When and Where to Reset Matters for Long-Term Test-Time Adaptation
Authors: Taejun Lim
Joong-Won Hwang
Kibok Lee
Venue: ICLR 2026 Poster (2026)
Keywords: Test-Time Adaptation
Continual Test-Time Adaptation
Long-Term Test-Time Adaptation
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: When continual test-time adaptation (TTA) persists over the long term, errors accumulate in the model and further cause it to predict only a few classes for all inputs, a phenomenon known as model collapse. Recent studies have explored reset strategies that completely erase these accumulated errors. However, their periodic resets lead to suboptimal adaptation, as they occur independently of the actual risk of collapse. Moreover, their full resets cause catastrophic loss of knowledge acquired over time, even though such knowledge could be beneficial in the future. To this end, we propose (1) an Adaptive and Selective Reset (ASR) scheme that dynamically determines when and where to reset, (2) an importance-aware regularizer to recover essential knowledge lost due to reset, and (3) an on-the-fly adaptation adjustment scheme to enhance adaptability under challenging domain shifts. Extensive experiments across long-term TTA benchmarks demonstrate the effectiveness of our approach, particularly under challenging conditions. Our code is available at https://github.com/YonseiML/asr.

Published as a conference paper at ICLR 2026
WHEN AND WHERE TO RESET MATTERS FOR KONG-TERM TIST-TIME ADAPTATION
Taejun Lim'
Joongwon Hwang?
Kibok Lee"
Yonsei University ETRI
(ttajjun. kibokk .c.kr "juhwang@etri.
ABSTRACT
When continual test-time adaptation (TTA) persists over the long term, errors ac- cumulate in the model and further cause it predict only few classes for all in- puts, a phenomenon known as model collapse. Recent studies have explored reset strategies that completely erase these accumulated errors. However, their periodic resets lead to suboptimal adaptation, as they occur independently of the actual risk of collapse. Moreover, their full resets cause catastrophic loss of knowledge acquired over time, even though such knowledge could be beneficial in the fu- ture. To this end, we propose (1) an Adaptive and Selective Reset (ASR) scheme that dynamically determines when and where to reset, (2) an importance-awarc regularizer to recover essential knowledge lost due to reset, and (3) an on-the-fly adaptation adjustment scheme to enhance adaptability under challenging domain shifts. Extensive experiments across long-term TTA benchmarks demonstrate the effectiveness of our approach, particularly under challenging conditions. Our code is available at https: / /gi thub. com/ Yonse ML./ asr.
INTRODUCTION
Test-time adaptation (TTA) (Liang et al., 2020; Sun et al., 2020; Wang et al., 2021) aims to address the growing challenge of distribution shifts in real-world applications by enabling model adaptation at test time. Recently, TTA research has expanded to continual scenarios (Wang et al., 2022; Döbler et al., 2023), allowing models to adapt to non- non-stationary stream of domains, where updates proceed continuously while errors accumulate over time. However, when domain shifts persist over the long term, these errors further result in model collapse (Niu et al., 2023; Shumailov et al., 2024), in which models converge to generate incorrect predictions concentrated only few classes for all inputs. To address this, recent studies have explored methods seeking to preserve knowledge from the source domain when adapting to target domains (Wang et al., 2022; Marsden al., 2024; Press al., 2023). A straightforward yet effective method involves periodically resetting model parameters to those of the source model (Press et al., 2023), which erases accumulated updates and errors, thereby reseuing the model from irreversible collapse. However, such mechanism forces resets to depend single pre-defined reset interval across all situations, leading to too frequent or infrequent resets. More- over, this completely erases the knowledge acquired during adaptation, thereby disrupting forward knowledge transfer within the continuously adapting model (Díaz- Rooriggzd et al., 2018).
To this end, we propose an Adaptive and Selective Reset (ASR) scheme that dynamically determines when and where to reset based on the concentration of predicted classes to estimate the risk of model collapse. We trigger a reset once the risk is deemed significant, and adjust its scope according to the severity of the risk. Several studies (Bai et al., 2021; Yang et al., 2024) showed that corruption from label noise begins at the end of the network. Since this corruption results collapse, we prioritize layers closer to the output for reset. Fig. illustrates how our ASR scheme differs from the aforesaid naive reset approach. In addition, we introduce an importance-awart regularizer to recover essential knowledge lost due reset. We estimate parameter importance through newly formulated Fisher information. Based on this, parameters crucial for previous tasks are aligned with their accumulated state, which incorporates all prior target knowledge. Finally, we propose adjusting model adaptation on the fly based on domain discrepancy. We define prediction inconsistency to quantify this discrepancy, and then it update model hyperparameters via reparameterization, improving our adaptability under challenging domain shifts. Our contributions are as follows:
We propose an Adaptive and Selective Reset (ASR) scheme that dynamically determines when and where to reset, effectively preventing model collapse while mitigating knowledge lost due reset.

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Published as a conference paper at ICLR 2026
RDumb (Baseline)
ASR (Ours)
Acc. (%)
Layer
Acc. (%)
Layer
40
140
33
20
5000
6000
7000
Test Batches
8000
9000
10000
Full Reset
5000
6000
7000
Test Batches
8OOD
9000 10000
Selective Reset Dataset: CCC-Hard
Fixed Reset Intervals
Dataset: HCC-Hard
Dynamic Reset Intervals
Illustrative comparison between a naive reset approach (RDumb; Press et al. (2023)) and our Adaptive and Selective Reset (ASR) based the same model (ETA; Niu et al. (2022)). RDumb fully resets parameters at fixed intervals (e.g., every 1000 steps), whereas ASR dynamically decides when and where reset, achieving more stable (i.e., less abrupt performance drop each reset) and higher performance. Dotted vertical lines indicate when resets occur.
Figure 1:
Beyond the reset, we introduce an importance-aware regularizer recover parameters that are in- evitably reset but deemed crucial to prior tasks, and on-the-fly adaptation adjustment that updates model hyperparametens according to domain discrepancy to enhance adaptability.
Extensive experimental results across various long-term TTA benchmarks demonstrate the effee- tiveness of our method, Remarkably, it yields a substantial 44.12% improvement over the state of the art on the challenging Hard (Press et al., 2023).
2 RELATED WORK
Test-time adaptation. TTA enables a model to adapt to unknown target environments without any target assumptions. Since true labels are unavailable at test time, early works have explored effective unsupervised adaptation (Kundu et al., 2020; Li et al., 2020; Liang et al., 2020). Initial TTA research proposed to adjust batch normalization statistics (Schneider et al., 2020; Mirza et al., 2022), which evolved toward integrating self training approaches (Zhang et al., 2022: Goyal et al., 2022), such as entropy minimization, improving predictive confidence on target data (Wang et al., 2021). which has been developed to prevent wrong confidence intensification (Zhang et al., 2025a; Han et al., 2025).
Continual test-time adaptation. Self-training methods face a critical challenge in non-stationary domain stream, where their performance gradually deteriorates over time with noisy pseudo- repeated (Wang et al., 2022: Niu al., 2023). It accumulates errors, enhancing predictive confidence in incorrect predictions, eventually leading them to converge to suboptimal solutions, phenomenon known as model collapse (Niu et al., 2023: Shumailov et al., 2024). Several studies (Niu al., 2023; Hoang et al., 2024) empirically demonstrated that once collapsed, a model assigns all inputs to few dominant elasses. COTTA (Wang et al., 2022) addresses this issue by stabilizing its self-training scheme using augmentation averaged pscudo-labels and preventing source knowledge forgetting via stochastic parameter restoration. To better handle error accumulation, recent research has explored reliable adaptation methods, such as adaptive learning rates (Park et al., 2024; Maharana et al., 2025) and adaptive loss functions (Liu et al., 2024a).
Long-term test-time adaptation. While effective at preventing collapse standard continual TTA, existing methods encounter difficulties in more realistic settings such as gradual (Döbler et al., 2023) or smooth (Press et al., 2023) long-term domain shifts. To address this, ROID (Marsden et al., 2024) proposes weight ensembling method that updates the current model by combining with weighted pre-trained model. CMF (Lee & Chang, 2024) further extends this by updating the pre-trained model with the current model, inspired by the Kalman filter (Särkkä & Svensson, 2023). More aggressive methods have also been proposed, including resetting all parameters to their pre-trained state at every fixed step (Press al., 2023). Others trigger resets only when extremely high predictive con- fidence (Niu et al., 2023) or severe distribution shifts from the source domain (Wang et al., 2024) are detected. Another line of research has developed regularization restrict the deviation between the pre-trained and current parameters, such as weighting regularization via Fisher information (Niu et al., 2022) or adjusting the regularization coefficient based on parameter divergence from the pre- trained state (Hoang et al., 2024). This coefficient can also be dynamically assigned for every single layer based on its depth (Yang et al., 2024) or its sensitivity to distribution shifts (Choi et al., 2022). Our work aligns with the emerging trend of long-term 7TA, which considers more realistic settings with long-term domain shifts. It addresses the limitations of conventional reset methods that either A
reset too frequently too infrequently and fully discard the knowledge acquired during adapta- tion. Specifically, our approach dynamically determines when and where to reset while recovering essential knowledge lost by reset.
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Published as a conference paper at ICLR 2026
Long Domain Stream
C
C-1 Ct
C C c c-1
Adaptive Reset by C E
Ce
G-11
302
000
Time
Online Batch Stream
Selective Reset with C c (Eq. (3))
Time 10
A
2
Ctt Pred. concentration (1)) Cc-1: Cum. concentration (Eq. (21)
1
Figure 2: Overview of our Adaptive and Selective Reset (ASR) scheme, which compares prediction concentration C, with its cumulative counterpart C,-1 I for each test batch from a long domain stream, triggers a reset when C, > C2-1, indicating that the model is corrupted severely enough to collapse, and determines layers to reset based on C. Cc-1, which reflects how severely the model is corrupted. On the upper side, icons inside dashed boxes, labeled with numbers, denote class labels. White icons represent correct predictions, while black icons represent incorrect predictions.
3
METHOD
3.1 PROBLEM DEFINITION
Given pre-trained source model fa.. our goal is to improve its performance test time over a long sequence of test domains without access to source data. A handful of test samples arrive in sequence and are then inaccessible once processed via the model. a test sample zi and generates a prediction of = o afo, (xt)), where f. yields logit outputs and is the softmax function. The model is evaluated using its predictions it. and is then adapted as 9-11- de using unsupervised objective functions. Moreover, we aim for stable adaptation without performance degradation under collapse-prone scenarios such perpetually changing cyclically recurring domain streams. To this end, we address the limitations of cxisting reset approaches, such suboptimal reset timing and catastrophic loss of knowledge, through the following three main components: (1) Adaptive and Selective Reset (ASR; illustrated in Fig. 2), (2) importance-aweer knowledge recovery, and (3) on-the-fly adaptation adjustment.
step f, the current model fo.-, given
3.2 MOTIVATION
We observed that RDumb's periodic reset (Press al., 2023) is only suitable for standard TTA benchmarks where domain shifts occur at regular intervals. However, real-world settings do not fol- low the fixed shift schedule, and their shift timing can vary significantly. RDumb reset either too early or too late, misaligned with the actual risk of collapse, leading to suboptimal or unstable adap- tation. Fig. shows that RDumb suffers from a substantial performance drop after each reset. This is primarily due to the full reset, which discards all knowledge acquired so far, causing significant delay recover the pre-reset performance. These observations motivate our reset scheme, which resets only when model is at the risk of collapse and reduces knowledge lost by reset. We support our motivation by quantifying the performance drop and the recovery delay Appendix A.1.
3.3 ADAPTIVE AND SELECTIVE RESET
When to reset. We introduce an adaptive reset scheme that triggers a reset only when a high risk of collapse is detected. To achieve this, we define prediction concentration C, leveraging the notion that entropy reflects the uniformity of a distribution, where So f ttmax (Mean (Log t.s) serves as the underlying measure, as follows:
B.
C==
Σ Pr. log(dr.) where P. =
1 B.
Σ
fo.
(x))
(x+))
C' is the total number of classes, and P.., indicates the probability of the e-th class in Pt. obtained by applying the softmax function a to the average logits of the batch B, at time step t. Note that C, quantifies prediction concentration; hence, large C, indicates low prediction diversity and a high risk of collapse. Although we can measure the concentration of predicted classes, it remains unclear when it is high enough suggest that the model is on the verge of collapse. We argue that when the concentration C, deviates from its long-term normal behavior, it can be regarded as an indication that collapse is likely to emerge, and define cumulative concentration C, computed via exponential
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Published as a conference paper at ICLR 2026
moving average (EMA), as follows:
pc) p pe- C.-1 (1-1+(1-pe)-Ce Cc:
(2)
where Pec is the momentum coefficient, and Co is initialized - log C*) using a pre-defined onp- We compare C2 with its cumulative counterpart C to determine whether to trigger a reset at each step Ct-1 is reinitialized as - log(ax- C') if the model reset; otherwise is updated via Eq. (2). We choose oo such that the initial cumulative value is sufficiently larger than Ct, preventing prema- ture resets early stages and providing sufficient warm-up time for Ct-1 1 to form a reliable estimate of the long-term normal behavior of C2 (see the top right of Fig. 2). We trigger a reset immediately when C, > C--1 is detected to prevent the accumulation of corrupted Fisher information (Sec. 3.4). To demonstrate that C₁ effective in detecting a high collapse risk, we evaluate its correlation with accuracy on the CCC-Hard dataset with the ETA model, as shown Fig. 3, where lower ac- curacy indicates a higher risk of collapse. We find a strong Pear- son correlation of 0.88, confirming the reliability of C, Detailed settings and additional analysis are provided in Appendix A.2.
Accuracy 0
r- 0.88
10
Where to reset. A critical drawback of reset is catastrophic loss of knowledge accumulated over time. To alleviate this, we take advantage of the hierarchical nature of deep neural networks. In early collapse, layers closer to the input tend to be more robust to corruption than those closer to the output, since the corruption
-b.6 -6.5 6,44 Prediction Concentration
Figure 3: Corr. of C, and Acc.
from label noise begins at the end of the network (Bai et al., 2021; Yang et al., 2024). Inspired by this, we introduce selective reset scheme that determines which layers to reset, prioritizing those closer to the output. As corruption intensifies during collapse, the reset should be applied to more layers in proportion to the estimated collapse severity. We quantify this severity by measuring how far our concentration metric C, deviates from its long-term normal behavior, i.e. C. C.-1. We define a reset proportion Te that controls how many layers reset, as follows:
Tt = To + X (C: - CC,-1)
(3)
To and A, are pre-defined as the minimum reset proportion and the reset proportion scaling factor. Tr is always greater than ro since reset is triggered only when C2 > C,-1, and r, is subject to upper bound of 1.0 (i.e., 100%). The layers to be reset are determined starting from the output, such that the last T. proportion of layers are reset, while the remaining 1 T₃ are preserved'.
