PACE: Pretrained Audio Continual Learning
Authors: Chang Li
Kanglei Zhou
Liyuan Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Audio recognition
Continual Learning
Incremental Learning
Catastrophic forgetting
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Audio is a fundamental modality for analyzing speech, music, and environmental sounds. While pretrained audio models have significantly advanced audio understanding, they remain fragile in real-world scenarios where data distributions evolve over time. In this work, we present the first systematic benchmark for audio continual learning (CL) with pretrained models (PTMs) and provide a comprehensive analysis of its unique challenges. Unlike in the vision domain where parameter-efficient fine-tuning (PEFT) has proven effective for CL, directly applying such strategies to audio leads to poor performance. This is due to a fundamental property of audio backbones: they emphasize low-level spectral details rather than structured semantics, resulting in severe upstream–downstream misalignment. Through extensive empirical analysis, we identify a promising technical route based on analytic classifiers with first-session adaptation (FSA), but also uncover two major limitations: representation saturation in coarse-grained scenarios and representation shifts in fine-grained scenarios. To address these challenges, we propose **PACE**, an innovative method that improves FSA via a regularized analytic classifier and introduces multi-session adaptation through adaptive subspace-orthogonal PEFT for better semantic alignment. Additionally, we design spectrogram-based boundary-aware perturbations to mitigate representation overlap and improve stability. Experiments across six diverse audio CL benchmarks demonstrate that PACE substantially outperforms state-of-the-art baselines, representing a significant step toward robust and scalable audio CL with PTMs.

Published as a conference paper at ICLR 2026
PACE: PRETRAINED AUDIO CONTINUAL LEARNING
Chang Li; Kanglei Zhou, Liyuan Wang
Department of Psychological and Cognitive Sciences, Tsinghua University
ABSTRACT
Audio is fundamental modality for analyzing speech, music, and environmental sounds. While pretrained audio models have significantly advanced audio un- derstanding, they remain fragile in real-world scenarios where data distributions evolve over time. In this work, we present the first systematic benchmark for audio continual learning (CL) with pretrained models (PTMs) and provide a com- prehensive analysis of its unique challenges. Unlike in the vision domain where parameter-efficien fine-tuning (PEFT) has proven effective for CL., directly ap- plying such strategies to audio leads to poor performance. This is due to fun- damental property of audio backbones: they emphasize low-level spectral details rather than structured semantics, resulting in severe upstream downnstteddwntrmmmmmmmm.. mis- alignment. Through extensive empirical analysis, we identify promising techni- cal route based analytic classifiers with first-session adaptation (FSA), but also uncover two major limitations: representation saturation in coarse-grainet sce- narios and representation shifts in fine-grained scenarios. To address these chal- lenges, we propose PACE, an innovative method that improves FSA via regular- ized analytic classifier and introduces multi-session adaptation through adaptive PEFT for better semantic alignment. Additionally, we design spectrogram- based boundary-aware perturbations to mitigate representation over- lap and improve stability. Experiments across six diverse audio CL benchmarks demonstrate that PACE substantially outperforms state-of-the-ee baselines, repre- senting significant step toward robust and scalable audio CL with PTMs.
INTRODUCTION
Audio central to human communication and environmental perception, supporting numerous ap- plications such as speech recognition HAbdel-Hamid et al. 2014), acoustic event detection (Zhuang jet al., 2010), and sound scene understanding (Nakamura et al. 2000; Zhou et al.) (024c). With the rise of large-scale supervised (Gong et al. 2021) and self-supervised pretraining (Gong et al.) 2022 Chen et al.) 2024 2022. Li et al. 2024b), pretrained audio models have achieved remarkable success across a wide range of downstream tasks. However, in real-world scenarios where audio distributions evolve continuously, these models often struggle to effectively adapt without incurring catastrophic forgetting, exposing key limitation for audio-related applications.
Continual learning (CL) aims to address this limitation by enabling models learn new tasks while retaining old knowl- edge (Zhou al., 2025b; [2244]] (2025)).
Casss
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Aig Intan-Clase Dicaon: 17 315 15
Aug Intwe-Cwee Daadan: 27.574
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CL with pretrained models (PTMs) (Wang) ett al. 2022b a). their extension to audio remains largely underexplored and highly non-trivial. Pretrained vision models gen- crally encode stable and well-structured semantic representations (Janson et al.).
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Figure 1: Audio CL (a) on SC2 suffers from clearly much stronger representation shifts between adjacent sessions than vision CL (b) on ImageNet-R.
"Co-first author 'Corresoondng author:

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Published as a conference paper at ICLR 2026
leading to relatively mild representation shifts across adjacent sessions (see Fig. I(b) b. In contrast, audio recognition depends heavily on fine-graine see-ggraned spectral cues (Wang et al.) Wu et al. whereas CL requires progressively adapting high-level semantic objectives to acquire discriminative representa- tions. Pretrained audio models (Chen et al. 2024. Gong et al., 2022 Alex et al. 2025). typically trained via spectrogram reconstruction objectives, prioritize level timefrrqueem patterns over structured semantics (Tabassum et al., 2024 Bao et al.; [psteei & Meir 2019). Despite the learning objective, the cestremm-dowstree the pretraining and downstream data distributions, reflected in Fig. [0] These mismatches force the backbone continually reshape its internal representations across sessions, inducing substantial representation shifts that often surpass the subtle spectral differences between classes, leading to severe catastrophic forgetting (see Fig. [(a).
mismatch can be further enlarged by discrepancies between
To systematically investigate whether and how pretraining- CL methods can be effectively applied to audio, we construct a comprehensive benchmark and uncover three key findings: First, we find that representative vision-domain CL methods, particularly those relying on task-shared representations, exhibit significant performance degradation when transferred to audio. We identify simple but effective technical route, which integrates first-session adaptation (FSA), backbone freezing, and second-orded analytic classification (McDonnell et al.] [023) Zhuang et al., (022). Second, although this approach achieves strong performance on grrrneegraieed benchmarks with relatively small domain gaps, it exhibits representation saturation when learning the first task (Li al., 2020), which hinders subsequent adaptation. Third, this approach becomes less effective when applied to the more demanding fine-grained scenarios (e.g., musical instrument and speaker classification) that involve substantial upstream-downstreen mismatch. As a result, pronounced performance gap remains compared to the joint training upper bound.
To close this gap, we propose PACE (Pretrained Audio Continual |Earning). novel method de- signed to fully harness pretrained audio models while overcoming pesstream-downsteer in both coarse- and fine-grained CL scenarios. Unlike vision CL. where freezing the pretrained backbone often suffices (Zhang et al. [023 2024), PACE selectively adapts the later backbone lay- ers with an audio-specific PEFT strategy tailored for FSA, enabling more effective representation learning, particularly on coarse tasks. To extend adaptability across sessions in fine-grained scenar- ios, PACE further introduces (1) multi-session adaptation (MSA), which incorporates an adaptive bosspace-orthogona. PEFT strategy enable progressive adaptation while constraining the drift of previously learned features, thereby achieving principled balance between stability and plasticity; and (2) boundary-aware perturbation mechanism, which applies targeted free-fryyyncc trans- formations to approximate historical decision boundaries, enhancing intra-class compactness and inter-class separability in the learned representation space.
mismatch
We conduct extensive experiments across three coarse-grained benchmarks (ESC-50, US8K, SC2) and three fine-graiin fine-grained benchmarks (TIMIT-2, TIMIT-3, VocalSet). PACE consistently outperforms -tate-of-the-arr CL methods, with notable gains of at least +5.3% on TIMIT-2, +4.1% on TIMIT- 3, and +6.3% on VocalSet. Moreover, significantly reduces the gap the joint training upper bound, achieving performance within 0.8% on ESC-50, 0.6% on US8K, 3.5% on SC2, 4.3% on TIMIT-2, 1. 1.2% on TIMIT-3, and 7.6% on VocalSet. To facilitate future research, we will release all constructed benchmarks and reproduced baselines along with our codebase.
