.
2023 Kishida et al. 2021), generative replay-based approaches (Meng et al. 2024 Zhang
et al. 2025 Wu et al. 2025). and prompt- based techniques (Wang al. 2022d c Smith al. 2023
Gao et al. 2024). Extensive experiments on datasets with substantial domain shifts demonstrate that
XEED consistently outperforms existing CII. methods in both standard accuracy and BiDoT Scores.
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Published as a conference paper at ICLR 2026
1.1 CONTRIBUTIONS
Our key contributions are follows: (1) We propose novel problem setting, Cross-Expanding Incremental Learning (XIL), which, to the best of our knowledge, is the first to extend class- incremental learning enable bidirectional domain knowledge transfer across previously learned classes (2) We propose a new framework, Semantic Expansion through Evolving Domains (XEED), which integrates newly introduced knowledge and enables bidirectional domain transfer by leverag- ing semantic relationships between classes and domains. (3) We introduce a new evaluation metric, the BiDoT Score, to measure the bidirectional domain transferabilityy (BiDoT), an aspect not ad- dressed in CIL. (4) We conduct extensive experiments and analyze the limitations of existing CIL baselines under the XIL. setting. XEED outperforms baselines with 7.1 % higher average standard accuracy, and up to 31.41% 1% higher BiDoTT scores across all datasets.
2 RELATED WORKS
2.1
GLASS-INCREMENTA LEARNING
Class-incremental learning (CIL) methodologies can be broadly categorized based on the learning
strategies they employ. Strategies such as knowledge distillation, regularization, and network ex-
pansion aim to retain prior knowledge while learning new tasks by transferring past information (Li
& Hotem| 2017 Rebuffi et al. 2017), constraining important parameters (Kirkpatrick et al. 2017
Aljundi et al. 2018), or dynamically extending the model architecture (Yan et al. 202 Douillard et al. 2022). ) Unlike such conventional approaches, prompt tuning methods leverage pre-triined
models to adapt downstream tasks with minimal resource consumption (Wang et al. 2022c Smith
ct al. 2023; Gao et al., 2024; Qu et al. 2025), achieving competitive or even superior performance much lower cost.
compared to full fine-tuning
Another common approach is to store a subset of training data as exemplars and replay them during new task learning to retain prior knowledge (Rebuffi et al., 2017] Hou al.; 2019: Wang et al., 2022a). However, growing concerns over privacy and regulations have led to a shift toward gen- erating synthetic images that resemble previous classes, rather than storing real user data (Meng) et al. 2024; Krawezyk& & Gepperth 2024] Wu et al., 2025: Zhang et al.] (2025). Inspired by these developments, our framework leverages generative models to generate synthetic cross-class domain transferred images and learns domain-specialize prompts to adapt to diverse domains.
2.2 MORE PROBLEM SETTINGS IN CIL
Recent studies (Wang et al. 2023 Shi Wang. 2023 Liu al. 2024, Ganin Lempitsky 2015
Zhou et al. 2022) have challenges core assumptions in CIL, such as tasks sharing the same do- main distribution or training and test data originating from the same domain. For settings where domain distributions vary across tasks with label space remains fixed, Liu et al. (2024) propose using batch-wise exponential moving average algorithm to adaptively mitigate classifier forgetting. Similarly. Shi & Wang (2023) introduce adaptive coefficient learning to dynamically adjust replay, cross-domain distillation, and divergence terms. Waag et al. (2023) consider significant domain gaps during training, similar to XII., but their objective is to mitigate knowledge interference across tasks by isolating task-specific knowledge. Their approach does not handle scenarios where class- domain associations change at test time, requiring generalization across both domain shifts and class boundaries. In contrast, XIL focuses on bidirectional domain knowledge transfer across tasks.
Other works (Kundu et al. 2020 Simon et al. 2022; Cho et al. 2023 Kishida al. [021] combine
CIL with domain adaptation or generalization. Kundu et al. (2020) leverage Gaussian prototype- based distribution information and guides to distinguish new classes under test-time domain shifts and adapt source-trained models. [Simon et al. (2022) use multi-source domain data to learn domain- invariant features via class-wise Mahalanobis metrics, enabling generalization to unseen domains. Cho et al.] (2023) propose a complementary framework in which a domain adaptation model pro- vides pseudo-labels for domain generalization, while the domain generalization model initializes the domain adaptation model, thereby addressing the joint challenge of domain adaptation and gener- alization. These approaches rely on strong assumptions: Kundu et al. (2020) require labeled target data for both old and new classes, and Simon et al. (0022); Cho et al. (2023) assume access to di- verse domains covering all classes. In contrast, XIL sees each class in only one source domain and must transfer knowledge bidirectionally to generalize across unseen domains.
3

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Published as a conference paper at ICLR 2026
Task [Cless Domate"
Specialization via Auxiliary Supervision
PIZZA
"Strawberry
Fealurre Extr Extractoris
Auxliary Classilier
"Pizze
Protokype
Task 1. (Clevve Drommrie'))
Prosappe Embessling
Classifler
Pon
"lookk"
Imaage Encoder
Domain Feature
'Aripane
Frotodapn
Task 1 (Cline Dumunin
Taxt
Extractor
Encoder
Loutteblle par ralless
Class Foature
Semantic Expansion Using Evolving Prototypes
Representation Modulation with Domain Semantics
Promp:
Figure 3: Overview of the XEED framework at task Prototype embedding fill colors represent classes, and outline/prompt colors represent domains. (1) During each task, domain-speciallzzed prompts are concatenated with image patches and optimized to encode domain-specifie semantics via auxiliary supervision. (2) A diffusion model generates synthetic images by disentangling and recombining class and domain semantics across tasks, enabling bidirectional domain transfer. (3) The classifier updates its class prototypes with generated data, leading to an evolving semantic space that captures richer class semanties across all seen domains. Best viewed in color.