3.4 AAOORTANCE-AARRE KNOWLEDGE RECOVERY
Although we mitigate catastrophic loss of knowledge due to reset, some highly important knowledge is still inevitably erased. To further address this issue, introduce importance aware regularizer designed to recover essential knowledge lost. At every iteration, we accumulate Iearnable parameters and their importance matrices computed via Fisher information (Kirkpatrick et al., 2017; Zenke et al., 2017; Schwarz et al., 2018). We then apply the regularizer to strongly guide parameters deemed significant in previous tasks toward alignment with the accumulated ones, follows:
0.-1l
(B, 0--1) A
Σ F (0-1160) -²)²
where L and L. denote total and unsupervised losses, F and Ø¹ are the i-th accumulated Fisher matrix and accumulated parameter, f/-1 is the i-th learnable parameter of f.-1. and xy is the regularization coefficient.
In the accumulation phase, the following dilemma arises: While parameters increasingly align with the current domain over time, their proximity to reset makes them more vulnerable corruption. By preximity to reset, we mean that over a prolonged period of adaptation, errors accumulate sub- stantially, compromising model integrity and increasing the necessity of reset. We further provide empirical evidence to support this in Appendix A.3. EMA is a commonly used accumulation tech- nique, but not an ideal choice here, as it inherently prioritizes recent information. To address this, we introduce a hybrid accumulation scheme that combines cumulative moving average (CMA) with EMA. At each iteration I, CMA accumulates learnable parameters and their Fisher matrices equally. EMA then aggregates the CMA-accumulatte values every reset-triggered point, after which CMA values are re-initialized to zero (see Algorithm 1). The acMuuuccuullted parameters and Fisher matrices correspond to 0 and F in Eq. (4). More details on this scheme are provided in
For example, with 15 layers and r; 0.5, the deepest layers are reset (rounded the nearest integer).
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Appendix A.4, and its computational efficiency is analyzed in Appendix A.5. Moreover, prove that the proposed regularizer effectively recovers essential knowledge by providing both theoretical and empirical evidence in Appendix A.6.
3.5 ON-THE-FLY ADAPTATION ADJUSTMENT
While we assume domain-evolving settings, we do not explicitly model how domain evolution un- folds when designing our method. Under challenging domain evolution, memorization of previ- ously seen target domains becomes important for effective adaptation. However, our adaptability may struggle to keep pace, since reset discards what the model has Iearned far during adapta- tion. In this case, strong guidance from the Fisher regularizer (Sec. 3.4) becomes necessary to fully exploit essential knowledge of prior target domains. Moreover, such challenging case amplifies the source-tare target discrepancy, thereby worsening label noise. Pseudo-labels tend to degenerate into nearly random assignments (Semenova et al., 2023), leading to unreliable estimation of C, in Eq. (1). To address this, we propose adjusting model adaptation on the fly based on domain discrepancy. We define prediction inconsistency Φh quantify domain discrepancy, follows:
18c
1 B.
6
1 (arg max(j/) + arg max(
where II denotes the indicator function, and ii and jf are the softmax outputs of the source model Sa, and the current model fo,_, for sample at step I, respectively. A larger Φe (closer to 1) indicates greater domain discrepancy. Based on this, we adjust adaptation on the fly by reparameterizing the regularization coefficient '5 in Eq. (4) and the momentum coefficient pe in Eq. (2) as follows:
)== λo o
MC = pg di + 1 - 14o
(7)
where An and Mn are pre-defined. As Φi increases, Λy increases within [0, Au] for stronger regular- ization in Eq. (4), and Mec increases linearly within [1 noo 1] to reduce the update effect of C.-1 in Eq. (2). Ao = O, no knowledge recovery occurs; if (10 = O, no update of C._1 occurs.
4
EXPERIMENTS
4.1 SETUP
Datasets. As discussed in Press et al. (2023), standard TTA benchmarks are inappropriate for eval- uating the stability of continual TTA methods under long-term adaptation, where models are highly prone to collapse. To address this, we leverage two recently introduced benchmarks (1, 2) specif- ically designed for collapse and adapt two widely used TTA benchmarks (3. 4) to better reflect long-term collapse-prone scenarios. We conduct experiments on the following four benchmarks: 1) Continually Changing Corruptions (CCC) (Press et al., 2023) is a benchmark systematically processed from ImageNet-C (Hendrycks & Dietterich, 2019). This assumes smooth domain shifts over the long term, where one gradually fades while another emerges, with the two overlapping. It is also divided into three adaptation difficulty levels (Easy / Medium / Hard), each incorporat- ing three corruption orderings and three corruption evolving speeds, resulting in nine variations per level. 2) Concatenated ImageNet-C (CIN-C) is an extended version of ImageNet-C. containing 50K images per corruption (10x more than the original), where 15 corruption types under the most severe condition (level 5) are in sequence. It often used by several works (Wang et al., 2022; Niu et al,, Gong et al., 2022; Brahma & Rai, 2023) to demonstrate their stability, while comparing with Tent (Wang et al., 2021) that fails to avoid collapse. Moreover, 3) ImageNet-C (IN-C) and 4) DetgeNet-D1109 (IN-D109) (Peng et al., 2019), both widely used TTA benchmarks, are adapted to reflect model collapse, following prior works (Press et al., 2023; Hoang et al., 2024). IN-C cyclically repeats the sequence of corruptions 20 times, including only four types on which the source model reaches less than 10% accuracy. IN-D109 is also processed in the same way as IN-C, but selects four domain types based on less than 50% accuracy. Refer to Appendix B for further dataset details. Baselines. We compare our approach with state-of-the-an continual TTA approaches. We categorize them into two groups based on whether they include any modules to prevent collapse. The first group without any safeguard against collapse includes ETA (Niu et al., 2022), RoTTA (Yuan et al., 2023), ViDA (Liu et al., 2024b), C-MAE (Liu et al., 2024a), PALM (Maharana et al., 2025), COME (Zhang et al., 2025a) and REM (Han et al., 2025). The second group involving collapse- preveetton modules includes EATA (Niu et al., 2022), CoTTA (Wang et al., 2022), RDumb (Press et al., 2023), SAR (Niu al., 2023), ROID (Marsden et al., 2024), CMF (Lee & Chang, 2024), PeTTA (Hoang et al., 2024)
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and ReservoirTTA (Vray al., 2025). Among all these baselines, we particularly compare with add-on methods including RDumb, COME and ReservoirTTA for a reliable evaluation of our reset scheme. Specifically, we apply these add-on methods to the commonly applicable baselines (i.e., ETA, EATA and ROID) and compare them with our approach in Table 1.
Implementation details. We implement all baselines in a unified repository (Marsden et al., 2024) using PyTorch (Paszke et al., 2019) and obtain all reported results via code replication to ensure fair and consistent comparison. Experiments are conducted on ResNet-50 (He et al., 2016), provided by either torchvis sion Robust Bench (Croce et al., 2021), as well as V1T-B-16 (Dosovitskiy et al., 2021) to evaluate the generalization of our approach. As for our implementation, we basically follow our TTA baselines. Specificallyy, T., in Eq. (4) is formulated based on what they define the final loss. We determine hyperparameters using only 5% of single holdout split in CCC-Hard and use the determined ones across all datasets. We assess robustness to hyperparameter changes across all CCC levels in Appendix A.7. For the CCC-based analysis, we consistently use single split with speed 2000 and seed 44. More details on our implementation are available in Appendix A.8.
4.2 MAIN RESULTS
a) CCC. We evaluate our reset scheme by comparing it with add-on methods that share the same TTA baselines, using the
Method
Easy
Medium
Hard
Mean
ETA (CML'22)
43.24_1.0
19.03-6.9
0.32-0.1
20.86
Table 1. Our ap- proach achieves the best average perfor- mance across all baselines, with particu- larly large improvements on CCC-Hard.
CCC benchmark
RDumb (NourPP5233)
49.47-0.8
39.42=1.5
9.77=18
32.89
COME (KLR'25)
6.34143
0.56iu1
0.14200
2.35
Reservoir (NeurlPS 25)
4..86_0.6
42.03_0.9
1.15108
30.68
ASR (Ours)
51.20.00.
41.8811
17.10.21
36.73
EATA (ICM1222)
49.52-0.9
39. 39.19-1.7
0,82+04
29.84
In contrast, COME performs poorly due
RDumb (NcurIPS'23)
49.95-0.8
39.93-14
11.09:1
33.66
to the absence of anti-collapse mech- anism. While the TTA baselines in Ta- ble 1 are built with entropy minimiza- tion (EM), we further report the results using objectives other than EM to vali- date the general utility of our method in Table 2. As any TTA model trains it- self using pseudo-labed derived from its own noisy predictions, it eventually col- lapses (Niu et al., 2023), thus making reset effective regardless of the underly-
COME (KC1)25)
46.67-33
36.63-16
0.80-04
28.03
Reservoir TTA (Ncu1PP925)
51.51=04
42.03=099
7.58=33
33.71
ASR (Ours)
50.40-0.9
40.78-1.7
16.22-14
35.80
ROID (WACV'24)
49. -9.88_.0.
40.47.1.4
12.48.26
34,28
+ RDumb (NcuriP5/25)
49.69=0.8
40.05=1.4
15.41215
35.05
-OME (KLR'25)
0.12,00
0.13:00
0.12:00
0.12
ReservoirTTA (NeuriPs 25)
50.86-0.3
40 40.89-0.8
13:71=10
35.15
ASR (Ours)
51.41.0.8
42.80
22.21
38.81
Table I: Evaluation of our reset via a comparison with
add-on methods across the commonly applicable TTA baselines using accuracy (%) on the CCC benchmark.
ing objective. Nevertheless, shown by ROID, self-entropy- based objectives that strongly sharpen confidence tend to be more effective because they significantly influence class concentration. Fur- thermore, CCC reveals the limitations of continual TTA methods and the effectiveness of our method under long-term collapse-prone settings in Table 4. Most baselines fail or collapse on CCC-Hard, whereas RDumb prevents collapse but degrades ROID on other levels. In contrast, our approach achieves consistent improvements and yields the largest gains on CCC-Hart surpassing the second best by 42% (i.e., 15.41 + 22.21).
Method
Easy
Medium
Hard
Mean
Method
Easy
Mediam
Haid
Mean
ROID (WACV'20)
19.88108
40.47.144
12.48.26
34.28
Source
54.92.01
41.74 os
14:83:06
37. 16
* RDDubb (Neu
49.69108
40.05114
15.41a15
35.05
CMF (CLR
61.52s07
51.50=63
1.79-17
38.27
(Ours)
51.41108
42,80115
22.21112
38.1
C-MARIVVIRSM
51.15-11
41.48-19
2692423
40.52
REM (CML)25)
66.16203
$7.99-09
10.9799
45.04
CMF (CCR'22)
49.31
40.61#16
0.89206
30.27
ETA (CMU12)
45.07:104
33.71-4.6
1:22-05
26.67
RDumh (3
49.00+a8
39,88+15
15.04+19
59.99106
50.50=1.4
23:27
44.58
* ASR (Ours)
51,042a9
42,03:14
21.04
38.04
ASR (Quss)
60.58 01
$1.63 1.8
24:45
45.55
PeTTA (NemlPSS24)
36,89+22
22.64+28
6.00+0.8
21.84
ROID (WACV124)
60.85ea7
52.19
14.30ax2
42.45
RDDub (NeurIPS/23)
36.81122
22,39126
5.81
21.67
(NeuriPS
60.60:97
25.72:14
46.00
(Ours)
39.36423
23.51+255
7.27:0.6
23.38
ASR (Ouns)
61.48207
53.55-1.3
28.09
47.71
Table Evaluation of our general utility under var- ious objectives including EM and Eo.-EM.
Table 3: Acc. (%) comparison on VIT.
We further evaluate the generalization of our method using ViT-B-16 in Table 3. We compare only to baselines that report ViT performance in their original papers. While CMF (Lee & Chang, 2024) and REM (Han et al., 2025) achieve strong results on CCC- ACC-Easy/-Medum,, they eventually suffer from collapse on CCC-Hard. However, C-MAE (Liu et al., 2024a) validates its efficacy CCC-
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CCC
CIN-C
IN-C
IN-DI09
Method
Fasy
Medium
Hand
L.d.
non-Lidd
Visit 1/20
Meon
Visit 1/ 20
Mean
Source
35,89-002
16.87a02
1.27000
18.01:00
18.01
3,08 73.08
3.08=00
32.52/ 32.52
52.52s00
ETA (CMB)/2)
43.24:1.0
19.03.66
0.32
13.61
43.63104
335.80
15
41.24 34.21
12.
RoTTA (CVPR/2)
2.28:106
1.76006
0.69102
29.05=20
29.71
12.45 12.96
17.60=28
39.89/ 34,34
40.6143.1
VIDA (CCR 34)
12.68 108
1.75
0.42:00
17.76.10 m0
17.76101
3.09/ 22.84
2.99 0.
0.01 70.01
0.01:60
PALM (AMA22))
1,56402
0,74
0.13400
12,69.65
12,0846.1
30.70=14
13,86/1442
2,06427
EATA (I))U22)
49.52a09
30.19
0.82104
47.81
47.54.02
31.31
36.33
1..
/4..3
41,6110.3
COME (5LR
46.57433
36.63416
0.50
44.14:03
44,091003
30.20
/2,06
33.02
42,94/
44,91
45,11106
CoTTA
17.50.110
9.8
1.5210.5
35.51:26
35.291224
18.78 /37.22
34.39
41.761 40.55
13.91
SAR (CC.822)
37,94:12
22.25
2,03405
40.35:188
40.07:406
24.38 34,93
34.09-2.4
40.861 33.11
39.09
CMP R2))
49.31-19
40.61416
0.8910A
48.61 11.1
48.2
35.07/
/.9440
39.38-10
44,69 45,
40.25
PoTTA (NcutIP524)
36 36.89222
22.64
6,00+04
31,55=001
51.61+9.1
11.91 12.40
12.65=03
39,56 42.69
42.76
ROID (WACV124)
49.88:088
40.47+14
12.48:26
48.58.0.1
48.25
35.32/ 38.02
37.96:006
+ RDDumb (NouiIPS223)
19.59200
40.05=14
15.11a15
48.00:001
47.67
35.607 35.75
38:12
46.07
/45.62
45,99102
ReservoirTTA TTA (Neari 25)
50.86.1033
40.8910.8
13.71=10
47.34:01
46.96.10.1
31.54/ /37000
36.84:12
44.71 45.76
45.61
ASR (Clur)
51,41
42.80
22.21-1.2
49.50
49.1418.2
35.66 /42.96
41.56
46.137 46.32 46.49
Table 4: Comparison with state-of-the-art continual TTA approaches across four datasets using ac-
curacy (%). Results for each level of CCC (Easy Medium Hard) are averaged over nine variations, considering three different corruption orderings and three corruption evolving speeds. CIN-C results are averaged over ten runs. In the non-i.i.d setting, we use a Dirichlet parameter ō = 0.1, following prior works (Gong et al., 2022; Yuan et al., 2023). For IN-C and IN-DI09, we report averages the first and last (20th) visits, as well as overall averages across all visits. Gray denotes model collapse, indicating performance worse than that of the source model (Press et al., 2023).