BENCHMARKING AUDIO CONTINUAL LEARNING
To systematically investigate audio CL with PTMs, we first introduce the problem formulation and then present comprehensive benchmark results that reveal the unique challenges of this setting.
Pretrained CL. CL with PTMS assumes access to a pretrained backbone fo parameterized by Oo obtained from a source domain, which is incrementally adapted to sequence of T tasks Ti..... Tr without retraining from scratch. Each task T, = (D,, updates the model from 0.-1 to 0g using Dr, and evaluation is performed over the accumulated label space V.
Pretrained Audio CL. In audio CL, each input In,1 E X, is raw audio signal, and each label Yn.t E 3/ belongs to a task-speeific category, where ) ny, = @ for + j. The objective is to learn from sequential datasets Di, Dr while preserving performance all previous classes. Unlike
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Benchmarka
Banchmarks
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DiaPrrong
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PEFT4T
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Sirzzon
Sersion
Seeaion
Sarsion
Sweston
Semaon
Comparison of vision CL and audio CL. (a) and (b) present performance patterns on audio and image datasets in both coarse- and fine-grainet settings. (c) shows that, despite strong first-task plasticity with PEFT-FT, large representation shifts lead to severe forgetting.
Figure 2:
vision CL, pretrained audio models face additional challenges in this setting due to fundamental mismatch between pretraining objectives and downstream task granularity.
In our empirical evaluation, we adopt EAT (Chen et al. 2024), general-purpose audio self- supervised model pretrained on large-scale audio and speech datasets (Gemmeke et al. 2017), as the default fo. We construct six representative audio CL benchmarks. The first three, ESC-50 Piizzak 2015). UrbanSound8K (Salamon et al., 2014). and Speech Commands V2 (SC2) (Warden 2018).
represent coarse-grained tasks such environmental sound classification and keyword spotting. These tasks are relatively well aligned with the EAT pretraining objective (Li & Angelov 2024),
leading to comparably smaller upstream-downstremn mismatch. To explore more challenging sce- narios involving severe distribution changes, we further introduce three fine-grained benchmarks: TIMIT-2&3 (Garofolo et al. I993) for speaker identification, and VocalSet (Wilkins et al., 2018) for musical instrument recognition. These tasks demand structured semantic understanding that is notably misaligned with HAT's pretraining, thus posing greater challenges for CL. Detailed dataset description and task configurations are provided in Sec. 4.1 and Sec.
From the benchmarking results, we identify three key empirical findings:
Finding 1: Vision CL methods degrade on audio tasks. As shown in Figs. 2(a) and 2(b) di- rectly transferring CL methods from the vision domain to audio understanding tasks yields different performance patterns. In particular, PEFT-based CL. methods such as L.P (Wang et al. 2022c). DualPrompt (Wang et al. (022b), and S-Prompt++ (Wang et al. (022aa) exhibit significantly worse performance in the audio domain, with degradation nearly three times larger than in vision. These methods rely on shared representations for prompt-key matching or mask-incremental adaptation, which appear less effective when handling the fineigrained spectral structures of audio. In contrast, statistics- based methods use a once-tuned backbone with an analytic classifier built on second-order statisties, exemplified by RanPAC (McDonnell al., 2023) and ACL (Zhuang et al., (022), and consistently deliver stronger, more stable results compared to PEFT-based methods in audio CL. We attribute this performance gap the pronounced representation shifts in the audio domain (see Figs. ((a) and lbb)). which manifest in rapid and substantial forgetting once new session is learned (see Fig. 2 While RanPAC sacrifices model plasticity for continual updates, it mitigates the more severe audio representation shifts, making this trade-off more suitable for audio CL.
These observations motivate us to adopt an analytic classifier with second-order statistics upon a frozen backbone as the foundational technical route for pretrained audio CL.
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Figure 3: Analysis of representation tuning in CL. (a) and (b) show RanPAC's first-session, future- session, and average performance across FSA epochs relative to joint training. (c) shows the gains from simply freezing shallow layers. (d) is the t-SNE visualization of VocalSet after FSA.
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Finding 2: Representation saturation on coarse-grained datasets. As shown in Figs. and 3(b) RanPAC achieves high first-session accuracy on coarse datasets even without FSA, sug- gesting a relatively small domain gap between pretraining and downstream tasks in these scenarios. Although FSA further improves first-session accuracy, it fails to meaningfully improve future-task accuracy, even with extended training. leaving a noticeable gap to the joint training upper bound. This indicates that the pretrained backbone already captures most of the relevant information in the first session, limiting its ability to extract additional discriminative features to benefit subsequent tasks, phenomenon we refer to as representation saturation. Furthermore, Fig, [(c] shows that freezing shallow layers during FSA improves performance, while full-layer tuning often degrades it, even falling below the no-FSA baseline. This highlights the risk of blindly fine-tuning all layers, which can erode generalizable low-level representations obtained from pretraining.
((a)
Finding 3: Larger performance gap on fine-grained benchmarks. When applied to fine-grained scenarios, the identified technical route (FSA and analytic classification) quickly degrades, exposing substantial limitations caused by tatreaammmowwstreamm mismatch. As shown in Fig. (3d) while FSA improves clustering of first-session classes, it fails to produce coherent distributions for future-session classes. Moreover, Table | shows that extended training on the first session can worsen performance on subsequent tasks, sug- gesting a strong tendency toward overfitting. This effect is especially pronounced in datasets with high semantic complexity. For instance, on VocalSet, the performance gap relative to joint training reaches 13.8%, compared to only 3% and 1% on ESC-50 and UrbanSound8K, respectively. These results suggest that first-session data alone is insufficient to bridge the semantic gap between pretraining and downstream objectives in fine-graines audio tasks. Together, these findings highlight the need for progressively aligning pretrained representations with downstream tasks over multiple sessions, while avoiding overfitting in early-stage adaptation.
Method w/o FSA Naive FSA
TIMIT-2 VocalSet
75.87
61.51
89.92
62.85
Extended FSA
83,25
61.18
Joint Training
95.22
76.65
Table I: Preliminary results on fine- grained benchmarks.