3 PROBLEM STATEMENT
Learning (CIL) (Zhou et al. 2023). the label space C C Y, where C repre- sents subset of classes from the complete class set Y. expands incrementally as new class subsets are introduced over time, while the domain distribution D remains fixed. The training sequence is Ten. {(C*, D), D)..... D)}, where C C Y, C' n 0, Vi * j. and denotes the total number of incremental learning steps. During inference, the model is evaluated on all previ- ously learned classes within the same domain encountered during training: EcuL- (C' D).
In
While CIL assumes a fixed domain distribution during incremental class learning, real- world scenar- ios often involve evolving domains. To bridge this gap, we introduce Cross-Expanding Incremen- tal Learning (XIL), a novel setting in which model should incrementally learn new classes from evolving domains while retaining prior knowledge. In addition, this setting aims to enable bidirec- tional transfer of domain knowledge across classes, including to domains where certain classes were never directly supervised. Formally, the label space in XIL expands over time with the introduc- tion of new class subsets as in CIL. However, the domain distribution D also varies across tasks. Each domain D comprises samples drawn from a unique distribution S {Zx, yr]]==1 PXy, where IA E X is an input, ys C C Y is the corresponding label, is the number of sam- ples, and Pxy is the joint input-label distribution (Wang et al., (20226).
domain shift be- tween tasks defined as Px * pi for all + j. XIL defines sequence of tasks as: TxiL= = ( D*), (C2, D2),..., (C", DD) (C,D")) C cY,C nc = vi j-
During inference, the model is evaluated not only previously observed class-domaie pairs, but also on its ability to generalize to novel combinations, i.e., classes appearing from encountered but unsupervised domains. This evaluation defined as: EXIL = - U D(), where D D or D * D', capturing both knowledge retention and bidirectional domain transfer across tasks.
THE PROPOSED FRAMEWORK
Our framework, XEED (Semantic Expansion through Evolving Domains), tackles the challenge of bidirectional domain transfcrability in XIL by incrementally expanding class semantics across all previously encountered domains. It consists of three core components that work in concert to support semantic expansion across tasks, as illustrated in Figure 3 First, domain-specialized prompts are learned via auxiliary supervision to encode domain-specific characteristics while re- maining disentangled from class semantics. Then, synthetic images are created by modulating class representations with domain features through residual- guidddd cross-attention. Finally, prototype embeddings are continuously updated with synthetic images, allowing the semantic representation space to evolve bidirectionally, extending newly learned classes to previously encountered domains and past classes to newly encountered domains.
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Published as a conference paper at ICLR 2026
4.1 OUR FRAMEWORK: SEMANTIC EXPANSION THROUGH EVOLVING DOMAINS (XEED)
4.1.1 DOMAIN SPECIALIZATION VIA AUXILIARY SUPERVISION
In XIL, the domains of class-incrementee data continuously change over time, and the model should be able to extract meaningful features for learned classes across all previously seen domains, even without direct supervision. To address this variability, we employ an auxiliary classifier during train- ing to learn small domain- -peeiii-ppe prompts (Jia et al., 2022) that effectively capture underlying domain characteristics, facilitating adaptation across multiple domains.
Given a task (C*, D*) E T consisting of input-label pairs (X), where C' =Y C Y de- notes the label subset at timestep t and D' is the associated domain, we introduce domain domain-specific prompt PD* to inject specific domain information of the task into frozen pre-trained feature ex- tractor fa. Each image sample 2; E X is first mapped to patch embeddings Fing E R N WxD where N is the number of image patches, and D is the embedding dimension. A class token Tels E R1xD is prepended, and learnable prompt PD' E RLLDD designed to encode domain-specific information, where is the length of the prompt token, is inserted after the class token, yielding the final input:
[ecss PD* Zimel
B(++++N)
The sequence is processed by the frozen feature extractor fo, and the output at the class token position is used as the image-level representation:
h4 (z)
To ensure the prompt pD' captures domain-level rather than class-specific features, auxiliary linear classifier As is introduced during training. takes h; as input and is trained with a cross- entropy loss to predict the class within domain D During training, only the prompt PD and the classifier Ag are updated, while all parameters of the feature extractor Sa remain frozen. The training objective of the current task dataset S {(z,, 3433 ~ P is defined as:
LCE
Σ
Ys log (ba).
(S
This auxiliary supervision acts as regularizer, guiding the prompt to encode shared domain char- acteristics while avoiding class-specific semantics. As result, the prompt effectively conditions the representation space of the feature extractor based on the domain D' of the input image.
4.1.2 REPRESENTATION MODULATION WITH DOMAIN SEMANTICS
To address unsupervised domain gaps in rehearsal-free CIL, we leverage the generative capability of pre-trained diffusion model conditioned on both image and text inputs (Ye et al.) 2023). Specif- ically, we transfer domain semantics to class-specife attributes via representation modulation in training-fire free manner. To enable bidirectional domain knowledge transfer, we extract class centroids discarding the original data.
from sequential data to generate and store
Exemplar generation incorporates sequentially incoming image I; as the image conditioning input I, and the class name corresponding to label as the text conditioning input T. Both inputs are encoded using CLIP image encoder Enc, and a CLIP text encoder Encr, respectively. At task t, we define an exemplar Tex as:
Tex = (z k, (T))(TT)) Ency (1)).
(4)
where is a diffusion model parameterized by 0, = is random noise, and k denotes the total num- ber of denoising steps. Using a small & preserves high-level semantics while modifying low-level details, mitigating overfitting to the original image and enabling faster generation.
For bidirectional domain knowledge transfer from one task f to another t' * t, we first suppress class-specific semantics in 1 and retain only domain-related features. We compute a residual vector by subtracting the class embedding from the image embedding (Ye al. 2023):
5 Enc, (T:) Encr(T)))
(5)
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Published as a conference paper at ICLR 2026
which is used as the image conditioning input for generation:
Ftransfer go k. Ency (T;
(6)
To preserve the speciic semantics of T. while injecting domain features from 6j, cross- attention (Vaswani et al., 2017) is modified to incorporate 87 only specific transformer blocks responsible for layout and style (Wang al. 2024). This enables modulation of the representation only in terms of the domain semantics of T?