Hard but does not generalize well the other levels. In contrast, our approach not only maintains strong performance on HCC-Hard but also achieves the best average performance.
b) CIN-C. Table 4 presents the results on CIN-C using the average accuracy over ten permutations, where corruption types are randomly shuffled. Baselines that achieve stable adaptation CCC also perform well on CIN-C. In particular, weight ensembling approaches (Marsden et al., 2024; Lee & Chang, 2024) demonstrate their effectiveness by achieving the top ranks among baselines. However, our method still yields the best performance on CIN-C, even though it is less prone to collapse. Most existing works assume label-ii.d. testing environments, but this assumption does not always hold in real-world applications. Recently, attention has been increasingly given to non-i.i.d. settings where labels are temporally correlated. Following Gong et al. (2022); Yuan et al. (2023), we use Dirichlet parameter d = 0.1 to control the test class distribution. Table 4 shows that our approach achieves the best adaptation performance, indicating that our reset scheme can properly determine the timing and scope of reset even under non-i.idd settings. Further non-i.i.d. results are shown in Fig. 7.
c) IN-C. We report the average accuracy over corruptions at the first and last (20th) visits, as well as the overall average across all visits for IN-C in Table 4. Most baselines succeed in avoiding collapse and achieve substantial improvements over the source model. Using IN-C, our approach proves that is effective not only in preventing collapse but also in enhancing model adaptability by getting the best performance consistently across the first, last and overall visits.
d) IN-D109. The results for IN-D 109 are reported in the same manner as IN-C, as shown in Table 4. Several baselines experience performance degradation when comparing Visit and 20. This implies early collapse that might be due to the reduced number of classes. IN-D109 has only 109 classes, roughly 10x fewer than other datasets. Consequently, skewed prediction distribution is more easily observed than in other datasets. However, our method still yields stable and superior performance.
4.3 ABLATION STUDY
We ablate each component from our approach to validate its individual effectiveness. Table 5 shows that dynamically determining when and where to reset is the most significant factor, as reflected in the top two ablated results. First, we replace our adaptive reset with a fixed- interval reset using T 20000, where Ho is omitted since C, in Eq. (1) is no longer com- puted. Second, we ablate our selective reset by applying full-parameter reset with r; = 1.0 in Eq. (3). The remaining components in Eqs. (4), (6), and (7) individually have limited impact but yield meaningful gains when combined. For ablated variants that remove the adaptive coefficients, we fix the corresponding hyperparameters to constant values. When Ag is removed, ' is set to 5.0, and when 44 is removed, Pec is set to 0.995. More ablation study results are provided in Appendix C.
G (3) F())
Ja (6)
Hi (7)
Easy
Medium
Hard
Mean
49.74
40.19
11.81
33.91
49.83
40.58
17.16
35.86
49.83
40.35
15.99
35.39
51.07
42.33
20.27
50.82
41.86
20.70
37.79
51.19
42:42
21.36
38.32
Table 5: Effeect of components in ASR on ROID
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Published as a conference paper at ICLR 2026
CCC-ard
CCC- CCC-Medium
Acc. (56) top: CCC-Hard bettom: OCC-Easy
20.99
40
41.13
39.09
22
2009
21.02
Acciracy (%%)
ROID ASR
15
20
10
52
1507
25:46
5
10
2032
50.26
0.69
A0g11000)
tog1900)
40g(800)
2i co)
(urss)
Aeg. Var(Logit Scales)
Threshold for True Positive Reset
Figure 4: (11) vs. (())).
Figure 5: Robust to Var( logit)). Figure Effect of False-positive
6=0.1
6=0.01
6 0.005
50
48.46
49.37
39.62
40.27
39.77
41.72
33.17
33,91
48
47.31
47.64
34.41
26.36
46.21
Acc.
96
30
20
24.47
44
10
9.48
20
ETA
+RDumb
+ASR (Ours)
ROID
#RDum
ASR (Durs)
Figure 7: Comparison of ETA/ ROID and their variants with RDumb and ASR over different Dirich- let parameters ó on non-i.id. CIN-C. The lower the 6, the more imbalanced the label distribution.
4.4 ANALYSIS
a) Empirical analysis Softmax (Mean (Logits)) Model collapse arises from errors accu- mulated over a long period of time, which severely degrades model performance. A key charac- teristic of collapse that the model predicts only few classes for all inputs. However, detecting collapse is non-trivial. This is because true labels are inaccessible at test time, making it impossible to determine whether the model produces skewed predictions due to collapse or whether the data itself contains skewed classes. As discussed in Sec. 4.4(b), we find that accurately detecting true collapse is not strictly necessary. Even when collapse is falsely detected, triggering a reset based on prediction bias still facilitates effective model adaptation. Accordingly, we focus on analyzing how to effectively measure bias in the predictive distribution. Although Mean (Sc (Softm famax (Logits) ) would be one of the most straightforward metrics for measuring prediction bias, we instead employ Sof tmax (Mean (Logita t ). By being more sensitive to large-magnitude logits, this approach ensures that the dominance of high- cotfideene predictions (Wei al., 2022) the primary drivers of model updates and collapse- is preserved and not diluted by lower-confidence predictions. This pro- vides clearer signal of prediction bias, enabling more reliable detection of model collapse. Fig. shows that So fftmax x (Mean iss maintains stable adaptation even under highly collapse- prone conditions, whereas Mean (Softmax (Logits) fails, achieving only 0.69% accuracy on CCC-Hard. Moreover, we empirically show that large variance in logit magnitudes across samples within a batch does not pose a significant issue for Softmax (Mean (Logits) ). This is important because it demonstrates that So f tmax (Mean (Logits) is not dominated by few samples with extremely large logits. We artificially manipulate logit-magnitude variance through transformation and evaluate the robustness of So ftmax (Mean (Logits) under increasingly ex- treme variance, as shown in Fig. Even when the variance increases by more than 15x, accuracy remains nearly unchanged (< 1%p on CCC-Hare Hard and <0.3%p on CCC-Easy). For this experiment, we use a single split (transition speed 1000; random seed 43) with ROID (Marsden et al., 2024).
sample
b) Risk of false-positive reset. One may question "whether approach still works well under label imbalance, where predictions are normally highly skewed." Imbalanced priors, however, do not disrupt our method. As model predictions become more skewed, C- the moving average of past prediction bias-increases accordingly, providing baseline. A high risk of collapse is then detected only when is significantly higher than this baseline. Thus. C,-1 serves as a reliable reference, making our reset mechanism robust against label imbalance. as empirically shown in Fig. 7 under various imbalanced settings. A related question is "whether our approach remains effective when C, temporarily spikes due to extremely label- imbelanced inputs under i.i.d. class priors." We argue that a reset is generally desirable whenever C. Ch current prediction bias-becomes high, since any high C, produces biased update signals that can lead the model to converge to suboptimal solutions. To test this, we conduct a controlled experiment using a single batch with uniformly distributed labels, ensuring that any triggered reset is nue-posirive. For each reset, C, is computed for the batch as in Eq. (1) and compared against a pre-defined threshold, initialized as described below Eq. (2). Raising the threshold increases true-positive resets while reducing posetioo resets
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Published as a conference paper at ICLR 2026
CCC-Basy
Original
Gain (%)
Modified
Guin (%)
CCC-Hard
Original
Gain (%)
Modified
Gain (%)
ETA
43,46
43.17
ETA
0.41
1.83
+ RDumb
49,53
+1399%
47,36
+9.7%
RDumb
9.46
+2007%
11.88
+549%
+ ASR (Ours)
51.27
+17.9%
51.15
+ +18.4%
ASR (Ours)
15.95
+3790%
17.61
+862%
ROID
49.95
49.54
ROID
9.63
16.51
+ RDumb
49.76
-0.3%
49,33
-0.4%
RDumb
14.03
+45.6%
15,99
-311%
+ ASR (Ours)
51.47
+3.0%
51.46
+3.8%
ASR (Ours)
21.22
+120%
21.56
+30.5%
Table 6: Acc. (%) of original and modified CCC-
Table 7: Acc. (%) of original and modified CCC-
Easy using seed 43. Gains (%) are relative to each Hard using seed 43. Gains (%) are relative each
corresponding baseline (e.g.. ETA or ROID). corresponding baseline (e.g., ETA or ROID).
x: Revisit (#) y: Acc. (%)
top: (standard)
bottom: 6 10 (more dynamic)
89:54
R0.89
99:75
40
36 36.91
34
36
34.22
28
74
39.29
B4.27
37.04
36.76
34:37
36.60
34.67
35, 91
34.23
II
11
5
10
11
12
13
15
16
17
20
Caussian
Shot
Impuise
80 89.83
82.00
63.88
#3.52
81
40
88:77
36
36
35 66
35.0
37.01
25 B8
26 PO
32
34.35
30
12
16
20
Figure 8: Performance comparison over revisits on IN-C with CDC settings. Domains dynamically change over time, controlled by 6, and are color-coded to visualize their dynamics.
caused by temporarily high C, in correctly adapting model. Fig. 6 shows that encouraging false- positive resets by lowering the threshold improves performance, thereby confirming that interrupting biased updates-even when the model appears to adapt correctly-help maintain robust long- term adaptation. For this experiment, we use a single split of CIN-C, corresponding to the first split in Table 32, with ROID our baseline.
c) Dynamically changing corruptions: variant of CCC. As discussed See. 3.2, real- domain shifts rarely follow a fixed schedule. Nonetheless, we adopt fixed domain-shift intervals in our benchmarks to maintain consistency with previous studies and enable fair comparisons. To further evaluate robustness under more realistic conditions, we introduce a modified CCC vari- ant, dynamically changing corruptions, where the duration of each corruption is randomly sampled from {1000, 2000, 5000) batches. Unlike the original CCC, where each corruption persists for fixed length, our variant introduces a stochastic transition schedule that more closely reflects the unpredictability of real-world data streams. Tables 6-7 present comparison between the original CCC and our modified variant. In CCC-Easy, the performance gains observed in the original CCC are consistently reproduced in our modified variant across all methods. However, in CCC-Hard, RDumb shows degraded performance on ROID under our modified variant. This suggests that a fixed reset schedule can hinder adaptability when corruption transitions are both challenging and unpredictable. In contrast, our adaptive reset schedule consistently preserves performance gains across both benchmarks, demonstrating its robustness to such temporal unpredictability.
d) CDC setting for dynamic domain-shift schedule. We demonstrate the robustness of our ap- proach under dynamic domain shifts by applying the Continual Dynamic Change (CDC; Zhang al. (2025b)) protocol to IN-C. This protocol explicitly introduces rapid domain switching and stochas- tie domain durations, controlled via the Dirichlet parameter 5. We evaluate our approach under both a standard setting (8 = 1.0) and a more highly dynamic setting (Ō = 10.0) to further emphasize our robustness. As shown in Fig. 8, RDumb at = 1.0 exhibits recurrent drops in accuracy; for instance, its accuracy falls from 36.91% to 34.00% at the fourth transition. In contrast, ASR main- tains robust performance and steadily improves over time, rising from 35.08% to 41.34% across 20 transitions. A similar trend is observed at 8 10.0, where RDumb undergoes repeated performance degradations, whereas ASR remains resilient and gradually improves, reaching 41.68% by the 20th transition. Full evaluation results under these CDC settings are provided in Appendix D.I.

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Published as a conference paper at ICLR 2026
(a) ETA
(b) RDumb
(c) + ASR (Ours)
1.00
Class Ratio
0.75
0.50
0.25
0.00
25
50
75 100
25
50
75
100
25
50
75
100
Test Batches (x10*)
Test Batches (x10²)
Test Batches (x10*)
Figure 9: Histogram of predictions
CCC-Hard for randomly selected ten fixed class labels, com-
paring ETA, RDumb, and ASR to evaluate robustness against model collapse. Results are measured every 10 batches, with class labels color-coded consistently.
Accuracy (%)
ASR (Qurs): 36.72%
RDumb: 88%
40
40
Accuracy 196)
35
45
40
60
100
30
30
ROID
RDumb
ASR (Ours)
25
59.0
59.5
60.0
60.5
61.0
25-128
64
Test Batches (x10")
Batch Size
Figure 10: Comparison of ASR and RDumb on ETA. Accuracy (%) is shown from a global per- spective (top) up to 110K batches, and at finer temporal scale (bottom) from 59K 61K batches.
Average accuracy (%) of ROID, RDumb, and ASR across all CCC levels under varying batch sizes from 128 down to 16.
Figure 11:
e) Collapse analysis. We track predictions for ten fixed classes over time to analyze model collapse, as shown in Fig. 9. Experiments are conducted on CCC-Hard under the standard assumption of uniform class distribution. We employ ETA (Niu et al., 2022) as baseline due its high susceptibil- ity to collapse, ensuring clear comparative analysis. Fig. 9 reveals that while ETA initially predicts a diverse set of classes, its prediction diversity abruptly diminishes, eventually failing to assign any of the ten monitored class labels. While RDumb (Press et al., 2023) helps mitigate such failure, its class distribution remains unstable and biased to some extent. In contrast, our method demonstrates superior resilience against collapse, consistently maintaining a uniform class distribution.
r) Stability over time. Beyond overall quantitative results, we examine whether our approach main- tains stability over time, it is vital for reliable real-world deployment. Fig. 10 illustrates accuracy trends for RDumb and ASR on the ETA model?. We compute the average accuracy at each step over 10 batches across all CCC levels. ASR consistently outperforms RDumb from global perspective (top), and its stability evidenced by its significantly smaller performance fluctuations compared to RDumb when viewed finer temporal scale (bottom).
g) Robustness to batch size. We evaluate the robustness of our approach with respect to batch size, as shown in Fig. 11. We report the average accuracy across all CCC levels while varying the batch size from 128 down to 16. Although performance decreases with smaller batch sizes as expected, our method degrades more gracefully than ROID and RDumb. It consistently outperforms others across all sizes and shows the potential to scale more effectively beyond a batch size of 128. While RDumb excels primarily at its default size (64), our method maintains stable, improved performance regardless of the size. For single-sample inputs, this challenge can be addressed by accumulating samples over time and adapting once a sufficient count is reached, following Gong et al. (2023); Niu et al. (2024). Results for extremely small batch sizes (below 16) are provided in Appendix D.2.
5 CONCLUSION
In this paper, mitigate model collapse in long-term TTA via Adaptive and Selective Reset (ASR), combined with importance-awee aware knowledge recovery and on-the-fly fly adaptation adjustment. Exper- imental results demonstrate the effectiveness of proposed method across long-term TTA bench- marks, particularly in challenging settings. Specifically, our method outperforms the state-of-thh-ard by 42% on HCC-Hard. We hope that our work motivates further exploration into advanced reset algorithms for robust and stable adaptation over the long term, while preventing model collapse.
"Two methods identical at 0, but the initial point in Fig. (top) averages [0, 999).