OUR PACE METHOD: PRETRAINED AUDIO CONTINUAL LEARNING
3.1 NOTATIONS AND DESIGN OVERVIEW
Notations. Let f(() denote pretrained backbone with L layers and g(.) alassification head. Given an input audio signal, we first compute its time-frequency map x E RTTxP== (e.g.. via STFT followed by Mel filtering), where T3 and Fz are the numbers of frames and Mel bins, respectively. The backbone produces a representation z = f()) E RD,, where D is the feature dimension. 2 is then passed through the head predict class probabilities ý g(2) in V.
Design Overview. Motivated by the empirical findings in Section [ we introduce PACE (Fig. 4 a unified, stage-wise framework for realigning pretrained audio representations with continual learn- ing (CL) objectives. We decompose the problem into two components: the pretrained backbone and the output head, and design targeted strategies for each.
Improved First-Session Adaptation (FSA). Empirical evidence from Finding and the embedding-space analysis in Fig. [ shows that naive backbone adaptation causes severe cross- session representation shift. This motivates adopting an analytic classifier as the default technical route. As shown in Finding 2, pretrained audio models already encode strong coarse-grained se- mantics, making them prone to first-session saturation. Naively tne-ttning the output head distorts these representations, limiting forward learning. To address this, we propose improved first-session adaptation (FSA) that freezes the output head and adapts only deeper layers using LoRA modules (A/ B Taune < S T.). Once adapted, the parametric head (-) is replaced with an analytic classifier o² (²(-) preserve pretrained semantics while avoiding unnecessary parameterization.
Multi-Session Adaptation (MSA) with Subspace Projection. Although FSA performs well on coarse-grained tasks, it is insufficient for fine-grained scenarios where stronger up- mismatch exists. Thus, we introduce multi-session adaptation (MSA), which progressively aligns representations using multiple sessions' distributions. However, vealed by Findings & 3. naive MSA exacerbates the representation shift, resulting in catastrophic forgetting. To ensure replay-free solution, we allow adaptation only when needed and constrain it
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Stage Pretraining
Stage
Stage 2: Subspace-Orthhggnnl PEFT
Stage Backbone
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Figure 4: The proposed PACE framework. Stage performs first-session adaptation with LoRA, followed by analytic inference. Stage introduces subspace-orthogonn PEFT via LoRA subtraction and gradient projection. Boundary-awand regularization involves adaptation in the first two stages. Stage freezes the backbone stable adaptation. #: frozen; è: tuning; -x: adaptation path.
via subspace-orthogonal MSA. In sessions < < Ta. gradients are projected onto an interference- free subspace Uú (via LoRA subtraction (Liu & Chang 2025 ), enabling controlled updates without distorting earlier knowledge. Inference remains analytic via @e(-).
Boundary-Award Regularization. One remaining challenge highlighted by Finding 3 is the over- lapping class boundaries. As representations stabilize, new classes may be forced into suboptimal, overlapping regions. This degrades both adaptation and generalization. To mitigate this, we intro- duce boundary-awan regularization. For each sample Zi,t, we generate perturbed variants E; that approximate class-boundary regions Bt. During training, Fi, pulled toward its class prototype (xxe) and pushed away from B., increasing inter-class margins and promoting compact, separable representations. This reduces boundary collisions and improves stability during MSA.
Each component of PACE is detailed in the following sections, including the improved FSA (Sec. 3.2pp and the subspace-orthogona MSA (Sec. 3.3) with boundary-awart regularization.
3.2 IMPROVED FIRST- ADAPTATION (FSA)
Empirical analysis shows that in coarse-grained audio CL, pretrained models already provide strong semantic priors. However, naively fine-tuning the full model during the first session tends disrupt these well-aligned representations, leading to early saturation. This indicates that the first session should emphasize targeted refinement, not full backbone adaptation. To this end, our improved FSA incorporates three key ideas: (1) restricting updates to the output head so that gradients flow primarily into the backbone; (2) adapting only deeper, semantic-relevan layers; and (3) replacing the trainable head with analytic classifier after adaptation to ensure stability in later sessions.
Restricted Head Learning. Existing FSA methods jointly train linear output head with essentially frozen backbone, which often causes the output head to overfit while leaving the back-
1.0
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bone insufficiently adapted for meaningful refinement. To ad- dress this, we introduce two modifications: (1) enforcing im- balanced optimization by setting the head's learning rate 7head significantly lower than that of the backbone Thb; and (2) adopt- ing staged strategy that first trains the head for Freat epochs with the backbone frozen, followed by backbone fine-tuning for Eo epochs with the head fixed (Ehead < Eσ). This asymmetric training scheme compels the backbone absorb most gradient signals, progressively enhancing representation quality with lim- ited data and achieving performance close to the upper bound. It worth noting that our strategy is opposite to those of LAE (Gao al. 2023) and SLCA (Zhang al. 2023 where backbone
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Figure 5: CKA (Kornblihh et al. 2019) visualization shows repre- sentation changes of the first ses- sion classes across layers CL.
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updates are suppressed to mitigate forgetting. This contrast highlights the unique characteristics of audio backbones, where selectively encouraging backbone adaptation is critical for effective transfer without incurring catastrophic forgetting.
Later Layer LoRA. The empirical analysis in Fig. 3(c) suggests that the first-session performance may improve when adaptation is restricted to deeper layers, i.e., freezing early layers while tuning later ones. This aligns with the hierarchical structure of audio models: shallow layers tend to encode domain-general time-frequency and acoustic patterns (Nizzumi al. 2021] Cramer et 2019). whereas deeper layers capture higher-level semantic abstractions that are more task-specific (Liu al., 2020a Baevski et al., 2020). Further supported by the centered kernel aligmment (CKA) visualization in Fig. which shows that the model tends to stay relatively stable in the early layers, we freeze encoder layers through Tumee and only adapt layers Tuune- The boundary layer Tuuee is determined via representation shift analysis during full fine-tuning, we select the shallowest layer whose CKA deviation from the pretrained model exceeds a threshold Player- Implementation details are provided in Algorithm and elaborated in Sec. B
Combining the above strategies, we apply LoRA to the tunable layers E [L LJ. where L denotes the total layer number. For each such layer, the adapted weight is given by:
w W/ + A, for Laune S SISAA L,
where W E Idd is the pretrained weight, and A E Raxr, Bi E <Rrdd are trainable low-rank matrices with rank r < d. This design allows to efficiently refine task-relevant semantic features while preserving general audio representations, yielding robust FSA without full model tuning.
Analytic Classifier. To maximally leverage the stabilization of prior representations and prevent accumulated biases from a trainable head h (-), we adopt an exemplar-free recursive analytic clas- sifier (MeDonnell et al. 2023) for final predictions. Given a random projector W proj Dx Dyoj to enhance feature discrmmnaillty (Tran et al. 2025). we can obtain the projected feature ma- trix 24 = W proj Zs [a.,....... .,....,,))
E N and corresponding one-hot label matrix Y, E R the autocorrelation matrix R is initialized with regularization term 7 0, i.e.,
R2 2, 2. 71)=1 This is then recursively updated using the Woodbury identity:
R₂ R R R
12. )
Classifier weights W. are then updated via a closed- rule:
We W,-1 R,2, 2,W. + R,2, Y..
In inference, a new feature 2,, is classified by: D1. = dr (W = 2, We. This design al- lows continual, exemplar-free updates to decision boundaries while preserving alignment with the stabilized representation space, ensuring robust and non non-destruetive learning.