4.1.3 SEMANTIC EXPANSION USING EVOLVING PROTOTYPES
To continuously expand the semantic representation space the classifier using generated data, employ evolving prototype embeddings that are incrementally updated as new domain knowledge arrives. Specifically, each class C within domain D E D is represented by domain-aware prototype vector uep'. This vector computed as the mean of feature embeddings extracted by the frozen feature extractor fo. conditioned on the corresponding domain-specialized prompt:
D' Me
Σ Z.EED
folx,,P1)
where E.' denotes the evolving support set for class in domain D As new samples are synthe- sized encountered, Ep' is expanded accordingly, allowing n to dynamically reflect the updated semantic structure of each class. Given test input æ, its feature embedding / is classified by com- puting the cosine similarity with the current of prototypes:
(h,
arg max GEC ||] 2
(8)
To select the appropriate domain prompt D during inference, the test image embedding fa(x) is compared with domain prototypes JP', The prompt Ó is chosen by minimizing the distance:
arg m [a(x) - , 1P''],, Each domain prototype Di is computed by averaging the image embeddings within each class under domain D', and then taking the mean across all classes:
1
Σ
Σ fo(
(9)
CD
ED'
rECo!
3,
EED!
where Df is the set of classes in domain D
5 EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Datasets. To properly evaluate under the proposed XIL. setting, each class must observable in every domain so that tasks can introduce new class-domain combinations and unseen combinations can be assessed at test time, We used widely adopted datasets for domain adaptation (Chang et al.
2023 Dayal et al. 2025) and generalization (Jiao al. 2025 Jin et al. (02T) T). PACS Li Liet 2017)
shows large style variations across domains, while DomainNet (Peng et 2019) has strong domain
style differences and severe label shifts. Office-31 (Saenko et al. 2010) reflects environmental
differences with small within-domain variation. Classes were randomly shuffled and assigned to tasks by first distributing an equal share via integer division. The remaining classes were then sequentially allocated to tasks in task order. In PACS, leftover classes were assigned to domains other than Photo, where no prompt tuning was applied, to prevent overfitting to a single class.
Evaluation Metrics. We evaluate using continual learning accuracy metrics (Rebuffi et al.) 2017 Li Hoiem 2017) and propose the BiDoT Score to measure bidirectional domain transferability XIL. The accuracy for task is defined as: ACC
E(zg)ee 1(y y), where 8 g/3

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Published as a conference paper at ICLR 2026
Table I: Final and average performance metries (BiDoT, accuracy) for each dataset, averaged over
three runs. Best results are highlighted in bold, and second results are underlined.
PACSS
Oftic-31
DomainNer
Method Joint
F-BiDoT
A-BiDoT
Final
Avg
F-BiDoT
A-BiDoT
Final
Avg
F-BiDoT
A-BiDoT
Final
Avg
64.99
23,20
23.20
33.76
LWF
26,86
43.18
32.72
61.17
26.67
21.56
42.72
62.55
10.28
8.48
15.82
32.15
EWC
24.25
35.52
35.27
57.20
47.02
68.41
49.57
56.58
12.49
12.96
12.81
32.59
S 3.Prompes
18.85
26.90
43.48
65.91
35.60
25.00
54.81
67.35
5.24
4.92
15.05
35.69
CODA-P
24.68
46.73
37.43
69.60
67.39
58.13
75.84
80.27
29.41
25.49
36.08
49.68
CPrompe
33.78
73.90
29.71
37.26
SimpleCIL
26.56
42.89
40.51
69.01
69.06
60.93
75.45
77.12
11.94
11.10
17.00
32.25
XEED
65.19
67.77
61.86
78.51
78,08
73.67
80.72
83.19
33,63
37.57
35.30
48.40
contains all samples from tasks to and 1(-) is the indicator function.
We report average (Avg)
and final (Final) accuracy after all tasks.
In addition, we introduce the BiDoT Score to explicitly measure the new challenge of bidirectional domain transferabilityy For each task i, the BiDo'T score is defined as:
BiDoT
Σ
1 y) y).
(10)
11:: unssen
(2.9))
where the evaluation set El: DDunseen.
unseen
includes all previously learned classes tested on unseen domains
gli unseen
U U (c*,
(11)
J=
DJ Dunnces
where class C had supervision during training. We report average (A-BiDoT) and final (F- BiDoT) scores. A-BiDoT captures consistent generalization, while F-BiDoT reflects overall gener- alization unseen domains.
Baselines. Since our proposed setting, XIL, novel and lacks directly comparable methods under identical conditions, we establish the most relevant prompt- baselines. These include traditional methods such as LwF (Li & Hoiem] 2017) and EWC (Kirk- patrick al., (017), and recent state-of-the-art prompt tuning methods: S-Prompts (Wang et al.) 2022ch, which learns independent prompts per domain; CODA-Prompl (Smith et al. 2023). which uses decomposed attention conditioned prompt tuning: and CPrompt (Gao et al. 2024). which trains the current task prompt and classifier guided by all existing prompts and classifiers. We also include SimpleCIL (Zhou et al. 2024a), a prototype- based classifier approach without further training of the pretrained model. To evaluate the inherent bidirectional domain transfer capability of the model, we additionally report results from offline joint fine-tuning (Joint-FT).
prompt-based based learning methods from CIL as
Architecture. All baselines were implemented using PyTorch (Paszke et al. 2019). with a ViT- B/16 backbone (Dosovitskiy et al., 2020) pretrained on ImageNet1K (Russakovsky et al. 2015). For high-quality synthetic image generation, we utilized IP-Adapter (Ye et adl.22223 built on top of Stable Diffusion XL (SDXL) (Podell et al. 2023).