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) of the Korean government (RS2024-00341749)
and Institute of Information Communications Technology Planning & Evaluation (ITTP) grant funded by MSIT (RS-2022-11221124 RS-2023-00259934, 242000222222200022222222222222,2 RS-2025-022830488)
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A ADDITIONAL DETAILS ON ASR
A.1 LIMITATIONS OF PULL-PARAMETES RESET
a) Performance drop. To discuss the limitations of full-parameter reset, we first quantify RDumb's performance degradation after reset under the same setup as in Fig. 1. For each reset, we compute the average accuracy over 10 batches immediately before and after the reset. We then take the difference between these two averages and report the mean difference across all resets. RDumb has detrimental resets, which are around 40% of the total resets. While beneficial resets yield an average improvement of +..32%p. detrimental resets cause an average degradation of -1 11.55%p. which is 2.67 larger and moreover exceeds RDumb's overall average accuracy of 9.77%. In contrast, while our approach has detrimental resets, which are around 38% of the total, their average performance degradation is only -2.50%p. which is 4.62 less than that of RDumb.
b) Recovery delay. When performance drops after reset, count how many batches are required to reach the maximum accuracy that has been achieved within the 10 batches before the drop. When the model fails reach it until the next reset, set the recovery delay the reset-to-reset interval (e.g., 1000). RDumb requires an average of 214 batches, indicating that recovery occupies 20% of the total adaptation steps, as reset occurs every 1000 batches. RDumb requires a substantial amount of time to recover performance drops after reset, underscoring the inefficiency of full-parameter reset. When performance drops, our method needs average of 32 batches, which 6.68 fewer than RDumb.
A.2 MORE DETAILS ON PREDICTION CONCENTRATION
To compute the correlation shown Fig. 3, we employ ETA as TTA baseline and CCC-Hard as a benchmark, they exhibit clear collapse behavior and thus suitable illustrate the relationship between collapse and prediction concentration C, Fig. doesn't 't encode temporal information; thus, points the right do not necessarily represent later time steps.
One may question whether the pattern in Fig. 3 could be an artifact of the logit averaging in Eq. (I). To address this, we recompute C, after eliminating the largest-magnitude logit in each batch, as well as after eliminating the top 10% of logits by magnitude. We then report the Pearson correlations in Table 8. Although these correlations are lower than the original value of 0.88 (see Fig. 3), they are still substantially strong. This indicates that the influence of extremely targe-magnitude logits is limited, and that the pattern in Fig. cannot be explained solely as an artifact of logit averaging.
Excluded logits
Pearson correlation
None
0.88
Top-1
0.85
Top- 10%
0.77
Table 8: Correlation between C, and accuracy after eliminating large-magnitude logits.
Moreover, as model approaches collapse, its predictions increasingly assign large logit values to small set of dominant classes, leading to an increase in logit magnitude. Finally, this explains that the pattern in Fig. 3 is attributed to overall logits rather than only few extreme outliers.
A.3 RISK OF PROXIMITY TO RESET
Being close to a reset can compromise parameter integrity, which negatively impacts adaptation. We empirically illustrate this phenomenon by delaying the reset point, allowing corrupted parameters to accumulate in (Eq. 4). Under recurring settings (IN-C), we observe harmful effects when corrupted information is reused. In the standard protocol, reset is triggered when C, C,-1. For variants, a reset delayed until C. - - 1 > e, retaining adapting parameters beyond the standard reset point. Table 9 shows that delaying reset results in substantial performance degradation, even below ETA, confirming that parameters are vulnerable to corruption near the reset point and that such corruption severely impairs adaptation.
A.4 MORE DETAILS ON KNOWLEDGE ACCUMULATION
We accomplish knowledge recovery by regularizing model parameters using their accumulated val- ues and their estimated importance, described in Sec. 3.4. However, special attention is required during the accumulation phase, as a trade-off arises: improving representation for the current domain increases vulnerability comruption due the accumulation of emror over time.
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Revisit #
5
Mean
ETA
30.64
36.76
36.16
35.96
35.80
35.88
w/ delay
0.001
28.42
33.39
37.80
38.82
39.02
36.25
+ w/ delay
0.01
27.94
28.06
27.94
28.30
28.12
28.29
ASR (Ours)
28.68
33.00
38.60
38.97
39.10
36.90
ROID
35.32
38.00
38.08
38.15
38.02
37.96
w/ delay
0.001
35.08
40.01
41,42
41.96
41.54
40.85
w/ delay
0.01
35.60
37.78
38.28
38.61
38.62
38.07
ASR (Ours)
35.66
41.03
42.20
42.06
42.96
41.56
Table 9: Accuracy (%) under the standard and delaying reset settings on IN-C. Here, e denotes the threshold for delaying reset; larger e value encourages stronger reset delaying.
To tackle this problem, we propose hybrid accumulation scheme that combines cumulative moving average (CMA) and exponential moving average (EMA). At every step, we use CMA to accumulate parameters 8:-1 and squared loss gradients with respect each parameter. which correspond to the diagonal of the Fisher information matrix, follows:
2
((B,;00)))))
(L - 1 (atest) F/-11 + (Ve:_, C(Be;0e-11
F
(8)
laaest
1 Intest)
02 1 + 0 04_
latest
where fattest represents the latest reset point prior to t, and Fi and 8 denote the CMA-accumulated Fisher information matrix and parameter for 6', respectively, both initialized to zero values at f = 0. We then aggregate these CMA values at every reset using EMA, as follows:
F FF. Fi g + 4o
+ F%
(10)
(40) -
(11)
where fAr and [g are the momentum coefficients, both pre-defined as 0.9, and Fi and 0 denote the EMA-accumulatedd Fisher information matrix and parameter for 0', respectively, both initialized to zero. After the EMA updates, Fi and 0f are re-initialized to
A.5 COMPUTATIONAL EFFICIENCY
We compare TTA baselines, ASR, and our ablations in terms of the number of trainable/total param- eters, computation time (seconds per batch), and the average accuracy (%) across all CCC levels as shown in Table 10. Parameter restoration methods (i.e., ROID, RDumb, and ASR) require additional memory to store the initial model state, and the overhead for our extra parameters (primarily Fisher information) is negligible compared to the model size of 25.5M.
Each size of f and 0 is [@). Each size of F and is also [0), as they retain only the diagonal elements, following standard practice in EWC (Kirkpatrick et al., 2017). 7). result, each these components occupies only 0.025M parameters (0.098% % of the total). ASR computes Fisher information once per batch (size 64), increasing less than 0.001 seconds per batch. Overall, the computation and memory overhead of our additional parameters is minimal, making our method highly efficient in practice.
Method
# Trainable
# Param
Time
Acc.
ETA
53.1K
25.5M
.083
21.72
ROID
53.
51.1M
.125
33,91
+ RDumb
53.1K
51.1M
.125
35,39
+ ASR (Ours)
53.1K
51.2M
.200
38.32
+ w/o recovery (Sec. 3.4)
53.1K
51.1M
.200
37.80
+ w/o on-the-fly (Sec. 3.5)
53.1K
51.2M
.181
37.89
Table 10: Computational efficiency on CCC. Trainable indicates the number of learnable parame-
ters; # Param the total number of parameters; Time the seconds per batch (64 samples); and Acc. the average accuracy (%) across all CCC levels.
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A.6 EFFECT OF KNOWLEDGE RECOVERY
Our proposed regularizer recovers essential knowledge that would otherwise be lost due to reset. We can explain this effect using the following two mechanisms from a theoretical perspective. First, we accumulate adapted parameters through combination of CMA and EMA. It allows us to preserve historical adaptation information in a way analogous to Polyak averaging (Polyak & Juditsky, 1992), which provides reliable information archive. Second, our Fisher-based regularization term follows the principle of Elastic Weight Consolidation (Kirkpatrick et al., 2017), 7), giving stronger penalties to parameters that are more important in prior domains. Together, these mechanisms encourage crucial parameters to stay close to their pre-reset states, thereby effectively recovering essential knowledge. The knowledge recovery module introduced in Sec. 3.4 aims to effectively restore information that would otherwise be lost due to the reset. We evaluate its effectiveness under the same setup as Table 33 by measuring how much knowledge from prior domains is recovered. We define knowledge recovery the average of per-domain gaps between the current, performance and the best previously achieved performance. Positive values indicate successful recovery, while negative values indicate forgetting. As shown in Table I1, our approach consistently recovers substantial knowledge without forgetting. At the 10th revisit, ETA+ASR achieves +0.58 compared to -1.94 (w/o recovery), while ROID+ASR achieves +0.16 16 compared to -0.52 (w/o recovery).
Here, knowledge refers to information encoded in model parameters during adaptation, correspond- ing to 8 in Eq. (4). The essential knowledge is identifiable through Fisher information that highlights parameters that are highly informative about prior domains. Because directly quantifying knowledge is inherently challenging, we use task performance as a proxy for its assessment.
Revisit #
10
15
20
Mean
ETA + ASR (Ours)
0.00
+0.58
+0.08
+0.02
+0.24
+ w/o knowledge recovery
0.00
-1.94
-1.16
-0.76
-0.56
ROID + ASR (Ours)
0.00
+0.16
+0.24
+0.01
+0.12
+ w/o knowledge recovery
0.00
-0.52
-0.42
-0.10
-0.14
Table 11: Knowledge recovery measured across multiple revisits on IN-C.
In addition, evaluate the effectiveness of our knowledge recovery using accuracy the evaluation metric. We assume domain- recurring settings by using IN-C as a benchmark to test whether models effectively preserve what they have learned. We compare our method with our variant that climinates the knowledge recovery module (Sec. 3.4). As reported in Table 12, our variant without the recovery module exhibits gradual accuracy decline across revisits, while method maintains the superior performance. These results demonstrate that the recovery module effectively mitigates forgetting of knowledge acquired from prior domains.
Revisit #
10
15
20
Mean
ETA
30.64
36.16
35.96
35.80
35.88
+ ASR (Ours)
28.68
38.60
38.97
39.10
36.90
+ w/o knowledge recovery
28.64
37.45
36.49
36.34
36.56
ROID
35.32
38.08
38.15
38.02
37.96
ASR (Ours)
35.66
42.20
42.06
42.96
41.56
+ w/o knowledge recovery
35.35
41.64
41.64
41.19
40.96
Table 12:
Accuracy (%) comparison across multiple revisits on IN-C.
The benefit of knowledge recovery may appear negligible as experiments in Table 12 are conducted under easy-to-adapy settings. However, it does not imply that knowledge recovery is also ineffective in challenging adaptation scenarios. Although Table 12 proves that the knowledge recovery module functions as intended, IN-C is not a suitable benchmark for measuring the performance contribution. As discussed in Sec. 3.5, under challenging conditions, the regularization coefficient 'y increases to encourage reuse of prior-domain information, thereby amplifying the impact of knowledge recovery. We thus consider CCC-Hard to better illustrate the contribution of the knowledge recovery module. Across several splits (e.g., a.g.... 4, 7, and 8), eliminating this module substantially degrades performance,
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whereas the full model consistently maintains higher aceuracy. These results confirm that the module makes a clear performance contribution under challenging adaptation settings.
Split
3
4
5
6
7
8
ROID
12.64
15.79
13.28
9.63
12.65
11.81
10.66
8.72
17.12
ASR (Ours)
20.99
22.51
20.40
21.22
22.93
21.36
22.37
23.84
24.25
w/o recovery
20.95
22.50
20.35
18.28
22.69
20.18
9.70
15.67
23.57
Table 13: Accuracy (%) comparison across splits in CCC-Hard.
A.7 HYPERPARAMETER SENSITIVITY
Since the validation set is unavailable in TTA, optimal hyperparameter tuning is inherently challeng- ing. Instead, we tune hyperparameters using only 5% of single holdout set CCC-Hard (transition speed 2000: random seed 44). We further validate that our approach is insensitive to hyperparameter changes, as illustrated in Fig. 12. Specifically, we evaluate performance across CCC levels while slightly perturbing the tuned values; the default settings are described in Table 14. The performance fluctuations observed in Fig. 12 are negligible. Finally, the capability to use set of hyperparameters across all benchmarks underscores the practicality of our approach.
ag (Below Eq. (2))
Ay (Eq. (3))
Åo (Eq. (6))
Ho (Eq. (7))
Ecy-Easy
Accuracy (%)
51.4
51.2
51.0
0.05
0.10
0.15
0.20
CCC- CCC-Medium
Accuracy (%)
42.6
42.4
42.2
0.3
0.4
0.5
0.6
10
20
30
40
5
10
20
0.05
0.10
0.15
0.20
Accuracy (%)
22.0
CCC-Hard
21.5
0.3
0.4
0.5
0.6
10
20
30
40
2
10
20
0.05
0.10
0.15
0.20
Figure 12: Hyperparameter sensitivity analysis showing robustness variations around the default.
A.8
IMPLEMENTATION DETAILS
The detailed hyperparameter settings are summarized in Table 14.
A.9
ALGORITHM
The complete workflow of ASR summarized in Algorithm 1.
A. ASR UNDER ABRUPT DOMAIN CHIANGES
Gan et al. (2023) observe that prediction confidence changes rapidly following domain shifts. Simi- larly, we find that prediction concentration exhibits abrupt dynamics with domain changes, as illus- trated in Fig. 13. Since ASR depends prediction concentration, it is essential to consider whether such abrupt behavior negatively impacts it. Abrupt declines in prediction concentration may appear to indicate random predictions. However, is not severe enough cause truly random behavior. In
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Hyperparameter
Reference
ResNet-50
ViT-B-16
Agi Initialization factor for C,-1
Below Eq. (2) in Sec. 3.3
0.5
5.0 x 10
Pe: EMA update momentum for C--1
Eq. (2) Sec. 3.3
0.995
0.995
To: Minimum reset proportion
Eq. (3) in 3.3
0.5
0.5
A,: Reset proportion scaling factor
Eq. (3) in Sec. 3,3
20.0
0.1
AF: Fisher regularization coefficient
Eq. (4) Sec. 3.4
5.0
5.0
Ag Initialization factor for λ
Eq. (6) in Sec. 3.5
5.0
5.0
Ho: Initialization factor for pcc
Eq. (7) in See. 3.5
0.15
1.0 x 10-3
Table 14: Hyperparameter settings for ResNet-50 and ViT-B-16 used across all benchmarks.