3.3 ADAPTIVE MULTI- SUBSPACE-ORTHOGGE
PEFT
FSA (Zhuang al. 2022 McDonnell et al. 2023) mitigates catastrophic representation shifts in the audio domain by refining pretrained representations during the first task. While this is often sufficient for coarse-grained datasets with minimal domain gap, it severely constrains backbone plasticity, limiting its applicability in fine-grained scenarios. In such settings, bridging the semantic mismatch between pretraining and downstream tasks requires learning new representations beyond those established in the first session. To address this challenge, we propose Adaptive Multi-Session Subspace-Orrhhggnaal Orthogonal PEFT that enables adequate adaptation across multiple sessions while avoid- ing destructive interference with previously acquired representations. Our key idea is to leverage data from multiple tasks to reshape the representation space, aligning it with downstream semantics under large domain gaps, while simultancously preserving the geometry of the previous representa- tion space to maintain compatibility with the analytic classifier. Once further backbone adaptation offers diminishing returns, we freeze the backbone for long-term stability, entering Stage 3 (t > Ts).
Multi-Session Adaptation (MSA). We extend FSA to the multi-session setting by introducing session-specife LoRA (Hu al. 2021), allowing each session to adapt the backbone while taining the parameters from previous ones. Specifically, for each session t E (1, we augment the base weights Wo with new low-rank updates A,B forming the session-specific parameters

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W. WT + E B,A, + B where B,,,B,Y: are frozen prevent retroactive inter- ference. However, updates may still misalign with earlier representations, leading to catastrophic forgetting decision boundaries. To counter this, aim to ensure that the backbone update 9update does not significantly alter the representations of past tasks. Formally, for any old sample Ti,r E Xr with 7 < i, the representation change should satisfy
(4) where 7hb is the backbone learning rate. To achieve this, we constrain gupdare to lie in the null space of the subspace spanned by previous representations. Specifically, let Joriginal denote the gradient computed from the cross-entropy loss Lce between the current model predictions (f)( and their labels V/. We then define the projected update as
(zi,)) = 7/o Sup = O,
(5) where Pu, = U,U,T, and U. is an orthonormal basis spanning the null subspace Uy of all previ- ously acquired representations. This projection ensures that adaptation updates minimally affect old samples, thereby preventing distortion of learned representations and maintaining stability in CL.
Jupdate = Puu, Goriginal = Puu (efco(9(((((())))
To compute the null space Ur, naive way would require storing all historical features from 2024b), resulting in extensive storage overheads. Inspired by
X1 Xy..... X,-1 (Wang et al. 2021
LoRA Subtraction (Liu & Chang 2025 Ilharco et al.] (203)), we instead construct unlearned
model by subtracting all previous LoRA parameters W.t Wunliam Wo - - A, B,,
For computational efficiency, we then compute the uncentered covariance matrix of the current ses- sion's features by Xueov = f funleum (X.) funleum (X,) E RDxD, which shares the same principal subspace with fiiblaari (2t) E N, where fualeam (-) denotes the frozen feature extractor using Wunneam and D the feature dimension. Through performing singular value decomposition (SVD) on XTIov, we obtain its eigendecomposition: Xiucor = (U!) and define the layer-wise pro-
jection operator as Pap: U...1.mm (U, (1.)))) where
Σ A
m arg max
> Psvd. X : i-th max diagonal entry of A4.
(6)
VIX Ei=1 A
Despite orthogonal projection, continual adaptation can still accumulate shifts that subtly degrade carly-session compatibility, especially when class sizes are small. We empirically find that control- ling the cumulative number of seen samples offers a good stability-plasticitt trade-off. Specifically, we define the threshold as Ta = arg maxt, Σ/= N₂ > Naop: where Natop is a threshold controlling the stability-plasticity trade-off. Once the backbone stabilizes, we transition to Stage 3 by freezing the backbone to preserve learned knowledge and update only the analytic classifier thereafter.
Boundary-Aware Regularization. While MSA preserves representations of previous sessions, may reduce the plasticity needed for learning new sessions (Liu & Chang 2025), especially when new- and old-class representations become entangled. This entanglement can cause updated decision boundaries to confuse past representations. To enhance separation, we introduce a boundary- aware regularization that encourages intra-class compactness and inter-class margin enlargement.
Specifically, for each input Fi,t at session f, we generate perturbed samples att Q( (r.rr) .r) using time-frequency masking (Park et al., 2019). where Q randomly masks time and frequency dimensions with ratios rT < Rt and I'F RF. producing N, perturbations per input. To detect boundary-prone samples, we examine whether these perturbations are consistently misclassified by temporary model &ump- which frozen the backbone from the previous model 6:-1 with only the classification head adjusted to session t. We include such samples in the boundary set B₁:
1 Ny
B, = fi-
Σ
1 [ (5. # vi.1 > pr)
NT k=1
where 1(.) is the indicator function, and Pp is the misclassification threshold. To regularize representations, we define the following loss for each clean input and its perturbations:
Greg (i) = max
0,
8 +
1
Σ N.S.
[[f((w) F(x))11 min BeB,
[lf(( - b112
(8)
S
No and ((x.) is the centroid of class c. This pulls features toward their class center and pushes them away from nearby boundary points, mitigating future confusion.
where S, = {xits _!.......

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4 EXPERIMENT
4.1 EXPERIMENTAL SETTING
We adopt EAT (Chen et al. 2024), a general-purpose self-supervised audio backhone pretrained on M (~~ 5000 hours) (Gemmeke al., (017). as the default implementation. EAT is a spectrogram- baaee masked prediction model following ViT architecture with 12 Trans- former (Vaswani et al. 2017) blocks. We benchmark performance across 3 coarse-grained and fine-grained datasets, spanning environmental sounds. speech, and music. All datasets are randomly split into an 8:2 ratio for training and testing. Additional details are provided in Sec. D
Coarse-graines Audio CL Benchmark. ESC-50 (Piczak 2015) consists of 2000 samples from 50 classes (5 seconds each), covering numerous environmental sounds such as barking, rain, doorbell, and sawing. UrbanSound8K (US8K) (Salamon et al. 2014) contains 8732 samples from 10 urban sound classes (up to 4 seconds each), representing typical city sounds such as air conditioner and car horn. Speech Command v2 (SC2) (Warden 2018) includes 105k one-second recordings from 35 speech classes for keyword recogmition. For CL. ESC-50 is split into 10 sessions with 5 classes each, US8K into sessions with 2 classes each, and SC2 into sessions with classes each. These datasets are either Nomain-matched with the pretrained model or well-adapted in the first session.
Fine-grained Audio CL Benehmark. TIMIT (Garofolo et al., 1993) classic speech corpus with 630 speakers from 8 U.S. dialects, each reading 10 phonetically rich sentences, totaling about 6300 utterances. We reformulate TIMIT as a continual speaker identification benchmark with 2 set- tings: TIMIT-2 (315 tasks with 2 speakers each) and TIMIT-3 (210 tasks with 3 speakers each). VocalSet (Wilkins et al. 2018) is a curated dataset for singing technique recognition and singer identification. It contains about 3560 clips (=10 hours) from 20 singers, covering 16 vocal tech- niques such as vibrato and belt. To balance the dataset, we randomly sample 79 clips per technique (64 for training and 15 for testing), and further split them into sessions with 2 classes each.