Training Details. The prompt length L was set 5, for both XEED and S-Prompts across all datasets, and the denoising step was set to 50, yielding good image quality across all datasets. Other baseline hyperparameters followed official implementations (Li & Hoiem 2017] Kirkpatrick jet al., 2017: Wang et al.) [202c;; (Smith et al.) [2023] Gao et al.) 2024). To generate images, we
extracted 5-10 10 class centroids from the training data and generated 25-30 samples per class.
5.2 EXPERIMENTAL RESULTS
We comprehensively analyzed our method, XEED, and baseline methods under the XIL. setting from multiple perspectives. Table repoorts performance unseen domains and overall accuracy, demonstrating superior generalization of XEED and the limitations of existing methods. XEED achieved the highest BiDoT scores across all datasets, with up to 31.41% % improvement over the second-best baseline on PACS, and also showed superior accuracy on PACS and Office-31. The larger BiDoT gaps in datasets with high cross-domain variation, such PACS and DomainNet, suggest that XEED effectively adapts semantics to evolving domains. Its strong standard accuracy further indicates effectiveness in both generalization and incremental learning. While CODA-P and

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Published as a conference paper at ICLR 2026
Office-31
-0
30.2
29.4
6.3
6.9
-0
11.3
16.9
56.2
-
1,4
5.6
0.4
22.1
63.1
Accuracy
CODA-P
26.5
42.0
-2.8
30.1
-21.4
20
-.99
21.2
8.7
-1111
-20
-27.1
36.6
46.0
-21.2
21.55
-13.4
24.00
CPromp
24.3
38.0
30.2
15.9
-12.2
-1.4
25.8
2.0
-0.55
-2.55
-28.0
-59.9
-2.33
29.0
-21.9
28.7
XEED
13.4
21.9
0.1
11.0
4.0
-3.1
.6.8
.9.8
2.7
-.7
-1.9
-10.1
-60
Cartson
Photo
Amazon
06LA
Webcom
Mean
Quccddewhanning
Oleart
Figure 4: Per-domain accuracy gap between XIL and CIL in the final task. "Mean" indicates the average across domains. A more negative gap implies poorer cross-domain generalization.
PACS
Office:31
DomainNet
F-BDOT
30
Mean
Alt
Cartson
Photo
Seatch
Amauson
ESLR
Vebbaam
Meen
Real
Quickdraw
Shatch Infegraph
Clipr
100
Fineel
Mean
Ast
Canloon
Photo
Statth
Mean
Amauun
DSLR
Welbram
Mean
Faxal
Quickdrow Painting
Shutch
Infogruphi
Clipri
LwF
88N
EWC
CODA-P
CPrompe
_B
XEED
Figure 5: Final BiDoT and accuracy per domain, averaged over three runs.
CPrompt focus mitigating forgetting under static distributions, they showed limited generaliza- tion across all datasets. Office-31, where in-domain variation small, prototype-bart based methods, XEED and SimpleCIL, outperformed linear classifier methods, highlighting the advantage of pro- totype representations in improving generalization. These findings underscore the need for realistic settings, such as XII,, drive progress beyond static continual learning scenarios.
To further assess the inherent ability of offline-trained Joint-FT and CII. baselines to transfer domain knowledge across tasks, evaluated the final accuracy gap between XIL and CIL in Figures and 4 As shown in Figure 2 baseline performance dropped significantly under XIL across all datasets, indicating limited generalization unsupervised class-domain associations. In contrast, XEED achieved a positive gap with improved performance on PACS and showed smaller accuracy gaps overall. Figure 4 provides a detailed domain-wing wise comparison of accuracy gaps to examine whether methods exhibit domain-specific bias in generalization. For EWC, the accuracy gap in Clipart domain of DomainNet was 52.6% below its mean, indicating strong domain-specific bias. In contrast, XEED showed only 4.8% deviation in its worst case (Infograph), suggesting much more balanced generalization. Even Office-31, CODA-P showed a 13.3% deviation in the Amazon domain, while XEED had only 2.2% deviation in DSLR, indicating more stable performance. These results reveal limited bidirectional domain transferability and domain-wise bias in prior methods, underscoring the value of synthetic image generation and prototype- -bssed classification for effective, unbiased domain adaptation.
Figure shows final per-domain BiDoT scores and accuracies, highlighting generalization behavior under different method designs. Since domains follow task order, methods such as EWC, which use parameter regularization, showed degraded performance later tasks across all datasets, as seen in their F-BiDoT scores. CODA-P, due to its decomposed prompt weighting mechanism, tended overfit specific domains, leading to imbalanced generalization across domains. CPrompt ad-
Campfire
Chair
Calulator
Clipart
Amazon
(Domainh
(Offcc-311)
Farnot
Aircraft Carriar
Appla
Backet
Infogreah (DomaiNet)
Peinling (DomainNab
Person
Elephent
Horse
Person
Art Paint (PACS)
Sketth (PACS)
Collected
Exemplar
Transferred
Groundtruth
Collected
Exemplar
Transferred
Figure 6:
Visualization of generated samples. Exemplars were generated from collected data and used to synthesize cross-domain transferred images, shown alongside the ground truth for each class the target domain.
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Art Painting
Cartoon
Photo
Sketch
Dog
Elephant
Giraffe
Guitar
Horse
House
Person
Figure 7: I-SNE visualization of CLIP embeddings on PACS. Stars indicate ground truth, circles represent generated data, and colors denote different classes.
dresses key CIL. limitation by learning prompts and classifiers beyond current-task data, outper- forming CODA-P under standard CIL. (Gao al., 2024). However, it showed reduced performance on Office-31 in both F-BiDoT and final accuracy under XIL,, suggesting that methods overly tai- lored to fixed CIL assumptions may struggle to generalize more dynamic scenarios such as XIL. XEED leverages domain-specializer prompts and prototype-based classification to generalize with- out overfitting, consistently achieving the highest mean F-BiDoT across all domains and datasets.