Algorithm 1: Adaptive and Selective Reset (ASR)
Input: Test batches {B, | adapting model fo. source model fog, cumulative concentration initialization factor Cgg, regularization coefficient initialization factor Xo EMA update momentum initialization factor μo. minimum reset proportion To. reset proportion scaling factor Xy, and EMA update momentum parameters ((ر,,مa)]
Initialize Co + - log(an C), Fo t 0, and 8 + 0;
for 1 (1....,7 T) do
/1 1) Model Adaptation
Generate logits 2; fa._, (B:);
Compute loss L(B,; 0(-)) in Eq. (4);
// CMA-based
Knowledge Accumulation
Update F. and 0, via Eq. (8) and Eq. (9);
Update 0e + im L(B,; 0-1)):
/1 2) On-the-fly Adaptation Adjustment:
Compute prediction inconsistency Φ: = [5 | max(()))))) + arg max
where i/ o (Jo, and of =z()):
Adjust regularization coefficient λ λo of
and momentum coefficient μcc = Oz +1- 40:
// 3) Adaptive and Selective Reset
Compute prediction concentration C. D p. log(ā..) where P. = -16/) E. 2f):
if - < 0 then
Update C. < pCc C. (1-Pe) pc) Cii
end else
Compute selective reset factor = rn + X, (C, C.-1) where E [ro. 1):
Reset only the last rt proportion of total layers; Initialize C2 +- - log(ao C);;
//
EMA-based Knowledge Accumulation
Update F (MA Fe, F
pr) Eq. (10);
Update + EMA(O, 0,,,0.,0 0,, μa) in Eq. (11);
Initialize f. 0 and 0, + 0:
end end
contrast, abrupt rises in prediction concentration often lead to C, > C.11. unintentionally triggering resets. Zhang et al. (2025b) note that negative knowledge transfer may occur with domain shifts and should be mitigated. Finally, such unintended resets can act as a safeguard against negative transfer. Overall, abrupt dynamics in prediction concentration with domain shifts do not pose risk to ASR.
B MORE DETAILS ON DATASETS
In this paper, we evaluate the stable adaptability continual TTA methods under four long-term adaptation benchmarks, which designed to induce model collapse.
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d1
d2
d3
44
96
d7
dg
dg
d1o
d11
d12
d13
d24
d1s
Pred. Concentration
6.4
6.5
-6.6
6.77
Time
Figure 13: Prediction concentration in Eq. (1) over time under 15 corruption types in CIN-C. Dashed vertical lines (red) indicate corruption boundaries. Colored ellipses highlight abrupt dynamics asso- ciated with domain shifts yellow abrupt decline; green
abrupt rise).
1) Continually Changing Corruptions (CCC) was introduced by RDumb (Press al., 2023) and is constructed from the ImageNet-C dataset. CCC converts abrupt corruption transitions of ImageNet- into smooth transitions by splitting integer corruption levels into continuous values from 0 in steps of 0.25. During the smooth corruption transition, one fades out (c.g.. 0.5 0.25) and another fades in (c.g.. 0.5 - 0.75) with the two overlapping. How these two corruptions move is determined by the source model's accuracy (0%-Hard 20%-Medium / 40%-Easy). The "0%-Hard" setting results in the strictest corruption transitions. Moreover, each mode consists of different corruption types, summarized in Table 15. Transition speed is defined as the number of images per step of the transition path and has three settings (1000 / 2000 5000). The ordering of corruptions in the path has three variants, which combinations of 3 paths x 3 speeds x 3 orderings, and each combination contains approximately 7.5M images. Lastly, CCC has several factors contributing to model collapse, including prolonged time steps (Wang et al., 2022), similar corruption difficulty (Press et al., 2023) and corruption repe- tition (Hoang et al., 2024).
determined by random seeds (43 /44 45).. CCC has total of 27
Level
Corruption Types
Easy
Gaussian noise, Shot_noiss se, Tmpulse now.sennoisse noise, Contrast
Medium
Gaussian.noise, Shot.noise, Impulse.noise, Defocus.blur, Glass blur, Motion blur, Zoom blur, Snow, Frost, Fog, Contrast, Elastic, Pixelate
Hard
Gaussian Shot Impulse.noise, Glass.blur, Motion.blur, Zoombburr Snow, Frost, Fog, Contrast, Elastic, Pixelate, JPEG
brocuss.burr,
Table 15: Corruption types in a transition path for each adaptation difficulty.
2) Concatenated ImageNet-C (CIN-C) consists of image samples of the ImageNet-C C validation set including 15 corruption types- Gaussian noise, Shot noise, Impulse noise, Defocus blur, Glass blur, Motion blur, Zoom blur, Snow, Frost, Fog, Contrast, Brightness, Elastic, Pixelate, and JPEG-at at the highest severity level (level 5). CIN-C has 50K images for each corruption type, 10x larger than the original ImageNet-C.
Sypes-Gauussian
3) ImageNet-C (IN-C) is processed to evaluate stability against model collapse. This includes only four corruption types level 5: Gaussian noise, Shot noise, Impulse noise, and Contrast. These are selected such that the source model achieves less than 10% accuracy, ensuring consistent adaptation difficulty across corruptions. Each corruption has 5K images, and the four corruptions are repeated 20 times, resulting in total of 400K images, which represents corruption repetition, one of the collapse contributors. The corruption ordering is fixed as Gaussian noise + Shot noise - Impulse noise Contrast.
4) ImageNet-D109 (IN-D109) is processed to evaluate stability against model collapse. It includes only four mains such that the source model achieves less than 50% accuracy, ensuring consistent adaptation difficulty across domains. The domain sequence follows the fixed ordering Clipart - Inforrph - Painting ++ Sketch and is repeated 20 times. Finally, IN-D109 includes only the classes shared with the DomainNet dataset, resulting in total of 109 classes.
Infograph, Painting, and Sketch-selected from the six available do-
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C ADDITIONAL ABLATION STUDIES
C.I EFFECT OF ADAPTIVE RESET
We evaluate the effectiveness of our adaptive reset scheme by comparing it with variants using fixed reset intervals. Table 16 shows that adaptive reset effectively identifies when the model is on the verge of collapse and determines appropriate reset timing, leading to improved performance.
Reset interval
Easy
Medium
Hard
Mean
Fixed
T = 1000
15.96
5.91
1.10
7.66
T = 10000
49.88
39.69
15.75
35.11
T = 20000
49.83
40.58
17.16
35,86
7' = 50000
49.90
40.16
14.26
34.77
Dynamic
ASR (Ours)
51.19
42.42
21.36
38.32
Table 16: Comparison with variants using fixed reset intervals T in terms of accuracy (%).
C.2 EFFECT OF SELECTIVE RESET
Table 17 evaluates the effectiveness of our selective reset by comparing it with erod-prroootiion reset variants. Among the variants, resetting the latter half layers yields the best performance. Our reset also consistently initializes the reset proportion at 50% (i.e., ra = 0.5). Consequently, our selective reset effective and resetting least 50% of layers is important mitigate accumulated errors.
Reset target
Easy
Medium
Hard
Mean
Fixed
20%
49.27
40.01
16.83
35.37
50%
50.91
42.07
20.80
37.93
80%
50.72
41.40
19.68
37.27
100%
49.83
40.35
15.99
35.39
Dynamic ASR (Ours)
51.19
42.42
21.36
38.32
Table 17: Comparison with our variants resetting a fixed % of layers closer to the output.
C.3 FAIR COMPARISON FOR RESET
We compare our reset to existing reset schemes proposed by SAR (Niu et al., 2023), RDumb (Press al., 2023), and DA-TTA (Wang al., 2024) across all CCC levels using ROID baseline, as shown Table 18. Existing schemes either reset all parameters periodically (RDumb) or reset based on extremely high prediction confidence (SAR), or reset based on significant distribution shifts from the source domain (DA-TTA). For a fair comparison, our method ignores other components that are directly unrelated to reset; otherwise, achieves $1.19 /2.42 21.36% on CCC-Easy -Medium / -Hard, respectively. Except for SAR, all existing schemes improve performance on CCC-Hard but degrade on easier levels. However, ASR consistently outperforms all other schemes, surpassing the second-best result by +2.80%p average.
Method
Easy
Medium
Hard
Mean
ROID
49.74
40.19
11.81
33.91
+ SAR
49.73
40.06
5.29
31.69
+ RDumb
49.56
39.77
14.06
34.46
+ DA-TTA
45,98
35.76
15.53
32.42
+ ASR (Ours)
50.70
41.72
19.36
37.26
Table 18: Accuracy (%) comparison across various reset schemes on CCC.
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C.4 EFFECT OF HYBRID KNOWLEDGE ACCUMULATION
In our hybrid knowledge accumulation scheme, EMA is applied top of CMA, CMA emphasizes local past information, mitigating the influence of recent parameters near collapse, whereas EMA globally weights more recent information, better reflecting distribution shifts. Table 19 compares our hybrid scheme with CMA-only baseline, reporting accuracy (%) across all CCC levels.
Method
Easy
Medium
Hard
Mean
CMA-only Hybrid (Ours)
50.03
41.13 42.42
18.42
36.53
51.19
21.36
38.32
Table 19: Hybrid knowledge accumulation (Ours) vs. CMA-only baseline.
C.5 OPTIMALITY OF REPARAMETERIIATTON
We assess the optimality of reparameterizztioo scheme in Eqs. (7). To design reparame- terization, we use only 5% a holdout set from CCC-Hard (transition speed 2000; random seed 44) and determine the expression that balances simplicity and performance. As shown in Tables 20-21, we compare our expression with alternative expressions across all CCC levels. Across Tables 20-21, two candidate expressions exhibit comparable performance. Finally, we select the one that performs better on CCC-Hard, as it more effectively lowers the risk of poor adaptation in real-world scenarios.
Af
Range
Easy
Medium
Hard
Mean
Au
{)o
51.07
42.33
20.27
37.89
00, 1
51.09
42.26
20.56
37.97
(1 x2)2
[xo.0]
51.15
42.34
20.42
37.97
Ao -0
[0, Ao
51.16
42.40
21.27
38.28
Ao of
[0, Aol
51.19
42.42
21.36
38.32
Table 20: Comparison with different expressions for Aye across all CCC levels using accuracy (%).
Pe
Range
Easy
Medium
Hard
Mean
1 140
{1 pa}
50.82
41.86
20.70
37.79
di
0,
51.48
42.42
0.31
31.40
1 Mo Ói
[1, 1 Pa]
51.17
42.47
4.07
32.57
μ40
(1
o?)
1 - Po. 1
51.14
42.40
21.11
38.22
po.
51.19
42.42
21.36
38.32
Table 21: Comparison with different expressions for pee across all CCC levels using accuracy (%).
D ADDITIONAL RESULTS
D. RESULTS UNDER CDC SETTINGS
In Tables 22-23, present the full results under CDC settings, which extend those shown in Fig. 8.
Recwniigg 1600
11
13
15
17
19
Mion
ETA
35.77
36.11
35.90
35.76
35
35
35.31
35.50
35:55
35.17
35:33
RDaadb
30.42
35.73
36.30
30.85
15.34
36.34
30.83
34.22
36.11
31.01
38.14
36.39
13.46
34.56
36.07
34.35
32.07
36.33
36.16
31,11
34.28
ASIL (Qaan)
25.45
30.25
33.45
33.05
1242
55.25
35.5 53
53.27
35.72
36.19
35.55
37/43
16:49
18.36
58.8
38.40
3894
3892
38.96
38.05
35.97
$4.34
28.01
16.41
14.00
16.14
1688
16.11
36.37
16.00
19.4
1661
15-41
14.31
15.44
ASR (Taan)
15/05
24.05
15.73
19.70
19
99.71
94
40.4
40.5
40.61
40-11
40.40
41.15
4035
41.07
41
41.0
40,80
41.10
41.34
40.07
Table 22: Results IN-C with CDC for 8 1.0 across revisit steps.
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Receriver rirl
à 100
2
6
9
19
11
13
13
14
15
16
17
18
19
70
Men
RDuesb
3562
31.11
14.85
12.64
36.51
33.55
34.17
ASR (Dan)
20.00
33.05
35.35
36.01
36.63
36.71
36
57.25
37.31
37.30
37.41
37.53
37.79
37.79
57.90
37.90
38.08
38.14
38.19
35.01
36.85
36.71
36.86
ASR (Dan)
33,02
36.33
31.89
35.37
39.54
38.49
40.42
40.23
40.3
40.83
4003
41.23
41.34
41448
41.00
41.00
41.99
41.82
41.51
41.68
40.15
Table 23: Results on IN-C with CDC for 8 10.0 across revisit steps.
D.2 ROBUSTNESS TO TRULY SMALL BATCH SIZES
We evaluate our method on truly small batch sizes, specifically 2 and 4, using single split (transition speed 1000; random seed 43) of CCC-Easy under the same setup Fig. 11. As shown in Table 24, at batch size 4, our method achieves 25.58 compared 17.85 for RDumb, indicating that its robustness extends to smaller batch sizes below 16. However, this advantage diminishes at extremely small sizes (e.g.: 2), where only marginal improvement is observed (Ours 6.46 vs. RDumb 5.87). It is important to note that online TTA and continual TTA are distinct. Online TTA represents an extreme scenario with batch size of I, where all continual TTA methods yield near-random performance (~0.1). A practical solution for continual TTA methods in this scenario is to stack samples over time and adapt when a sufficient number is obtained.
Batch size
2
4
ROID
0.13
16.91
+ RDumb
5.87
17.85
ASR (Ours)
6.46
25.58
Table 24: Accuracy (%) under truly small batch sizes on CCC-Fasy.
D.3 RESULTS FOR CIFARIO-C/100-C
We focus on more challenging and realistic evaluation settings, as addressed in Press et al. (2023). The standard CIFAR-C setting relatively simple and less representative of real-world scenarios. Following Press et al. (2023); Hoang et al. (2024), we categorize corruption types into three groups (Fasy Medium Hard) based on the source model's accuracy to ensure consistent difficulty within the group, reported in Table 25 for CIFARIO-C and Table 26 for CIFAR100-C. We then repeat the corruptions cyclically. The experimental results on CIFARIO-C/ / CIFARI00-C are shown in Fig. 14 and 15.
Level
Corruption Types
Easy
Motionbblur, Snow, Fog, Elastic, JPEG
Medium
Defocus bluur, Glass Zoom Frost, Contrast, Pixelate
Hard
Gaussian.noise, Shot.ncise, Impulse.noise
Table 25: Corruption types for each level of CIFARIO-C.
Level
Corruption Types
Easy
Inpslse.noise, Defocus Motion.blur, Zoombblur, Snow, Brightness, Elastic
Medium
Glass.blur, Frost, Fog, Contrast, JPEG
Hard
Gaussian Shot_ncise, Pixelate
Table 26: Corruption types for each level of CIFARI00-C.
D.4 FULL RESULTS ON RESNET
In Tables 28-34, present the full results ResNet-50,, which extend those reported in Table 4.
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Published as a conference paper at ICLR 2026
C10-C-Easy
C10-C-Medium
Clr-C-Hard
84.20
86.21
90
93.09
$3.97
86.23
84.13
80
77.78
80.63
70.78
$2.69
80
80
Acc. (96)
20
70
60
60
50
50
47.85
ETA
#RDumb
+ASR (Ours)
ROID
+RDumb
+ASR (Durs)
Figure 14: Comparison of ETA ROID and their variants combined with RDumb or ASR across
three levels of CIFARIO-C using accuracy (%), averaged over 1000 recurring visits of corruptions.
C100-C-Easy
C100-C-Medium
C100-C-Hard
70
71:15
72.93
70
67.33
70
65.70
68.66
68.53
68.70
Acc, (%6
60
60
60
50.27
50
50
43.10
ETA
+RDumb +RDumb
+ASR (Ours)
ROID
+ASR (Ours)
Figure 15: Comparison of ETA ROID and their variants combined with RDumb ASR across
three levels of CIFARI00-C using accuracy (%), averaged over 1000 recurring visits of corruptions.