Baseline Methods. We adopt the EAT (Chen et al., 2024 backbone, pretrained on AudioSet- 2M (Gemmeke et al.) (017) with ~-50000 hours of audio. EAT follows the VIT design with 12 Transformer (Vaswani et al. (017)) blocks. To validate the effect of pretrained audio CL models, we
consider state-of-the-arr (So baselines developed for vision domain, including (1) PEFT-based methods such as L2P (Wang et al., (022c). DualPrompt (Wang et al. (2022b). S-Prompt++ (Wang al. 2022a), LoRASub (Liu & Chang] (025), and HiDe-PET (Wang et al. [2023] 2025), and (2) statistics-based methods, such as Nearest-Prototy
Classification (NPC) (Rebulfi et al. 2017b), RanPAC (MeDonnell et al. 2023) and ACI. (Zhuang et al. 2022).
4.2 EXPERIMENTAL RESULTS
Overall Performance. We evaluate PACE against SoTA methods across six audio CL benchmarks, using the supervuud pretrained audio model EAT (Chen et al., 2024]. In Table PACE consis- tently outperforms all baselines, achieving the best performance across both coarse and fine bench- marks. We also conduct additional benchmarks non-human audio and cross-domain evaluation, and further validate the effectiveness of PACE with another self-supervised pretrained audio model SSLAM Alex et al. 2025), as shown in Sec. E,3 and Sec. E.S] respectively.
On coarse-grained benchmarks (ESC-50, US8K, SC2), joint training with LoRA yields strong results by fully leveraging task-specific supervision, highlighting the potential backbone plasticity under non-continual conditions. However, prompt-based methods such as L2P, DualPrompt, and S-Prompt++ perform poorly CL due to their reliance on shared prompt keys, which are highly vuinerable to forgetting. Their hybrid use of task- shakes and task task-specific components often induces representation shifts, sometimes performing worse than naive PEFT. HiDe-PET partially addresses classifier forgetting via feature replay, but its effectiveness is limited as the stored features them selves suffer continual representation shifts. LoRASub mitigates drift to some extent but still inher- its continual classifier degradation and requires parameter expansion over long task sequences (e.g.. TIMIT). In contrast, statistics-bat based methods such as RanPAC and ACL freeze the backbone and rely on FSA with analytic classifier, offering more robust performance by avoiding drift. Nonethe- less, they encounter a ceiling due to representation saturation, particularly in early adaptation. PACE overcomes this limitation via layer-aware tuning and adaptive subspace-orthogonal regularization,
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Published as a conference paper at ICLR 2026
Table A sample of average top-1 accuracy (%) of different methods on six audio CL benchmarks.
Coarse-Grained
Fine-Grained
ESC-50
US8K
SC2
TIMIT-2
TIMT:3
VocalSet
Naive Methods
EAT (LoRA)) Joint Training
96.50
98.07
95.91
95.22
95.22
76.65
EAT (Frozen) + Lincar probe
40.50
49.99
32.84
2.30
4.92
13.82
EAT (Prompt) Linear Probe [Lsterr al.
2021
58.75
44.98
41.54
1.67
1.43
22.04
EAT (Adapter) Lincar Probe
2019
59.25
38.76
55.33
6.22
14.12
16.45
EAT (LoRA) Linear Probe
Hü ai. 2021
64.00
49.68
30.56
0.00
2.62
15.79
PEFT-Bosed CL Methods
L2P Wang et al. 2022c
39.50
38.75
14.70
1.50
2.53
20.39
rompl Wang el
2022b
$7.00
42.40
21.92
5.87
10.00
12.50
S.Prompt++ Wang al.
8.25
HiDe-Prompt Wane
2025
83.75
79.89
40.10
47.78
49.60
48.36
HiDe-LoRA Wany al
21255
88.75
76.48
33.66
47.30
49.60
46.05
HiDe-Adapter Wang al
2025
82.75
78.03
33,71
7.14
12.22
49,67
LoRASub (Liu & Chang
57.50
57.81
34.24
0.00
0.00
Statistics Rased CI. Methods
Nearest Class Mean (NCM) (Rehulli at. 2017h
33.25
36.09
9.30
6,90
6.83
32.89
Nearest Class Mean (NCM) ESA
49.00
42.44
57.60
23.97
34,68
34,53
ACL (High-Orden) (Zhuang et al. 2022
90.00
95.98
80.29
75.56
75.56
62.50
(High- Order) RanPAC (High-Order)
75.87
75.87
MMDDoneel al. 2023
92.25
97.08
90.53
85,63
89.92
62.82
PACE (Ours)
95.75
97.49
91.87
90.95
94.05
69.08
Table 3: Ablation results of our improved FSA on coarse coarse-grained datasets.
Table 4:
Ablation results of our key components on fine ane-grained datasets.
Method
ESC-50
US8K
SC2
Method
TIMIT-2
TIMIT-3
VocalSet
w/o FSA
92.50
96.49
81.22
Ours
90.95
94.05
69.08
Naive FSA
92.25 0275
97.08 ears
90.53+ 11.3++1-446
Ours w/o FSA
75.87 1885 75.87 19.3%
61.51
-1.01% 10.0%%
Low Learning Rate
93.75*
1.9% 97.35+ nass
90.95+1 989
Ours who MSA
85.63 sws
89 92.92
4.405
62. 22.82- 9086
Learning & Freeze
94,50+
2 1s
97.38 ovns
91.30* -1 17744%
Ours w/o Greg
89.21
1919
93.73"
0.545
66.78 1.1%6
Our FSA
95.75
3.19 97.49* uuss
91,87+13.005
Ours w/o GP
88.01 3229
89.05
5311
58.55
15.206
enhancing representation plasticity while maintaining stability. As result, it consistently narrows the gap to the joint training upper bound (e.g.. 0.75% on ESC-50, 0.58% on US8K).
On fine-grained benchmarks (TIMIT-2&3, VocalSet), we observe significantly lower performance of all baselines, including joint training, highlighting the inherent domain gap in these tasks. TIMIT in particular suffers from instability due to the large number of classes (up to 630). Prompt-based methods consistently fail in these settings. Statistics -hased methods, while more stable, leave a substantial gap relative joint training. In contrast, PACE substantially narrows this gap, FSA- based statistical methods alone are insufficient to effectively align representations with downstream domains. By augmenting FSA with MSA, while simultaneously mitigating representation forget- ting, combined with audio-specific PEFT and tailored perturbation loss, PACE achieves more dis- criminative and stable representations. Consequently, demonstrates stronger alignment between pretraining and downstream tasks in fine-grained scenarios, where the performance gap from joint training is much smaller (4.27% and 1.17% on TIMIT-2 and TIMIT-3, 7.57% on VocalSet).