To highlight the effectiveness of our generated samples in approximating real-world data, Figure 6 compares the cross cross-domain transferred data with the ground truth, while Figure 7 illustrates their proximity in feature space.
5.3 ABLATION STUDY
We conducted an ablation study to evaluate the contribution of each component in XEED, as shown in Table 2 Four ablation configurations were tested. First, w/o prompt removes the domain- specialized prompts, using frozen pre-trained feature extractor without any domain adaptation. This setting evaluates whether prototype embeddings alone are sufficient to distinguish between classes across domains. Second, w/o generation excludes synthetic image generation, assessing the role generated samples maintaining robust bidirectional domain transferability. Third, w/o prototype replaces prototype-based classification with an auxiliary linear classifier, testing whether generalization still achievable without semantic expansion using evolving prototypes. Lastly, w/o inference disables domain prototype matching and instead randomly selects domain prompt during inference, measuring the importance of domain-aware prompt selection for consistent prediction.
W/o prompts led moderate drops in F-BiDoT scores across all datasets, despite relatively sta- ble final accuracy. This indicates that domain- specific prompts are crucial for achieving ro- bust generalization
Table 2: Ablation study
XEED components.
PACS
DomainNet
Office331
F F-BiDoT
Final
F FiBiDo
Final
F-BiDoT Final
XEED
65.19
61.86
33.63
35.30
78.08
80.72
unseen domains, even when the base model performs reasonably well. W/o generation caused the most severe perfor- mance degradation, especially in F-BiDoT, demonstrating the central role of generated samples in enabling bidirectional domain transfer. W/o prototype led a disproportionately larger degradation in F-BiDoT compared to final accuracy, more than in other ablations. This highlights the importance of evolving prototypes in enhancing generalization to unseen domains. Lastly, w/o inference showed minimal impact in Office-31 due to its low inter-domain variation, but led to larger drops in both F- BiDoT and final accuracy in more diverse datasets. This highlights the importance of domain- -ware prompt selection under greater domain shift. Overall, the ablations confirm the necessity of each XEED component for robust domain transfer and incremental learning.
wio generation
prompts
20.91
45.22
39.78
58.46
26.62
4.47
10.52
30.66
33.62
65.41
52.40
73.80
wio prototype
18.85
43.48
5.24
15.05
35.60
54.81
inference
52.33
51.26
26.00
26.54
77.83
79.78
5.4 ADDITIONAL EXPERIMENTAL ANALYSES
Impact of Synthetic Exemplar Quality on Prototype Dynamics. To assess whether synthetic exemplars introduce stylistically inconsistent samples that could distort prototype learning, we per- formed an additional analysis on the Office-31 dataset. For each class-domain pair, we used CLIP embeddings to compute Mahalanobis distances over generated samples, treating the bottom 5% as normal samples and the top 5% as potential outliers.

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To assess the impact of such outliers proto- type formation, we compare prototype-normat distances (Normal) with prototype-outlier dis- tances (Outlier). If outliers distorted the pro- totypes, they would lie closer the prototype than normal samples, effectively pulling the prototype toward them. However, we consistently observe the opposite: as shown in Table domain-level results averaged across all classes indicate that outliers remain farther from the proto- type (% Farther). with 85- 100% of classes exhibiting this pattern. Because prototype updates rely on mean aggregation, higher-quality samples naturally dominate the estimate, preventing atypical outliers from exerting significant influence.
Table 3: Prototype-sample distance statistics.
Domain
Normal
Outlier
Farther
GT Outlier
Amazon
25.23
37.75
85.0
41.08
DSLR
19.88
29.89
100,0
23.53
Webcam
23.71
37.41
95.2
27.25
B
Finally, we compare these distances with those from the real dataset. The absolute prototype-sampe distances of synthetic images are comparable to the natural intra-class variation in real data (GT Outlier), and in some domains (e.g., Amazon), real outliers are even farther from the prototype. This shows that the variability introduced by generated samples falls within the natural range of real images and does not negatively affect prototype construction.
Effect of Trainable Parameter on Domain Specialization. XIL requires models to gener- alize to unseen class-domain combinations, and excessive trainable parameters can lead the model to over-specialize to domain-specifie patterns present in the training distribution. For this reason, XEED employs frozen backbone with lightweight domain prompts, which lim- its overfitting while preserving generalization capacity, and still provides strong performance on seen domains. To validate this design choice, we implemented an adapter-based vari- ant by attaching residual adapters (Houlsby et al.] 2019) to every ViT block and compared it with the proposed prompt-based XEED model on the Office-31 dataset. As shown in Ta- ble 4 the adapter variant shows an almost 11% drop in F-BiDoT, indicating weakened gener- alization to unseen class-domain combinations when more trainable parameters are introduced. In contrast, XEED maintains higher Fi- nal and Avg performance, showing that lightweight prompt adaptation provides Method
Table 4: Trainable parameter comparison.
F-BiDoT
A-BiDoT
Final
Avg
Prompt (Ours)
78.08
73.67
80.72
83.19
better balance between robustness to distri- bution shift and strong in-domain accuracy.
Adapter
67.97
60.85
76.71
81.55
More Incremental Tasks, We further evaluate a more ane-grained setting by splitting each in- cremental task in half (6 tasks total, with 2 per domain in Office-31). As shown in Table increased granularity substantially degrades the strongest baseline, highlighting that limited per- task class diversity leads to poorer generaliza- tion. XEED maintains strong generalization and remains superior overall.
Table 5: More Incremental Tasks.
Method
F-BiDoT
A-BiDoT
Final
Avg
XEED
84.40
80.92
80.75
84.19
CPrompt
70.12
71.72
73.75
79.72
Prompt Selection. In XIL, classes and domains continually expand, and a parametric selector trained only on past distributions would struggle to generalize to unseen class- combinations.