D.5 FULL RESULTS ON VIT
In Tables 35-37, we present the full results on ViT-B-16, which extend those reported in Table 3.
D.6 RESULTS ON VIT-TINY
We evaluate our method on a lightweight backbone (ViT-Tiny). As reported in Table 27, our method consistently improves over the baselines on CCC-Fasy and CCC-Medium. However, due to the ex- tremely limited backbone capacity, adapting to CCC-Hard is particularly challenging, as reflected in Table 27 where all ROID-based variants achieve only 0.10% accuracy. Even under such severe pacity constraints, our method maintains performance gains comparable to those reported in Table 3, demonstrating robustness.
ViT-Tiny
CCC-Hard
CCC-Medium
CCC-Easy
ETA
2.29
34.20
47.09
RDumb
4.45
32.51
45.51
+ ASR (Ours)
5.30
36.48
47.23
ROID
0.10
32.14
45.29
RDumb
0.10
31.61
44.92
ASR (Ours)
0.10
34.47
45.64
Table 27: Accuracy (%) comparison on ViT-Tiny.
E DISCUSSIONS
Q: Does ASR rely on incremental or heuristic solutions for long-term TTA?
A: ASR is not collection ad-hoc fixes. We reframe long-term TTA via a new perspective, treating collapse prevention fixed interval (Press et al., 2023) or after collapse (Niu et al., 2023), but ASR continuously estimates the risk of collapse. Although ASR is integrated with multiple components, these are built under the shared principle: balancing catastrophic and graceful knowledge forgetting.
continuous decision-making task. Prior work typically performs reset at
Q: Doesn' ASR fail to overcome the need for reset in long-term TTA?
A: Reset is an essential and widely recognized mechanism for preventing collapse in long-term TTA. Neural networks often converge sharp minima, which are tricky to escape using standard gradient
23

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Published as a conference paper at ICLR 2026
updates (Keskar et al., 2017), and collapse represents more challenging situation, making recovery nearly impossible without a reset (Hoang et al., 2024). Despite its critical role, reset has been largely underexplored: existing methods typically apply full-parameter reset at fixed intervals, ignoring the model's current state. We directly address these fundamental limitations by proposing a scheme that dynamically adjusts both the timing and scope of reset.
Q: Are the marginal gains worth the engineering effort, would simpler variants suffice?
A: ASR is specifically designed to address collapse in long-term TTA and proves highly effective in challenging realistic scenarios. CCC-Hard best exemplifies such conditions, where ASR achieves a substamtial 44. 12% improvement over the state of the art, demonstrating that it addresses hard tasks robustly. In contrast, easier benchmarks such as IN-C or IN-D naturally yield smaller gains that simpler methods could also achieve in these settings. This highlights that modest gains easy tasks do not indicate that simpler variants would suffice for more challenging scenarios. As collapse-pre prone benchmarks become more available, we expect our advantages to be even more pronounced.
Transition speed
1000
2000
5000
Acc, (9)
Coruptiin ordering
44
45
43
Mean
Source
33.89
33.97
33.95
33.69
33.90
33.99
33.34
34.06
34.23
33. 89-
RMT (CVPR'23)
48.15
46.78
47. 37
46.70
46.44
47.80
48.61
45.03
48.07
47.22++ 1.0
RoTTA (3VPR'2)
1.76
1.49
1.88
2.43
1.84
2.28
2.50
3.06
3.28
2.28=00.6
SANTA (TMLR'23)
47.33
47.47
47.49
47.87
47.68
47.77
48.32
47.11
48.10
47.68 0.4
LAW (WACV'24)
2.71
2.50
2.92
2.99
2.44
3.25
2.79
2.20
3.55
2.82:#0.4
ViDA (ICLR'24)
13.52
12.28
12.74
11.89
13.81
12.29
0.88
DPLOT (CVPR'24)
36,68
35.71
35, 84
36,18
34,02
35.79
33.55
32.94
34.61
35.04++1.2
PALM (AAAI'25)
1.55
1.34
1.66
1.67
1.29
1.70
1.84
1.23
0.5 1.73
CoTTA (CVPR'22)
17.01
15.98
16.24
18.05
17.02
17.13
19.33
18.10
17.50::1.0
SAR (ICLR/23)
36.65
36.24
36.47
39.21
37,55
38.75
39.92
37.84
38.83
37.94+1122
PETAL (CIPP9223)
2.57
2.52
2.64
2.62
2.54
2.71
2.66
2.43
2.64
0.
CMF (ICLR22)
48.29
48.33
48.20
49.38
49.25
49.12
50.87
49.95
50.40
49.31=1099
RDumb (NeutIPS233)
48.02
48.07
47.95
49.08
48.96
48.83
50.41
49.57
50.08
49.00:10.8
ASR (Ours)
50.08
50.12
49.98
51,11
50.97
50.82
52.58
51.59
52.11
51.0440.9
DATTA (ECCVV22)
9.87
18.26
23.48
28.49
24.46
20.79
25.26
29.36
23.65
22.62:55.5
PeTTA (NeurlPS'24)
34.61
34.43
34,56
36.45
36.26
36.40
40.43
38.90
40.01
36, 89-22.2
RDumb (NeurlPS'23)
34.53
34.36
34.50
36.36
36.18
36.33
40.30
38.81
39.92
26-2122.22
ASR (Ours)
36,96
36.92
37.11
38,72
38.59
38.79
43.14
41.52
42.53
39.36-t:2.3
ETA (ICM)/22)
42.13
42.23
42.12
43.46
43.13
42.87
45.25
43.86
44.07
43. 24-2+4++11 1.0
RDDmb (Neu1PP523)
48.55
48.57
48.49
49.35
50.79
49.97
49.477-00.8
COME (ICI R'25)
10.96
8.87
10.49
0.63
10.42
9.79
2.46
1.98
1.43
4.34
ReservoirTIS (NeustS'22)
49.02
48.96
48.93
49.47
48.38
80
49.95
47.49
48.77
86:10.6
ASR (Ours)
50.33
50.31
50.13
51.27
51.12
50.92
52.73
51.78
52,21
0..
EATA (CCLL22)
48.53
48.65
48.48
49.52
49.47
49.35
51.00
50.07
50.64
49.52-20.9
RDumb (NeutIPS/23)
49,06
49.09
49.03
50.02
49.89
49.85
51.20
50.41
51.02
49.95=10.8
COME (ICLR225)
46.99
47,04
47.
37.50
47.72
47.63
49.11
48.26
48.80
46.67-13333
Reservoir TA (Neur)PS'25)
51.07
51.19
$1.18
51.72
$1.45
$1.49
$2.39
$1.30
51.76
51.51:0.44
ASR (Ours)
49.40
49.40
49.32
50.43
50.31
50.17
52.00
51.06
51.53
50.40-1-09
ROID (WACV/24)
49,02
49.03
48.92
49.95
49,81
49.74
$1.15
50.37
50.94
49.88 88.
RDumb (N6u1P5'23)
48.82
48.85
48.74
49.76
49.63
49.56
50.91
50.15
50.75
49.69-10.8
COME (CCLR'2)
0.11
0.12
0.12
0.12
0.13
0.12
0.11
0.11
0.13
0.12=0.00
ReservoirTTA (NewsP5'25)
50.45
50.57
50.51
$1.06
50.89
50.87
51.43
50.65
$1.30
50.86:00.3
ASR (Ours)
50.50
50.58
50.42
51.47
51.36
51.19
52.86
51.94
52.35
51.11:088
Table 28: Performance comparison with state-of-the--ne methods on CCC-Easy across nine variants:
three corruption transition speeds (1000/ 2000 / 5000) and three corruption orderings that are deter-
mined by random seeds (43 / 44/ / 45).
24

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Published as a conference paper at ICLR 2026
Transition speed
1000
5000
Acc. (%)
Coruuttion ordering
44
45
43
45
43
44
45
Mean
Source
16.95
16.78
16.95
16.59
16.87
16.97
16.57
16.91
17.20
16.87
RMT (CVPR'23)
35.48
35.38
35.60
36.07
35.65
34.08
35.41
31.42
36.09
35.02:+:44
RoTTA (CVPR'23)
1.23
1.00
1.36
1.70
2.76
2.08
1.76=0.6
SANTA (IMLR/25)
33.75
33.77
34.17
35.65
34.18
34.26
35.94
33.79
34.57
34.45:0.88
LAW (WACV'24)
1.56
1.09
1.50
1.57
1.38
1.30=0.4
VIDA (ICL8'24)
6.16
6.10
6.20
5.73
6.19
5.78
4.95
5.65
5.01
5.75=00.5
DPLOT (CVPR'24)
14.64
10.70
18.70
12.05
7.58
18.07
9.50
6.83
20.08
13.13:24.77
PALM (AAAI'25
0.76
0.50
0.98
0.63
1.47
0.51
0.62
0.74
CaTTA (CVPR'22)
9.62
8.65
9.10
10.04
9.06
10.30
10.56
9.52
11.63
9.83
SAR (ICLR'23)
19.98
21.20
20.01
23.25
20.91
21.19
23. 89
24.91
24.89
22.25++119
PETAL (CVPR(23)
2.00:1.2
CMF (ICTR))3)
38.77
38.41
38.79
41.32
40.28
40.28
42.84
41.93
42.85
40.61++1.6
RDumb (Neu1PS/23)
38.13
37.86
38.20
40.66
39.59
39.61
41.96
40.99
41.95
39.88:115
ASR (Ours)
40.23
40.01
40.33
42.69
41.70
41.66
44.22
43.25
44.15
42.03+1.66
DATTA (BCCVV22)
9.42
10,96
9.19
12.78
13.28
12.85
19.46
14.37
17.49
13.31:3..2
PeTTA (NcarPP5:24)
22.64-4-2.8
RDumb (NeutPS"23)
19.28
19.23
19.56
23.07
21.64
21.87
26.97
24.84
25.05
22. 2.216622.66
ASR (Ours)
22.47
22.90
22.99
26.67
25.33
23.99
19.62
20.34
27.29
23.51:122.5
ETA (CMLU22)
22.28
13.05
25.36
17.55
20.87
28.41
19.03+469
RDumb (NeutIP'23)
37.72
37.45
37.83
40,21
39.05
39.15
41.55
40.46
41.34
39 39.42:1155
COME (CCLR'2
0.77
0.53
0.55
0.68
0.47
0.57
0.68
0.31
0.44
0.56=#0.1
ReservoirTTA (NeurtP5'25)
41.22
40,97
41.40
42.97
41.56
41.87
43.30
41.72
42.03-10.9
ASR (Ours)
40.10
39.78
40.04
42,34
41.47
41.49
44.13
43.35
44.25
41.88+-11 1.6
EATA (ICML/2))
37.36
36.91
37. 40
39.98
38.66
80.80
41.52
40.47
41.58
39. 1.7
RDDumb (NewtP8523)
38.26
38.06
38.43
41.99
40.91
39.93:11.
COME (ICLR'25)
34.81
34.63
35.02
37.47
36.03
36.15
38.86
37.87
38.79
36.65 16.6331116 1.6
Reservoir TA (NeutP5'25)
41.22
40.97
41.40
42.97
41.56
41.87
43.30
41.72
43.29
42.03
ASR (Ours)
38.84
38.65
38.95
41.38
40.34
40.34
43.21
42.30
43.04
1.7
ROID (WACV'24)
38.79
38.64
38.91
41.24
40.16
40.19
42.44
41.44
42.41
40.47+114
RDumb (Neu1P5'23)
38.42
38.26
38.56
40.85
39.75
39.77
42.00
40.94
41.90
40.05
COME (TCLR'25)
0.15
0.12
0.15
0.11
0.18
0.11
0.11
0.13
0.13-40.0
ReservoirTTA (Neu)1SS'25)
39.99
39.87
40.15
41.85
40.60
40.67
42.07
40.87
41.98
40. 40.89-10.8
(Ours)
44.77
43.98
80-+1.-
Table 29: Performance comparison with soute-of-the-are methods on CCC-Medium across nine vari- ants: three corruption transition speeds (1000/ 2000 / 5000) and three corruption orderings that are determined by random seeds (43 44 45).
25

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Published as a conference paper at ICLR 2026
Transition speed
1000
2000
5000
Acc. (%)
Coruuttin ordering
43
44
45
43
45
43
44
45
Mean
Snurce
1.29
1.23
1,31
1.31
1.23
1.30
1.33
1.19
1.25
1.27++0.0
RMT (C)PR(23)
12.13
13.18
13.12
9.43
10.74
12.83
7.23
0.86
9.27
9.92-13.7
RaTTA (C)PPR23)
0.50
0.77
0.74
0.66
0.77
0.96
0.79
0.17
0.87
0.69:0.2
SANTA (IMLR'25)
9.28
9.93
9.16
9.08
9.89
9.14
8.96
9.77
9.97
9.4622..4
LAW (WACV'24)
0.34
0.17
0.22
0.31
0.22
0.27
0.16
0.23
VIDA (ICLR224)
0.44
0.39
0.45
0.45
0.40
0.44
0.48
0.38
0.38
0.42=0.00
DPLOT (CVPK'24)
0.53
0.88
0.77
0.64
0.24
0.31
1.22
0.12
0.36
0.56
PALM (AAAI'25)
0.14
0.12
0.10
0.14
0.17
0.13
0.12
0.16
0.13
CaTTA (CNPR'22)
1.73
1.98
1.95
1.43
1.71
2.09
1.44
0.20
1.19
0.5
SAR (13).R)23)
1.54
1.64
1.52
1.61
2.29
1.67
2.90
2.50
2.56
2.03++0.5
PETAL (CVPR'23)
0.68
0.64
0.74
0.81
0.56
0.80
0.96
0.14
0,55
0.65=0.2
CMF ((CTU))34)
1.06
0.62
0.46
1,088
0.40
01773
2.41
01299
0.95
0.89+0.6
RDumb (NeurlPS'23)
13,00
15.10
12.44
13.51
15.44
13,56
16.51
17.96
17.85
15.04+19
ASR (Ours)
19.57
21.40
19.43
20.10
21.39
21.49
20.62
22.46
22.86
21,04+
DATTA (ECCV'24)
3.00
2.48
2.57
1.49
1.51
1.56
1.61
1.70
1.53
0.9
PeTTA (Nc4)P)5'24)
4.93
5.44
4.70
6.58
6.00=0088
RDmmb (NeurIPS'23)
5.04
5.48
4.72
5.69
6. 18
5.82
6.14
6.63
6.62
5.8
ASR (Ours)
7.42
7.67
7.03
7.62
8.42
7.53
6.31
6.86
6.55
0.27
ETA (ICML)22)
0.67
0.28
0.26
0.41
0.18
0.29
0.34
0.19
0,24
0.32-+0.1
RDumb (NeutIPS233)
7.58
9.64
6,90
9.46
11.08
8.74
10.33
12.67
11.57
9.