Ablation Studies. Since coarse grained tasks are well handled by FSA alone, but fine tineggrained tasks require further adaptation via MSA, we evaluate FSA design choices on coarse datasets (Table 3 and ablate all core components on fine-grained datasets (Table
Table 3 demonstrates that our improved FSA (see Sec. 3.2 outperforms the naive FSA by combin-
ing two key design choices: restricted head learning (via low learning rate and early freezing of the classification head) and controlled adaptation later transformer layers. To further examine the sensitivity of the layer-freezing threshold, we select Player 0.94 through grid search fine-grained datasets. This threshold determines which layers remain frozen during adaptation (see Fig, 6(a). As shown in Fig. (6bb) freezing exactly these layers yields the best performance, confirming the impor- tance and effectiveness of later-layer adaptation. In addition to FSA, Table 4 shows that the full PACE model arises from the complementary contributions of its core components, including MSA

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Published as a conference paper at ICLR 2026
CKA Laye's [X4)
Aporreay (99)
T1472
TRNT
VoceBet
MSA (Chcser)
ESC-00
0:357
98.25
68-
USBK-
0:375
0.734
0.992
0.743
57.0 08
37.58
$7.35
96.25
90-
55.92
K15A (Chosen)
2
66-
FSA
502-
0.289
0.441
3.932
0.048
50.55 53
54
$0:30
50.73
0.788
05.63
63-
WhoeBet-
0.214
0.000
2.502
0.018
21
13.22
04.20
73.07
PSA
62-
wi-
0
75
Liger
Preaze Layer
(4)
MEA1
Truks
[0]
MSA Tooos
Figure 6: Sensitivity of the Prozen-layer number (Luune) within our FSA strategy.
Figure 7: Sensitivity of the adaptation-session
number (Lez) within our MSA strategy.
Speckag
(e) Oura 1oo
(b) Oura wia MBA
[c] Oura wis GP
000 542
45-
70 No4 7244
PA7 STAR N0. 21.8
0.
tax 53. 727
48
11. 753 71 15
-14
71. 00. 0-2 75 08
00
253 T1 1
67
242
22
N5. 71. 3 5551 1IKE
7M
550 253 153 53 05 AT
08
253
68-
54
05
48
48
22
95
26
57
18
24
15 10
Figure 8: I-SNE visualization compar- Figure 9: Heatmap visualization of the accuracy across ses-
ing different perturbation effects.
sions: (a) Ours, (b) Ours w/o MSA, and (c) Ours w/o GP.
gradient projection, and boundary-aware regularization (Creg). Each component pro- vides a distinct benefit, and removing any of them leads to a clear performance drop on fine-grained tasks. We additionally examine the sensitivity of the number of adaptation sessions in our MSA strategy. In Fig. 7, performance remains stable across a broad range of adaptation-sesee demonstrating that PACE is robust to this hyperparameter and that our early-stopping mechanism effectively identifies an appropriate adaptation horizon, as further detailed in Sec. E. We also examine the remaining hyperparameter sensitivities in Sec. E.6
(see Sec. 3.3).
settings,
Visualizations. To aid in the better understanding of our method, we provide additional visual anal-
yses. In Fig. [ we compare perturbation effects using t-SNE: additive noise (see Fig. ((a) sig- nificantly distorts the data manifold, whereas fime-freynency masking (see Fig. 8(b) preserves local neighborhood structure and class consistency, making it suitable for boundary aware regular- ization. Fig. ( visualizes accuracy across sessions, highlighting the stabilizing effect of MSA and GP. Without GP (see Fig. (c.) the model suffers severe catastrophic forgetting: for example, after completing Session 8, the accuracy on Session 1 classes drops from its initial 100% to 7.9%, indi- cating substantial representation drift and boundary collapse. In contrast, full PACE (see Fig. (aa) maintains high accuracy throughout, demonstrating that GP and boundary-aware regularization ef- fectively constrain cross-session interference and preserve earlier knowledge.
5 CONCLUSION
While PTMs with PEFT have enabled substantial progress in vision CL, their direct application to audio faces fundamental challenges due to severe upstream-downstrenn systematic benchmarking, we uncover unique obstacles in audio CL., such as representation satura- tion in early adaptation and representation shift in fine-grained scenarios. To address these issues, we propose PACE, a unified framework that combines selective first-session adaptation, adaptive PEFT, and boundary-aware perturbations to enhance representation alignment and maintain appropriate plasticity-stabilie trade-off. PACE achieves state-of-the-ae perfor- mance across six diverse audio CL benchmarks. Although it requires slightly more adaptation time than RanPAC, the overall training cost remains substantially lower than prior PEFT-based base- lines, demonstrating its effectiveness in both coarse- and fine-grained scenarios. Beyond technical contributions, our work offers a foundation for robust continual adaptation of pretrained audio mod- els. with broad relevance to real-world applications in speech recognition, audio captioning, smart homes, environmental sound understanding, and so on.
mismatch. Through
subspace-orthogonm
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Published as a conference paper at ICLR 2026
Acknowledgment. This work was supported by the NSFC Project No. 62406160 and Beijing Natural Science Foundation L247011 and Beijing Major Science and Technology Project No. 2251100008425003.
Ethics statement. acknowledge that and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Reproducibility statement. We have included the source code with clear instructions, and will release them upon acceptance.
REFERENCES
Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn, and Dong Yu. Convolutional neural networks for speech recognition. JEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(10):1533-1544 2014.
Tony Alex, Sara Atito, Armin Mustafa, Muhammad Awais, and Philip JB Jackson. Sslam: Enhanc- ing self-supervised models with audio mixtures for polyphonic soundscapes. In International Conference on Learning Represenlutions, 2025,
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. wav2vec 2.0: A frame- work for self-supervised learning of speech representations. Advances in Neural Information Processing Systems, 2212222494666 2020.
Ye Bai, Jingping Chen, Jitong Chen, Wei Chen, Zhuo Chen, Chuang Ding, Linhao Dong, Qianqian Dong, Yujiao Du, Kepan Gao, al. Seed-asr: Understanding diverse speech and contexts with Ilm-based speech recognition. arXiv preprint arXiv:2407.04673 2024.
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers. In International Conference on Learning Representations.
Ruchi Bhatt, Pratibha Kumari, Dwarikanath Mahapatra, Abdulmotaleb El Saddik, and Mukesh Saini. Characterizing continual learning scenarios and strategies for audio analysis. arXiv preprint arXiv:2407.00465, 2024.
Umberto Cappellazzo, Daniele Falavigna, and Alessio Brutti. Efficient fine-tuning of audio spectro- gram transformers via soft mixture of adapters. arXiv preprint arXiv:2402. 008288 2024.
Heng-Jui Chang, Saurabhchand Bhati, James Glass, and Alexander H Liu. Usad: Universal speech and audio representation via distillation. arXiv preprint arXiv:2506. 18843, 2025.
Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujic Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, al. Wavlm: Large-scale self-supervised pre-training for full stack speech processing. IEEE Journal of Selected Topics in Signal Processing, 16(6):1505- 1518, 2022.
Wenxi Chen, Yuzhe Liang Ziyang Ma, Zhisheng Zheng, and Xie Chen. Eat: Self-supervised pre- training with efficient audio transformer. In International Joint Conference on Artificial Intelli- gence, 2024.