XEED uses a non-parametric selector in the prototype space, which in- cludes synthetic exemplars for unseen combinations and enables flex- ible routing without task-specific parameters. Prompt selection accu- racy Office-31 (Table 6) measured on the final task, shows that the selector generalizes well even to unseen class-domain pairs. Note that the goal is not domain classification, but effective routing prompts that support class prediction.
Table 6: Prompt selection.
Metric
Accuracy (%)
Overall 86.86
Unseen
83.19
6 CONCLUSION
In this work, we introduced Cross-Expanding Incremental Learning (XIL), novel setting that ex- tends Mlas-incremmnnal learning to enable bidirectional domain knowledge transfer. To tackle the challenges of XIL, we proposed XEED (Semantic Expansion through Evolving Domains), a frame- work that combines
prompts, residual-guided modulation, and evolving proto- types. We introduced the BiDoT Score, a new metric for explicitly measuring bidirectional domain transferability, which is not captured by existing CIL benchmarks. Experiments on diverse bench- marks demonstrated the effectiveness of XEED and revealed the limitations of prior methods under XII.. We hope this work fosters further research toward generalizable continual learning paradigms.
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7 ACKNOWLEDGEMENT
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (ITTP) grant funded by the Korea government (MSIT) [RS-2021-1111331,, Artificial Intelligence Graduate School Program (Chung-Ang University)).
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A
APPENDIX
We provide additional clarifications here in response to the key concerns raised during rebuttal. Other revisions have been incorporated into the main paper.
Relationship Between XIL and Cross-Domain Continual Learning. Existing definitions of Cross-Domain Continual Learning (CDCL) differ across the literature. As summarized in Sec- tion 2.2 Simon et al.] 2022) defines CDCL as CIL with domain generalization, where each task provides multi-domain data and model learns domain-invariant features. In contrast, (De Carvalho learning with domain adapta- tion. where models have access to unlabeled target domain data and adapt to fixed target domain.
al. 2024 Weng al. 2024) frame CDCL as
XIL differs from both: neither classes nor domains repeat across tasks, and each class appears in exactly one domain. Model receives neither multi-domain supervision nor target domain data, yet must predict all classes across all domains test time. This requires domain knowledge to transfer across classes and to support both forward and backward transfer, capabilities not addressed by existing CDCL settings. This structure reflects practical scenarios where classes arise new domains without additional supervision. XIL. captures this challenge, and XEED is designed to enable the necessary bidirectional cross-class transfer domain knowledge.
Choice of Datasets for Evaluating XIL. XIL requires each task to introduce new combinations, which many common benchmarks cannot support. CoRE50 (Lomonaco & Maltoni 2017) includes only 10 classes across 11 domains, allowing at most one class per task and preventing valid multi-class XIL task construction. Using subsets would make results highly dependent on the chosen sessions.
Likewise, benchmarks such as CIFAR-100 (Krizhevsky al. 2009). TinyImageNet (Le & Yang 2015), and ImageNet-subbets contain only single domain, collapsing XIL into ordinary CIL. Multi- domain datasets like ImageNet-R (Hendrycks al. 202T) are also unsuitable because domains are not aligned across classes, making unseen class-domain evaluation impossible.
For these reasons, we use Office-31, PACS, and DomainNet, which naturally satisfy XIL's structural requirements and provide meaningful evaluation of cross-class domain transfer.
Effect of Domain Order. We evaluated XEED and CPrompt under randomly shuffled domain orders across both Office-31 and PACS, as shown in the table below. XEED remains stable across orderings, while CPrompt shows strong sensitivity. Domain abbreviations: W Webcam, A Ama- zon, D DSLR, Cartoon, S Sketch, P Photo, Ar Art.
Dataset Office-31
Domain Order
Method
F-BiDoT
A-BiDoT
Final
Avg
W + A +D
XEED
75.75
75.54
80.02
85,80
CPrompt
66.97
65.11
75.37
83.98
Office-31
D W +
XEED
79.93
85.00
83.16
91.89
CPrompt
67.62
82.16
76.16
91.22
PACS
C-S-PP-A Ar
XEED
64.99
70.24
67.67
80.57
CPrompt
18.18
31.78
28.27
61.57
PACS
P
XEED
59.88
71.07
66.14
81.58
CPrompt
39.73
54.41
53.21
75.09
Future Work: Toward a Unified Diserininative-Gennrattt Model. Discriminative objectives (e.g., cross-entropy) focus class-specific cues but often miss global distributional structure, lim- iting domain separation and transfer. Generative models such as diffusion models capture full data distributions and thus encode domain-level characteristics that discriminative training alone cannot. Our findings suggest that both signals are necessary for stable learning under domain shift.
No existing method unifies discriminative modeling with diffusion-bran based distribution modeling. As a future direction, we aim to develop a hybrid discriminative -eriminatvve-eeeeaatvee framework building on XEED, enabling more efficient and robust forward and backward transfer of domain knowledge.