COME (CCL)225
0.15
0.11
0.14
0.15
0.11
0.24
0.13
0.10
0.13
0.14=00.0
ReservoirTTA (Neur)PS'25)
1.73
0.89
0.42
0.93
0.93
2.63
0.24
1,99
0.57
1.15
ASR (Ours)
15.01
18.18
13.36
15.95
18.57
15.83
18.07
18.32
20.59
17.10:22.1
EATA (ICM)/22)
1.25
0.80
0.57
1.11
0.49
0.64
1.67
0.32
0.51
0.82 0.4
RDumb (NcurPP523)
9.14
10.97
8.07
10.52
12.48
9.94
11.67
13.88
1
COME (CCRR22))
0.74
0.96
0.79
1.07
0.29
0.85
1.51
0.21
0.79
0.80
ReservoirTIn (NeutP8'25)
11.34
10.53
11:47
9.36
4.21
10.86
6,42
2,82
1,20
7.58
ASR (Ours)
15.01
17.19
14.03
15.63
17.49
15.38
17.42
15.36
18.44
16.22:1.44
ROID (WACV'24)
12.64
15.79
13.28
9.63
12.65
11.81
10.66
8.72
17.12
12.48++2.6
RDumb (NeurIPS'2) 23)
14.13
15.92
13.74
14.03
16.05
14.06
15.48
17.34
17.98
15.41=15
COME (ICL 2'25
0.12
0.11
0.10
0.13
0.10
0.15
0.13
0.10
0.12
0.12+0.0
Reservoir TT (NeurtPS'25)
11.83
14.92
13.50
13.41
13.53
13.20
13.60
13.96
15.46
13.71+100
ASR (Ours)
20.99
22.51
20.40
21.22
22.93
21.36
22.37
23.84
22.21+122
Table 30: Performance comparison with thethe-rrt methods on CCC-Hard across nine variants: three corruption transition speeds (1000 2000 / 5000) and three corruption orderings that are deter-
mined by random seeds (43 44 / 45).
Method
3
4
6
9
10
Meun
Source
18.01
18.01
18.01
18.01
18.01
18.01
18.01
18.01
18.01
18.01
18.01:10.0
RMT (CVPR'23)
47.68
45.48
44.09
43.87
46.48
45.70
44.68
44.86
43.61
43.64
45.01+1..
Source-free
42.33
39.13
38.21
37.75
33.02
33.63
36.88+3.1
RoTTA (CVPR'23)
27.21
31.85
27.23
24.99
28.56
30.99
30.55
29.61
30.70
28.85
29.05+12.0
SANTA (TM)R'23)
40.00
39.85
39.83
39.77
39.84
39.53
39.83
39.85
39.63
39.98
39.81+-0.1
LAW (WACV'24)
22.91
17.65
1.14
14.63
24.72
17.70
10.91
11.27
11.66
2.06
13.47-17.4
VIDA (ICLR22))
17.87
17.81
17.62
17.76
17.78
17.83
17.77
17.80
17.79
17.60
17.76:10.1
DPLOT (CVPR'24)
37.52
33.86
30.34
31.38
33.64
29.72
32.66
30.58
29.99
30.38
32.00++2.3
PALM (AAAI225
21.14
3.47
16.37
23.57
14.06
8.57
11.02
8.86
4.75
12.69-16.3
EATA (ICM)/22)
48.03
47.60
47.85
47.42
48.18 88
47.87
47.75
47.78
48.01
47.65
47.81-10.2
+ COME (XCLR'25
44.34
43.66
44.13
43.59
44.48
44.24
44.04
44.16
44.62
44.13
44, 14:1-.0
CoTTA (CVPR(222
39.59
36.76
31.44
35.60
38.70
36.71
36.41
34.66
38.18
32.03
35.51+22.6
SAR (CLR'23)
41.62
41.13
40.77
40.04
41.61
40.71
41.48
40.63
40,44
35.11
1..35-+1.88
PETAL (CVPR'23)
40.87
38.09
33.08
38.92
40.03
38.73
37.50
36.94
34.65
34.03
CMF (CLx'24)
48.74
48.41
48.67
48.35
48.83
48.61
48.57
48.58
48.80
48.56
48.6110.11
DATTA (ECCV'24)
35.97
37.58
33.45
37.68
35.69
32.80
31.88
36.86
28.88
34.81
34.56-22.7
PcTTA (NcurIPS'24)
31.57
31.57
31.44
31.59
31.56
31.36
1
31.40
31.65
31.76
31.55.1011
ETA (JCLL'22
43.68
43.69
42.97
42.91
44.19
44. 12
43.84
43.79
43.88
43.03
43.611104
RDumb (NeurIP5'3)
46.44
46.09
46.48
46.06
46.54 54
46.39
46.75
46.31
46.39--0.2
ASR (Ours)
47,50
46.89
47.10
46.89
47.51
47.43
47.26
47.22
47.15
46.79
47.17-0.2
ROID (WACV'24)
48.66
48.53
48.57
48 47
48.66
48.56
48
48.53
48.66
48.
48 18.588+0.1
+ RDumb (NeurIP8/23)
48.01
47.92
48.07
47.90
48.02
47.96
48.04
48.02
48.
48.00
48 48.00+10.1
COME (CLI)22)
0.17
0.32
0.39
0.33
0.31
0.42
0.29
0.85
0.75
0.28
0.411022
+ Reservoir ITA (NeastP::2) 25)
47.40
47.42
47.23
47.30
47.38
47.33
47.47
47.30
47.28
47.28
47.34:10.1
+ ASR (Ours)
49.76
49.31
49.40
49.20
49.78
49.63
49.42
49.60
49.54
49.32
49,50-10.2
Table 31: Accuracy (%) on CIN-C over ten random permutations of the corruption order.
26

---

Published as a conference paper at ICLR 2026
Method
3
4
6
7
9
10
Mean
Source
18.01
18.01
18.01
18.01
18.01
18.01
18.01
18.01
18.01
18.01
18.01-:0.0
RMT (C)PR'23)
46.53
44.99
42.80
44.17
45.95
44.01
43.88
43.59
42.99
42.92
44. 14.18-11.2
Source- free
42.07
38.68
32.79
36.20
37.47
37.30
34,04
32.34
33.08
36.41-33.2
RoTTA (CVPR'23)
29.33
32.30
27.21
27.16
29.09
32.17
30.24
30.19
30.41
28.96
29.71
SANTA (5))R'23)
39.60
39.38
39.28
39.28
39.54
39.52
39.37
39.44
39.42
39.16
39 39.40:10.1
LAW (WACV'34)
21.82
15.81
1.47
15.34
24.20
16.41
9.00
13.86
13.06
2.74
13.37-+6.9
ViDA (4CLR'24)
17.86
17.82
17.60
17.77
17.77
17.85
17.78
17.80
17.79
17.60
17.76++0.1
DPLOT (CVPPR224
36.98
34.19
30.29
30.54
34.04
27.87
33.05
30.02
29.84
29.38
31.62++2.77
PALM(ANN22) (AAAI225
19.09
14.37
3.18
16.71
22.72
13.95
7.52
10.76
8.30
4.23
12.0816.1
EATA (CCLL222
47.70
47.29
47.65 63
47. 12
47.89 89
47.65 67.63
47.51
47.52
47.71
47.41
97.54:10.2
+ COME (ICLR225
44.26
433.69
44.11
43.62
44.41
44.11
43.86
44.24
44.55
44.05
44.09+0..3
CoTTA (CVPR(22)
39.10
36.39
31.57
35.61
38.
36.41
36.15
34.40
32.14
32.73
35,29+2244
SAR (ICLRR23)
40.75
40.57
40.018
39.04
40.89
39.68
40.30
39.52
39.44
40.40
40.07++0.6
PETAL (CVPR'23)
26.41
23.71
17.45
22.96
24.88
22.74
22.97
20.34
17.88
19.07
21.84:2299
CMF (CLRR22)
48.44
48.06
48.28
48.03
48.57
48,33
48.15
48.27
48.44
48.19
48.28::0.2
DATTA (ECCV'24)
7.94
3.30
2.42
1.81
3.75
2.46
1.66
4.07
2.72
2.37
3.25
PeTTA (NcurIP5'24
31.49
31,62
31,60
31.55
31.57
31.69
31.66
31.47
31.69
31.74
31.61+10.1
ETA (ICM)222)
43.75
43.61
43.29
43.09
44.32 32
43.95
43 90
43.56
43.72
43.08
43 43.63-10.4
+ RDumh (NourIPS'23)
46.21
45,92
46.34
45.68
46.20
46.
46.19
46.42
46.00
ASR (Ours)
47.31
46.62
46.93
46.47
47.04
47,00
46.72
46.87
46.81
46.50
46.83+0.22
ROID (WACV'34)
48.46
48.32
48.25
48. 11
48.28 28
48.27
48.1
48.24
48.24
48.16
48,25++0.1
+ RDumb (NcurIPS'23)
47.64
47.60
47.77
47.60
47.64
47.72
47.58
47.72
47.75
47.66
47.674...1
COME (CLR225)
0.15
0.38
0.29
0.29
0.37
0.31
0.31
0.37
0.37
0.28
0.31
ReservoirTTA (N
46.93
47.08
46.90
46.91
46. 96
46.49
47.04
46.88
46.98
46.88
46.96++0.1
+ ASR (Ours)
49.37
48.98
49.07
48.84
49.45
49.27
49.04
49.27
49.16
48.99
49. 49.14400.2
Table 32: Accuracy (%) on non-i.i.d CIN-C over ten random permutations of the corruption order.
Necaningg 111
Medhod
2
3
5
6
K
9
19
11
11
15
14
13
16
12
1B
1V
20
Mean
3.56
3166
3.06
3.16
3.16
5:28
3.06
RMT
27.63
33.01
37.25
30.08
33.99
40.78
41.11
41.36
41.72
41.76
41.76
41.92
42.01
43.02
41.95
42.05
42.10
43.08
42.06
42.09
40.34:3.55
31.15
36.20
16.47
14.43
19:40
18.00
19:00
19.11
34:00
19:75
16.74
SANTA
17.25
23.75
37:30
37.00
28.94
28.81
28-91
26.99
26.99
26-43
28.00
26.15
25
20.0010.5
LAW
25.85
30.02
31.95
32.08
31.00
31.31
30.75
30.34
30.55
20.36
25,55
20.32
20.44
23.36
20:37
20.35
29.41
25.39
26.30
20.24
20.81=1.66
3.00
3.3
2.95
DPLOT
35.75
57.07
53.33
57:35
37.35
$3.36
36.5-11.7
PALM
34.66
31.70
32.14
31.71
11.34
50.76
09.71
30.54
10.75
51.16
33.01
30.75
51.78
30.96
10.03
54.91
33.95
30.03
54.96
30.96
COME
30.21
34.99
34.90
34.52
34.17
35.35
33.82
31.04
35.25
52.19
31.9
12.92
52.57
32.04
12.40
$2.46
31.52
12.35
52.10
32.00
53.221.1
CFITA
18:75
24.00
20.02
31.39
33.47
54.06
35.47
35.00
16.23
56.55
35.20
16:94
$7.05
31.17
31:24
50.25
37:20
31.25
57.36
31.22
PETTA
18.74
25.64
19:12
91
11.76
11.09
54.04
5410
34.88
54.31
31.50
54.34
54.38
CME
15.07
38.00
31
39.58
19.53
33.05
19-43
16.66
38:20
16:11
19.5
33.61
19:51
58.55
38.50
16.94
74.0
DATTA
20.11
39:67
19:5
19:59
19.60
19.07
1953
19.67
19:07
19.50
19.00
1967
19.07
19:57
19.6
19.00
12.40
ETA
30164
35.30
36.55
36.67
35.76
36.58
36.5
36-47
36.25
36.16
35.05
36:00
36.66
35.01
35.95
33.91
35.29
35.76
35.80
35.00
5518-122
36.97
56.66
36.51
ASE
26
33.09
34.85
11/00
56.34
33.33
34.00
36.79
34.00
18.97
59.35
30407
34.00
M.40.19
ROID
18:11
35.00
16
10.00
38.43
18:11
17.95
38:30
18.15
37.50
17.97
30.0
1336-00
COME
4.98
0.56
0.88
0.9
0.06
006
0.58
0.66
0.08
0.06
0.88
0.08
0.58
0.06
0
0,385111
36.58
16.97
15.99
90
58
37
17%5
17.00
37
35.05
17.11
17
Table 33: Accuracy (%) on IN-C across 20 recurring visits of the domain sequence.
Recaniing
Mehaal
3
6
9
10
11
13
13
14
15
16
17
18
19
20
Men
RMI
45.16
45.51
16.35
45.34
16.18
-6.81
48.33
05.38
45.37
16.53
46.09
45:53
40.39
15.31
40.87
66.66.04
22.28
41.72
03.83
43.92
41:41
63.28
63.31
41.90
43.87
43.68
41.77
63.43
41.26
43.75
43.11
-1.68
03.68
63.16
41.65
4311.004
37.04
SANTA
41.50
41.29
41.44661
LAW
-1.19
15.30
32.09
31176
30/24
MU.0
25.78
39:35
20.12
29.0
29:33
79.65
24.07
3967
20.35
79.6
39.66
29
24.7
MEYESA
VISLA
0.21
0.01
0.01
0.00
0.01
0.01
0301
0100
6m
PALM
13:56
1.74
1442
1,42
1.42
1.42
1.42
1.43
1:42
1,42
1.42
1.42
1,42
1.43
1.42
1.42
1.42
1.42
1.42
1.43
206127
43.42
41.9
4136
41.58
41.30
41.37
41.43
COME:
00.90
15.06
25.00
15.55
0530
15.15
05.09
45.0
15.04
15.5
16.5
4491
(511464
CHITTA
41.75
45.70
16.75
45.73
10
0535
41.0
11.49
6413
41.30
13.9
6145
12.45
11.50
41.50
1131
00:55
(351-31
43:04
43.35
41.00
-2.00
41.59
40.87
39.47
38.36
31.29
36.49
3497
34.11
33.11
300923
PETAL
0.00
CMF
44.00
45.21
45.42
45.15
45.39
45.99
4262
45.14
45.31
45.5
45.72
45.32
45.1
45.36
44344
45.00
45.0
45.13
4511
43.6
4515
TAATTA
75.75
3.90
0.55
0.66
6:81
0.6
0391
6:81
0.91
0391
6.81
7M173
43.1
43.31
17.75
EIA
41.24
40/42
$1.30
2036
39477
28.2
17.44
37.25
20.6
35.51
36.44
26.26
15.44
15:07
20.47
35
34:24
30.37
34:21
S111-111
-1.05
41:39
42:13
11.16
41.18
41.45
11.2%
41:34
41:31
41:01
42.00
41
4036
41.09
41.44
41.20
41.82
4175
41.33:6.3
ROID
46.00
46:22
46/02
46.35
45-22
M6.29
46
45.4
46.32
40.14
46-12
46:25
46.05
46.13
46:15
46.30
45.25
46:22
46.0
46.17
-OOME
0.30
0.36
0.19
1176
0.16
6.90
15.17
15
10
16
11:00
15.81
11 26
11
15.7
15.0 60
15:37
45
11.00
(Dazs)
46.30
46.53
45.32
46.52
46.01
4630
46.50
46.53
46.48
45.45
46:33
46.32
Table 34: Accuracy (%) on IN-D109 across 20 recurring visits of the domain sequence.