Aurora Linh Cramer, Ho-Hsiang Wu, Justin Salamon, and Juan Pablo Bello. Look, listen, and learn more: Design choices for deep audio embeddings. In IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3852-38566 IEEE, 2019.
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord. Dytox: Transformers for continual learning with dynamic token expansion. In Proceedings of the JEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pp.
2022.
Baruch Epstein and Ron Meir. Generalization bounds for unsupervised and semi-supervised learning with autoencoders. arXiv preprint arXiv: 1902. 01 044, 2019.
Qiankun Gao, Chen Zhao, Yifan Sun, Teng Xi. Gang Zhang, Bernard Ghanem, and Jian Zhang. A unified continual learning framework with general parameter-efficiee tuning. In Proceedings of the IEFE/CVF International Conference on Computer Vision, pp. 11483-1144 11493, 2023.
11

---

Published as a conference paper at ICLR 2026
John S Garofolo, Lori F Lamel, William M Fisher, David S Pallett, Nancy L Dahlgren, Victor Zue, and Jonathan Fiscus. Timit acoustic-phonetic continuous speech corpus. (No Title), 1993.
Jort Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. Audio set: An ontology and human-labeled dataset for audio events. In IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 776-780. IEBE, 2017.
Yuan Gong, Yu-An Chung, and James Glass, Ast: Audio spectrogram transformer. In Interspeech, pp. 571-575, 2021.
Yuan Gong, Cheng-I Yu-An Chung, and James Glass. Ssast: Self-supervised audio spectrogram transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp- 10699-10709, 2022.
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, An- drea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for nlp. In International conference machine learning, pp. 2790-2799. PMLR, 2019.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.0960 2021.
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In International Conference on Learning Representations, 2023.
Paul Janson, Wenxuan Zhang, Rahaf Aljundi, and Mohamed Elhoseiny. simple baseline that ques- tions the use of pretrained-models in continual learning. In NeurlPS Workshop on Distribution Shifts: Connecting Methods and Applications.
Yuxuan Jiang, Zehua Chen, Zeqian Ju, Chang Li, Weibei Dou, and Jun Zhu. Freeaudio: Training- free timing planning for controllable long-form text-to-audio generation. In Proceedings of the 33rd ACM International Conference on Multimedia, pp. 9871-9971999 1881-9 2025.
Menelaos Kanakis, David Bruggemann, Suman Saha, Stamatios Georgoulis, Anton Obukhov, and Luc Van Gool. Reparameterizing convolutions for incremental multi-task learning without task interference. In European Conference on Computer Vision, pp. 689-707. Springer, 2020.
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka maabssa-Barwinska. al. Overcom- ing catastrophic forgetting in neural networks. PNAS, 2017.
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited. In International Conference Machine Learning, pp. 3519- 3529. PMIR, 2019.
Minh Le, An Nguyen, Huy Nguyen, Trang Nguyen, Trang Pham, Linh Van Ngo, and Nhat Ho. Mixture of experts meets prompt-based continual learning. Advances in Neural Information Pro- cessing Systems, 37: 19025-025111 19025-11906, 19062, 2024.
Brian Lester, Rami AI-Rfou, and Noah Constant. The power of scale for eframeter-efficient prompt tuning. arXiv preprint arXiv:2104.0 00.6994.0 2021.
Chang Li, Ruoyu Wang, Lijuan Liu, Jun Du, Yixuan Sun, Zilu Guo, Zhenrong Zhang, Yuan Jiang, Jianqing Gao, and Feng Ma. Quality-aware masked diffusion transformer for enhanced music generation. arXiv preprint arXiv:2405. 15863, 2024a.
Chang Li, Zehua Chen, Liyuan Wang, and Jun Zhu. Audio super-resolution with latent bridge models. arXiv preprint 250.:v::59..6000000 2025a.
Tingle Li, Baihe Huang, Xiaobin Zhuang, Dongya Jia, Jiawei Chen, Yuping Wang. Zhuo Chen, Gopala Anumanchipalli, and Yuxuan Wang. Sounding that object: Interactive object-aware image to audio generation. arXiv preprint arXiv:2506. 042 14, 2025b.
12

---

Published as a conference paper at ICLR 2026
Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint 11X1:2201100190,, 2021.
Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, and Dejing Dou. Rifle: Backpropagation in depth for deep transfer learning through re-initializing the fully-connected layer. In Interna- tional Conference on Machine Learning, pp. 6010-6019, PMLR, 2020.
Yi Li and Plamen Angelov. Explainable audio-visual representation learning via prototypical contrastive masked autoencoder. In NeurlPS Workshop Supervspee SearinngTThoon and Practice, 2024.
Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma, Xingran Chen, Hanzhi Yin, Chenghao Xiao, Chenghua Lin, Anton Ragni, Emmanouil Benetos, al. Mert: Acoustic music understanding model with large-scale self-supervised training. In International Conference on Learning Repre- sentations, 2024b.
Yan-Shuo Liang and Wu-Jun Li. Inflora: Interference-free low-rank adaptation for continual learn- ing. In Proceedings of the IEEF/CVE Conference on Computer Vision and Pattern Recognition, pp. 23638-23647, 2024.
Alexander Liu, Sang-gil Lee, Chao-Han Huck Yang. Yuan Gong, Yu-Chiang Frank Wang. James R Glass, Rafael Valle, and Bryan Catanzaro. Uniwav: Towards unified pre-training for speech representation learning and generation. arXiv preprint arXiv:2503.0073: 2025.
Andy T Liu, Shu-wen Yang, Po-Han Chi, Po-chun Hsu, and Hung-yi Lee. Mockingjay: Unsu- pervised speech representation learning with deep bidirectional transformer encoders. In IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6423. IEEE, 2020a.
Xialei Liu, Chenshen Wu, Mikel Menta, Luis Herranz, Bogdan Raducanu, Andrew D Bagdanov, Shangling Jui, and Joost van de Weijer. Generative feature replay for class-incremental leam- ing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 226-227, 2020b.
Xuan Liu and Xiaobin Chang. Lora subtraction for drift-resistant space in exemplar-free continual learning. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 15308- 15318, 2025.
Mark McDonnell, Dong Gong, Amin Parvaneh, Ehsan Abbasnejad, and Anton Van den Hengel. Ranpac: Random projections and pre-trained models for continual learning. Advances in Neural Information Processing Systems, 36: 12022-12053, 2023.
Shentong Mo, Weiguo Pian, and Yapeng Tian. Class-incremental grouping network for continual audio-visual learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7788-7798, 2023.
Manjunath Mulimani and Annamaria Mesaros. Online incremental learning for audio classification using pretrained audio model. arXiv preprint PrXXiv2508..07.32 2025.
Satoshi
Nakamura, Kazuo Hiyane, Futoshi Asano, Takanobu Nishiura, and Takeshi Yamada. Acous- tical sound database in real environments for sound scene understanding and hands-free speech recognition. 2000.
Daisuke Nüzzumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, and Kunio Kashino. Byol for audio: Self-supervised learning for general-purposs audio representation. In International Joint Conference Neural Nenworks, pp. 1-8. IEEE, 2021.
Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoe V Le. Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv: arXiv: 01199900477799 2019.