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XIL Task Construction and Evaluation Code. We provide reference implementation code for (1) constructing XIL tasks and (2) computing the XIL evaluation metrics used in the main ex- periments. These snippets reproduce the core logic used in our experiments and are included for clarity and reproducibility. The full implementation is available at https: //gt thub. com/
heayounchoi/XTI
Code 1: Python code for XII. task construction.
def tikk iclass_list, domain_iist): Shuffle global clasa order
cioye_liel.copy! randem. thuffeecclssees
classes ellacsted per domain
pes_domain (elasseee) // len (domain_.ist)
task_order
Assign class subsets to
for in Comain_liatt
Lask_ordez wpw.edee..ppeedececccce Iclasses [idx
Ldx per_domain])
16x per_domaim
taek_order
Code 2: Python code for computing XIL metrics (Final, BiDoT,, and Prompt Accuracy).
def compute_xis wil__etrrie pred, y_true, dom_pred, dom_true, Lask_ordez) metrics ()
1. Pinal acouTacy
ly_pred y_tzue) imean
BiDeT (unseen class-dcmain sccurscy) unseer_idx a
1 for in range (y_true) if [_true(:) in task_order (don_true
metrics ['BiDoI"]
(é_ y_true [unseen_idx) .mean
Prompt salection sccuracy metrica [*Prompt"] 100 (don_pred dom_true) meean
ricrica
16


Reviews:
[{"review_id": "G2InBT10Hg", "reviewer": "Reviewer_RYCp", "summary": "This paper introduces Cross-Expanding Incremental Learning (XIL), a new setting that extends class-incremental learning to handle data from distinct and evolving domains. XIL requires models to perform bidirectional domain transferability, integrating new classes into old domains and adapting old classes to new ones. To address this challenge, the authors propose XEED, a framework that combines domain-specialized prompts, residual-guided modulation, and evolving prototypes to expand class semantics across domains. They also introduce the BiDoT Score, a metric that measures how well models generalize across unseen class–domain combinations. Experiments on benchmark datasets with significant domain shifts show that XEED outperforms existing methods in both accuracy and BiDoT scores.", "questions": "- How do the authors ensure that the diffusion-generated exemplars are of sufficient visual and semantic quality to represent unseen domain–class combinations?\n\n- Could noisy or low-fidelity exemplars negatively affect the evolving prototype updates, and if so, how is this mitigated?\n\n- How scalable is XEED when applied to larger datasets or a greater number of incremental tasks and domains?\n\n- Why was the backbone \n𝑓\n𝜙\nf\nϕ\n\t​\n\n kept frozen during prompt tuning, and have the authors experimented with partial fine-tuning or adapter layers?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is clearly written and well-structured, allowing readers to easily follow the logical flow and understand the motivation and proposed method at both conceptual and technical levels.\n\n- The empirical results convincingly demonstrate the effectiveness of the proposed approach across multiple standard benchmark datasets.", "weaknesses": "- The **quality and fidelity of synthetic exemplars** present a potential limitation. The proposed method assumes that diffusion-based generation can effectively represent unseen domain–class combinations. However, such generative processes may produce low-fidelity or stylistically inconsistent samples, which could introduce noise into the evolving prototype updates and distort the learned feature space. A more detailed analysis of the generation quality—either through visual inspection, quantitative metrics such as FID or LPIPS, or ablation studies—would help validate whether the synthetic exemplars truly enhance domain transfer rather than acting as noisy augmentations.\n\n- Another concern lies in the **high computational cost and reliance on diffusion models**. The method depends on a pre-trained diffusion generator to synthesize cross-domain exemplars, which is computationally expensive due to multiple denoising steps, high GPU memory requirements, and significant I/O overhead. While diffusion models contribute to the quality of generated samples, their use raises questions about scalability and practicality in large-scale or real-time incremental learning scenarios. A discussion of the computational budget, inference latency, and trade-offs between efficiency and performance would strengthen the paper’s experimental transparency.\n\n- Finally, the framework’s **dependence on frozen backbones and domain prompts** may restrict adaptability under severe domain shifts. The feature extractor remains fixed during prompt tuning, assuming pre-trained features are sufficiently general for new domains. In cases where the domain gap is large (e.g., transferring from natural images to sketches or depth maps), this assumption may not hold, leading to suboptimal adaptation even with domain-specific prompts. Allowing partial backbone fine-tuning or incorporating adaptive normalization could improve flexibility and robustness in such scenarios.", "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": "6wxoH1pJQG", "reviewer": "Reviewer_9JFF", "summary": "This paper introduces Cross-Expanding Incremental Learning (XIL), a new, more realistic problem setting that extends traditional Class-Incremental Learning (CIL) by requiring models to learn new classes that arrive from distinct, evolving domains. The core challenge is achieving bidirectional domain transferability (BiDoT), where the model must generalize previously learned classes to new domains and new classes to old domains, even for combinations never seen during training. To address this, the authors propose a novel framework called Semantic Expansion through Evolving Domains (XEED), which utilizes domain-specialized prompts to adapt to different domains, a generative model to synthesize images for unseen class-domain pairs, and evolving prototypes to continuously expand the classifier's semantic space. Accompanied by a new evaluation metric, the BiDoT Score, experiments show that XEED significantly outperforms existing CIL methods, which struggle in this dynamic setting, thereby establishing a strong foundation for continual learning in real-world, non-static environments.", "questions": "NO", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper's foremost strength is its introduction of the Cross-Expanding Incremental Learning (XIL) problem, which establishes a more realistic and challenging benchmark for continual learning by forcing models to simultaneously handle new classes and evolving data domains.\n\nThe proposed XEED presents a highly innovative solution, ingeniously using a generative model to synthesize data for unseen class-domain pairs, thereby directly addressing the core challenge of bidirectional knowledge transfer.", "weaknesses": "The paper does not sufficiently explain how the baseline methods, originally designed for the standard CIL setting, were adapted to the new multi-domain XIL setting. This is particularly concerning for methods like S-Prompts, which show drastically lower performance, raising questions about whether the comparison is entirely fair\n\nAs a paper proposing a new problem setting and a novel evaluation metric, the absence of publicly available code is a major drawback. This lack of resources makes it extremely difficult for the research community to verify the reported results, adopt the XIL benchmark, and fairly compare future methods, thus hindering follow-up research.