27

---

Published as a conference paper at ICLR 2026
Transition speed
1000
2000
5000
Acc. (%)
Corruption ordering
43
44
45
43
44
45
43
44
45
Mean
Source
54.74
55.18
54.47
54.97
55.03
54.77
55.09
54.88
55.12
54.92=0.2
CMF 24)
60.68
60.96
60.59
61.51
61.74
61.51
62.66
62.52
61.52
61.52=0.7
CMAE (CVPR'24)
48.11
49.94
48.19
50.68
50.44
52.57
51.04
54,89
54,50
51.15-2.3
REM (ICML'25)
65.82
66.12
65.79
66.14
66.23
66.05
66.93
66.02
66.36
66.16
ETA (ICML/22
47.95
47.68
47.47
48.71
15.74
48.44
49.92
49.78
49.93
45.07=10.44
RDumb (NcurIPS/23)
59.25
59.56
59.13
59.74
60.07
59.74
60.76
60.75
60.91
59.99=0.6
ASR (Ours)
59.62
59.99
59.59
60.34
60.69
60.46
61.57
61.33
61.62
60.58_0.7
ROID (WACV'24)
60.01
60.32
59.88
60.67
60.99
60.68
61.66
61.64
61.82
60.85-0.7
+ RDumb (N)
59.78
60.09
59.65
60.44
60.75
60.43
61.35
61.39
61.56
60. 0,7
+ ASR (Ours)
60.61
60.86
60.51
61.28
61.54
61.30
62.46
62.21
62.52
6,748-0.7
Table 35: Performance comparison with state saatee-o-ttee of-the-art art methods on CCC-Easy across nine variants:
three corruption transition speeds (1000/ / 2000 5000) and three corruption orderings that are deter-
mined by random seeds (43 44 / 45).
Transition speed
1000
2000
5000
Acc. (%)
Corruption ordering
44
45
43
44
43
45
Mean
Source CMF (ICLR'24)
41.76
41.47
40.49
42.32
41.10
41.49
42.15
42.39
42.51
41.74:10.66
52.17
51.63
51.87
34.21
53.49
53.59
55.41
55.33
55.82
51.50:16.3
CMAE 24)
41.76
36.63
41.88
43.16
40.52
44.40
45.02
46.41
51.52
43.48++3.9
REM (ICML/25)
57.34
57.23
56.92
58.36
57.28
57.68
58.94
58.41
59.71
57.99.1.9
ETA (ICML/22)
34.62
23.74
28.04
34.04
34.54
36.05
35.36
38.07
38.97
33.71++4.6
RDumb (NeurIPS/33)
49.02
48.88
48.57
50.77
50.16
50.28
51.97
$2.25
52.58
50.50
ASR (Ours)
49.86
49.69
49.57
51.96
51.39
51.46
53.31
53.55
53.92
1..
ROID (WACV'24)
50.72
50.67
50.39
52.61
$1.91
52.00
53.62
53.72
54.08
52.1911.3
RDumb (NeurIPS'23)
50.23
50,20
49.81
52.11
51.45
51.39
53.09
53.26
53.62
11.6811.3
ASR (Ours)
52.14
52.08
51.98
53.91
53.07
53.26
55.03
54.95
55.57
1.3
Table 36: Performance comparison with state-of -aa--offthhheee the- methods on CCC-Medium across nine vari- ants: three corruption transition speeds (1000/ / 2000 / 5000) and three corruption orderings that determined by random seeds (43 / 44 45).
Transition speed
1000
2000
5000
Acc. (%)
Corruption ordering
43
44
45
43
44
45
43
44
45
Mean
Source CMF (ICLR'2))
14.40
15.44
15.40
14.16
15.38
14.10
13.90
15.31
15.40
14.83++0.6
1.22
0.30
2.74
2.17
0.14
0.85
3.23
0.13
5.34
1.7941.7
CMAE (CVPR'24)
26.47
26.78
22.70
25.95
28.33
24.96
26.60
30.27
30.20
26.92:42.3
REM (ICML/25)
3.80
8.53
7.03
5.58
5.94
9.39
10.67
38.45
9.31
10.97499.9
ETA (ICM))22)
1.34
0.33
1,66
0.99
1.34
1.02
1.97
1,58
0.79
1.22+0.5
RDDmb (NeurIPS/33)
22,41
24.43
22.01
23.52
25.54
23.39
22.16
23.52
22.42
23.27+1.1
ASR (Ours)
24.67
25.88
24.14
23.93
25.21
24.26
22.76
23.96
25.20
24.45+00.9
ROID (WACV'24)
11.74
23.23
1.00
25.75
9.08
25.10
12.49
6.75
13.55
14.30+8,22
RDumb (NeudPPS23) (3)
24.17
25.40
23.76
25.05
26.62
24.84
26.25
27.37
28.01
25.72
ASR (Ours)
27.69
28.76
27.31
27.62
28.82
27.52
27.58
28.67
28.85
28.09.00.6
Table 37: Performance comparison with sfate-of-the-ar methods on CCC-Hard across nine variants: three corruption transition speeds (1000 / 2000 / 5000) and three corruption orderings that are deter- mined by random seeds (43 / 44 / 45).
28


Reviews:
[{"review_id": "zNnuh2kKTv", "reviewer": "Reviewer_CQqn", "summary": "The authors investigate the problem of long-term continual test time adaptation. Previous algorithms were shown to collapse at some point during longer term adaptation, and simple resetting methods have been proposed as baselines in this problem setting. Full model reset like in RDumb naturally yields a substantial drop in downstream performance in the first steps after the reset, hence the authors propose an adaptive resetting scheme where the timing of these resets is adaptively computed, and instead of resetting the full model, only parts of the model parameters are reset to baseline. The authors show in multiple experiments vs. main baselines ROID and RDumb that their adaptive strategy yields performance improvements in the CCC and other continual adaptation benchmarks.", "questions": "1. Which empirically observed behaviors motivated the development of the different adaptation of the reset procedure? Can you make statements about sources of the collapse (as you allude to with Figure 3)\n2. Section 3.3:  \"recover essential knowledge lost\" and \"While parameters and their Fisher matrices increasingly align with the current domain, their proximity to reset makes them more vulnerable (...)\" -- is there empirical and theoretical evidence for these statements?\n3. Could you make the code for the algorithm available, will it be released under an open source license?\n4. Could you comment on additional hyperparameters that were introduced, and the robustness of the method under variations of these?\n5. Were the algorithms in e.g. Table 1 and other tables re-run for this paper, or are any number in the paper copied from previous papers?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- empirical performance of the proposed algorithm outperforms the considered methods\n- results are clearly presented, and the paper is easy to follow\n- considered experiments are well presented, relevant ablations performed", "weaknesses": "The method is overall incremental, and the components of the method are heuristic fixes to the collapse problem. The scientific depth of the study is limited; I see limited value between what is already known in the field. The method fundamentally does not overcome the issue that reset is required to prevent collapse in longer-term test-time adaptation.\n\nWhile there are certaintly gains over the state of the art, they seem marginal and the amount of engineering to get these 1%-point improvements makes it questionable if the simpler variants are also sufficient in practice.", "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": "e2pQ0EyV9j", "reviewer": "Reviewer_UtEd", "summary": "This paper tackles long-term continual TTA, where models suffer from error accumulation and eventual collapse (predicting only a few classes). The authors propose:  1. Adaptive and Selective Reset (ASR) — dynamically determines when and which layers to reset based on prediction concentration, mitigating both over- and under-resetting. 2. Importance-aware knowledge recovery — recovers lost information post-reset using Fisher-based regularization with a hybrid (CMA + EMA) accumulation scheme. 3. On-the-fly adaptation adjustment — adaptively adjusts according to prediction inconsistency between the source and the current model. Extensive experiments on CCC, CIN-C, IN-C, and IN-D109 show large gains over prior TTA methods (e.g., +44.12% on CCC-Hard).", "questions": "How about the performance of more recent and advanced TTA methods, such as CMF, PeTTA, or ReCAP (Region Confidence Proxy for Wild Test-Time Adaptation, ICML 2025), when combined with ASR? Is ASR still effective in these cases?\n\nHow about the computational efficiency of the accumulated Fisher Matrix? More detailed analyses are preferred.\n\nI am also curious about the performance of ASR on lightweight backbones, such as ViT-Tiny or Swin-Tiny.\n\nIt would be more informative to replace Tables 3 and 4 with a unified figure showing results for a wider range of reset intervals (e.g., 2000, 4000, 6000) and reset ratios (e.g., 10%, 20%, 30%), enabling a more thorough sensitivity ablation analysis.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The studied problem of when and where to reset is a very practical problem, and it is a key step to enhance the stability of TTA under long-term and large-scale real-world application settings.\n\nThe proposed methods (from fixed or heuristic resets to a data-driven and risk-aware reset strategy) are simple yet effective.\n\nThe combination of adaptive reset timing, layer-wise selective reset, and Fisher-based knowledge recovery makes the overall method cohesive and effective.", "weaknesses": "The proposed method involves many hyperparameters, making it potentially difficult to tune in real-world online testing scenarios. Could the authors clarify how these hyperparameters are determined and whether ASR is sensitive to them?", "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": "WxBWCfdJ4p", "reviewer": "Reviewer_wwgg", "summary": "This paper addresses the problem of model collapse in long-term continual test-time adaptation (TTA) due to error accumulation. The authors propose an Adaptive and Selective Reset (ASR) scheme that dynamically determines when to reset by monitoring prediction concentration and where to reset by selectively resetting layers based on the estimated collapse risk. The method is supplemented by an importance-aware regularizer to recover lost knowledge and an on-the-fly mechanism to adjust adaptation based on domain discrepancy.", "questions": "1. In Figure 2, what do the numbers, icons, and the different colors in the top bar represent? A legend would be helpful.\n\n2. The paper states hyperparameters were tuned on a small holdout from CCC-Hard (Line 310). Were these exact same hyperparameters then used for all other datasets and settings (CIN-C, IN-C, IN-D109, non-i.i.d.)?\n\n3. A general question about resetting. How would a parameter-reset method like ASR be applied to TTA methods that do not primarily adapt the model parameters, such as prompt-based methods (e.g., FOA, DPCore)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The primary strength is the novel ASR mechanism, which offers a more motivated and flexible alternative to naive periodic resets by dynamically linking the reset trigger and scope to a quantifiable measure of model collapse (prediction concentration). This adaptive approach is intuitive and addresses a clear limitation of prior work. The method is validated by extensive experiments on long-term TTA benchmarks, demonstrating significant performance gains, particularly on the challenging CCC-Hard dataset.", "weaknesses": "1. The definition of \"prediction concentration\" (Eq. 1), which is central to the reset trigger, is based on the entropy of average logits. This metric seems sensitive to factors not fully explored: it's unclear if it's robust to logits of different magnitudes, and its dependency on batch composition (batch size, class distribution) is a concern. Furthermore, the supporting correlation in Figure 3 lacks context, as the dataset and settings used to generate it are not specified. \nMoreover, the paper's justification for the method's effectiveness (e.g., Line 474) relies on the assumption that TTA models \"strengthen predictive confidence\" for the imbalance label setting. This holds for entropy minimization methods but not necessarily for all TTA strategies. This raises a significant question about whether ASR can reliably detect collapse in models using other objectives, such as consistency regularization, potentially limiting its applicability.\n\n2. While the \"importance-aware knowledge recovery\" component is intended to address catastrophic forgetting, the experiments lack direct evidence of its efficacy. In recurring domain scenarios (e.g., IN-C 20 visits), it is not shown whether the model effectively re-utilizes previously learned knowledge from a specific domain or simply re-adapts more successfully from a cleaner reset state.\n\n3. The comparisons in Table 1 are difficult to interpret. As ASR is presented as an add-on component, it's unclear if it should be compared only against RDumb or also against other SOTA add-ons like COME (which is listed but not directly compared, e.g., COME+ETA vs. ASR+ETA). To validate its general utility, ASR should be evaluated on a broader range of base TTA methods, particularly those utilizing different objective functions beyond ETA and ROID.\n\n4. The evaluation benchmarks (like CCC and CIN-C) primarily feature slow and predictable domain changes. I'm wondering if we reset the model every time the domains change (i.e., exactly at the domain boundary), is this performance an upper bound for ASR or RDumb? (e.g., in Figure B.1, which is CIN-C)  Furthermore, this raises questions about the method's robustness in more dynamic environments, where it's unclear if it would still reset appropriately when the domain changes faster, or if frequent resets would degrade adaptation performance. Evaluating on a more dynamic benchmark, such as the CDC setting from DPCore, would be a valuable test.", "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": "BK9O8JKWTr", "reviewer": "Reviewer_8GCC", "summary": "This paper proposes the Adaptive and Selective Reset (ASR) scheme to address the problem of model collapse in long-term Test-Time Adaptation (TTA). The main contributions are: 1) The ASR mechanism dynamically determines when and which parts of the model to reset; 2) An importance-aware knowledge recovery regularizer based on Fisher information; 3) Dynamic adjustment of hyperparameters according to domain differences to enhance adaptability. Experiments show that ASR performs well in multiple benchmark tests and significantly improves the stability and adaptability of the model.", "questions": "It is suggested that the author, when addressing the issue of when to reset, should first rule out other non-model factors that may cause the predicted classes to concentrate on a few categories.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "1. Clear Structure: The article is well-organized with distinct logical sections, progressing from introduction to methodology and then experiments, which allows readers to easily grasp the research content and contributions.\n\n2. Professional Expression: Accurate terminology, rigorous mathematical formulas, and clear presentation of charts and experimental results enhance the article's scientific validity and readability.\n\n3. Dynamic and Selective Reset Mechanism: The ASR scheme dynamically determines the timing and scope of resets, effectively preventing model collapse while minimizing knowledge loss.\n\n4. Enhanced Comprehensive Adaptability: By integrating knowledge recovery and dynamic adjustment mechanisms, ASR improves the model's adaptability in complex environments and achieves outstanding performance.", "weaknesses": "1.The author aims to address the challenge of model collapse in long-term TTA. The proposed ASR module triggers resets based on the concentration of predicted classes. However, there is an issue here. In long-term TTA, model collapse can lead to increased concentration of predicted classes. Yet, in real-world scenarios, such increased concentration could stem from various factors. For instance, the distribution of test data is often non-stationary and imbalanced. Test data may temporarily concentrate on a few classes over time, which is not due to any issue with the model itself. The author needs to analyze this aspect.\n\n2.In Section 3.2, the calculation of prediction concentration seems to indicate that a higher value corresponds to lower class prediction concentration. However, the reset criterion set by the author is that prediction concentration is higher than cumulative concentration. Is there an error here? The author is advised to double-check.", "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)