\n\nWhile the XEED is effective, its core components: domain-specific prompts, generative replay, and prototype-based classifiers, are existing techniques.", "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": "c8RW8xIxo7", "reviewer": "Reviewer_gcQm", "summary": "This paper proposes the Cross-Expanding Incremental Learning (XIL), a new setting of continual learning. The XIL presents the problem of existing setting of class-incremental learning (CIL), where the model learns from the data of same domains. XIL emphasizes the  capability of transferring knowledge across different domains and proposes a new settiing. A corresponding method XEED is proposed to address the XIL. XEED is a generative replay-based method which leverages a pre-trained diffusion model and CLIP to generate samples that are transferable across different domains. Domain-specific prompts and prototypes are proposed to preserve knowledge from different tasks. Several experiments are conducted to validate the effectness of XEED.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper consider a new paradigm of continual learning, where the capability of transferring across different domains are considered.\n2. This paper is well writen with good presentation and clear motivation.\n3. The technique of generating exemplars with representation modulation is noval and sound.", "weaknesses": "1. Using the pre-trained diffusion model and CLIP. Although this paper is overall good, I have several concerns about using the pre-trained diffusion model. Since a good exemplar or sample for transferring can be generated, the knowledge related to the domains and classes is already encoded in the pre-trained model. As such, do we still need continual learning of another model? Most existing generative replay-based methods train a generative model during the continual learning, which is incremental learning process parallel to the training model. However, if the model needs another model which already has the knowledge of future data, since we already have a good one, what is the meaning of incremental learning of that model? \n2. The proposed method XEED is simple and preserving prompts and prototypes specific to distinct domains or classes has been widey studied in the community of continual learning. \n3. The capability of transferring across domains, the major challenge defined in this paper, seems mainly benefit from the generative replay. It seems that the major problem defined in this paper is solved by introducing knowledge of other models while the other parts of this method have mere innovation. One can argue that with such a replay mechamism, existing CIL methods can be easily transferred to the setting of XIL.\n4. Results on dataset specially designed for the domain and class incremental learning, e.g., CoRE50 [1]. should be included. The datasets used in the experiments are not designed for incremental learning. The CoRE50, which is designed for both the CIL and Domain-incremental learning tasks, can be a good benchmark of XIL.\n\n[1] Vincenzo Lomonaco and Davide Maltoni. Core50: a new dataset and benchmark for continuous object recognition. In Conference on Robot Learning, pages 17–26, 2017.", "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": "0DC6qYYmtJ", "reviewer": "Reviewer_w46G", "summary": "This paper presents cross-expanding incremental learning where not only class changes occur but also domain shift happens at the same time. To address this problem, authors proposes Semantic Expansion through Evolving Domains (XEED).", "questions": "1) the following references also deal with domain adaptation and continual learning. I suggest to review them in the paper.\n\n[1] Towards Cross-Domain Continual Learning\n[2] Cross-Domain Continual Learning via CLAMP\n\n2) please kindly explain the difference of your works with cross-domain continual learning as proposed in [1] and [2]\n\n3) I suggest to elaborate more on the real-world context of your setting. This allows readers to appreciate more on your contributions. Also, it is suggested to link directly your problem with a concrete dataset that you use. Is there any dataset that can represent your problem?\n\n4) the prompt selection mechanism is non-parametric and perhaps over-simplified.\n\n5) prompt selection accuracy should be reported.\n\n6) the domain order should affect the result. it should be detailed in the paper.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1) although it intersects with existing setting, the XIL problem is new. \n2) the proposed solution, namely XEED, performs well.", "weaknesses": "1) the following references also deal with domain adaptation and continual learning. I suggest to review them in the paper.\n\n[1] Towards Cross-Domain Continual Learning\n[2] Cross-Domain Continual Learning via CLAMP\n\n2) please kindly explain the difference of your works with cross-domain continual learning as proposed in [1] and [2]\n\n3) I suggest to elaborate more on the real-world context of your setting. This allows readers to appreciate more on your contributions. Also, it is suggested to link directly your problem with a concrete dataset that you use. Is there any dataset that can represent your problem?\n\n4) the prompt selection mechanism is non-parametric and perhaps over-simplified.\n\n5) prompt selection accuracy should be reported.\n\n6) the domain order should affect the result. it should be detailed in the paper.", "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": "Z15NjnUauv", "reviewer": "Reviewer_EJAk", "summary": "The paper introduces Cross-Expanding Incremental Learning (XIL)—a setting where both classes and domains evolve—and proposes XEED, a rehearsal-free framework that (a) learns domain-specialized prompts, (b) modulates class features with domain residuals using a diffusion model (IP-Adapter + SDXL), and (c) performs prototype-based classification with evolving, domain-aware prototypes. Also, define the BiDoT Score to measure bidirectional domain transferability. XEED substantially improves BiDoT and accuracy on PACS, Office-31, and DomainNet versus strong CIL/prompt baselines.", "questions": "Please refer to the weakness", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper introduces a new continual learning setting, specifically,\n\n1) Clean formulation of XIL + dedicated BiDoT metric.\n\n2) Method components are modular and leverage frozen encoder + small learned parts.\n\n3) Privacy-friendlier than exemplar replay; aligns with synthetic replay trends.", "weaknesses": "1) The paper admits that no directly comparable XIL baselines exist and hence reuses CIL prompt-based methods (S-Prompts, CODA-P, CPrompt, etc.) as proxies. Since XIL is a new setting, the paper adapts existing CIL methods as baselines rather than comparing with purpose-built domain-evolving or generative continual learning frameworks. This makes it difficult to fully assess XEED’s relative performance or to verify whether its improvements come from the new setting or the framework design itself.\n\n2) XEED’s strongest BiDoT improvements depend critically on synthetic image generation using IP-Adapter + SDXL. The ablation study shows that removing this component causes F-BiDoT to plummet (e.g., from 65.19 → 20.91 on PACS and 33.63 → 4.47 on DomainNet). This dependence implies that the system’s success hinges on access to a large diffusion model and high-quality generation. The method may fail or become impractical if the computing is limited or the diffusion model struggles with domain realism.\n\n3) Limited or no experiment in common continual learning benchmarks such as CIFAR100, TinyImagenet200, ImageNetsubset100, Food, Cars, ImageNet-R/A.", "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)