Weiguo Pian, Shentong Mo, Yunhui Guo, and Yapeng Tian. Audio-visual duss-incrrmennal leamn ing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7799- 7811, 2023.
13

---

Published as a conference paper at ICLR 2026
Karol Piczak. Esc: Dataset for environmental sound classification. In Proceedings of the ACM International Conference on Multimedia, pp. 1015-1018, 2015.
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi. Learning multiple visual domains with residual adapters. Advances in Neural Information Processing Systems, 30, 2017a.
Sylvestre-Alviss Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Laamper. icarl: Incremental classifier and representation learning. In Proceedings of the IEEE conference Computer Vision and Pattern Recognition, pp. 2001-2010, 2017b.
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne. Experience replay for continual learning. Advances in Neural Information Processing Systems, 32, 2019.
Justin Salamon, Christopher Jacoby, and Juan Pablo Bello. A dataset and taxonomy for urban sound research. In Proceedings of the ACM International Conference on Multimedia, pp. 1041-1044, 2014.
Riyansha Singh, Parinita Nema, and Vinod K Kurmi. Towards robust few-shot class incremental learning in audio classification using contrastive representation. arXiv preprint arXiv:2407.19265.
2024.
Bob I. Sturm. analysis of the gtzan music genre dataset. In Proceedings of the second ternational ACM workshop on Music information retrieval with user-centered and multimodal strategies, pp. 7-12, 2012.
Bob L Sturm. The gtzan dataset: Its contents, its faults, their effects on evaluation, and its future use. arXiv preprint arXiv:1306. 10o::3306.411 1461, 2013.
Afrina Tabassum, Dung Tran, Trung Dang, Ismini Lourentzou, and Kazuhito Koishida. uamix-mae: Efficient tuning of pretrained audio transformers with unsupervised audio mixtures. In IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5435-5439. IEEE, 2024.
Quyen Tran, Tung Lam Tran, Khanh Doan, Toan Tran, Dinh Phung, Khoat Than, and Trung Le. Boosting multiple views for pretrained-basee continual learning. In International Conference on Learning Representations, 2025.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Informa- tion Processing Systems, 30, 2017.
Helin Wang, Yuexian Zou, Dading Chong, and Wenwu Wang. Environmental sound classification with parallel temporal-spectral attention. Proceedings of INTERSPEECH 2020.
Liyuan Wang, Jingyi Xie, Xingxing Zhang, Mingyi Huang, Hang Su, and Jun Zhu, Hierarchical de- composition of prompt-basee continual learning: Rethinking obscured sub-optimality. Advances in Neural Information Processing Systems, 36:69054-69015 2023.
Liyuan Wang. Xingxing Zhang, Hang Su, and Jun Zhu. A comprehensive survey of continual learning: Theory, method and application. IEEE transactions on pattern analysis and machine intelligence, 46(8):5362-558) 53833, 2024a.
Liyuan Wang, Jingyi Xie, Xingxing Zhang, Hang Su, and Jun Zhu. Hide-pet: continual learning via hierarchical decomposition of parameter-efficcent tuning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Shipeng Wang, Xiaorong Li, Jian Sun, and Zongben Xu. Training networks in null space of feature covariance for continual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 184-193, 2021.
Shipeng Wang, Xiaorong Li, Jian Sun, and Zongben Xu. Training networks in null space of feature covariance with sulf-superviiio for incremental learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024b.
14

---

Published as a conference paper at ICLR 2026
Yabin Wang, Zhiwu Huang, and Xiaopeng Hong. S-prompts learning with pre- tre-trained transformers: An occam's 's razor for domain incremental learning. Advances in Neural Information Processing Systems, 35:5682-5692 2022a.
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen- Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, et al. Dualprompt: Complementary prompting for rehearsal-free continual learning. In European Conference on Computer Vision, pp. 631-648. Springer, 2022h.
Zifeng Wang, Zizhao Zhang, Chen- Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vin- cent Perot, Jennifer Dy, and Tomas Pfister. Learning prompt for continual learning. In Pro- ceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 139-149, 2022c.
Pete Warden. Speech commands: dataset for limited-vocabulary speech recognition. arXiv preprint 1rXiv:1804.033 2018.
Julia Wilkins, Prem Seetharaman, Alison Wahl, and Bryan Pardo. Vocalset: A singing voice dataset. In ISMIR, pp. 0768 2018.
Yusong Wu, Christos Tsirigotis, Ke Chen, Cheng-Zhi Anna Huang, Aaron Courville, Oriol Nieto, Prem Seetharaman, and Justin Salamon. Flam: Frame-wise language-audis modeling. In Inter- national Conference on Machine Learning.
Yi Yuan, Xubo Liu, Haohe Liu, Mark D Plumbley, and Wenwu Wang. Flowsep: Language-queried sound separation with rectified flow matching. In /EEE International Conference on Acoustics, Speech and Signal Processing, pp. 1-5. IEEE, 2025.
Friedemann Zenke, Ben Poole, and Surya Ganguli. Continual learning through synaptic intelligence. In International Conference on Machine Learning, pp. 3987-3995 PMLR, 2017.
Gengwei Zhang, Liyuan Wang, Guoliang Kang, Ling Chen, and Yunchao Wei. Slea: Slow learner with classifier alignment for continual learning pre-trained model. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 19148-19158, 2023.
Gengwei Zhang, Liyuan Wang, Guoliang Kang, Ling Chen, and Yunchao Wei. Slea++: Un- leash the power of sequential fine-tuning for continual learning with pre-training. arXiv preprint arXiv:2408.0822 08295, 2024.
Linglan Zhao, Xuerui Zhang, Ke Yan, Shouhong Ding, and Weiran Huang. Safe: Slow and fast parameter-efficient tuning for continual learning with pre-trained models. Advances in Neural Information Processing Systems, 37:113772-11399 2024.
Dei Zhou, Hai-Long Sun, Jingyi Ning, Han-Jia Ye, and De-Chuan Zhan. Continual learning with pre-trrined models: A survey. International Joint Conference on Artificial Intelligence, 2024a.
Da- Wei Zhou, Hai-Long Sun, Hin-Jia Ye, and De-Chuan Zhan. Expandable subspace ensemble for pre-trained model-based class incremental learning. In Proceedings the IEEÉ/CVF Conference on Computer Vision and Pattern Recognition, pp. 23554 2024b.
Kanglei Zhou, Ruizhi Cai, Liyuan Wang, Hubert PH Shum, and Xiaohui Liang. A comprehensive survey of action quality assessment: Method and benchmark. arXiv preprint arXiv:2412.11149, 2024c.
Kanglei Zhou, Liyuan Wang. Xingxing Zhang, Hubert PH Shum, Frederick WB Li, Jianguo Li, and Xiaohui Liang. Magr: Manifold-aligned graph regularization for continual action quality assessment. In European Conference on Computer Vision, pp. 375-392, Springer, 2024d.
Kanglei Zhou, Zikai Hao, Liyuan Wang, and Xiaohui Liang. Adaptive alignment learning for continual perceptual quality assessment of 360-degree videos in virtual reality. IEEE Transactions Visualization and Computer Graphics, 31(5):2880-289) 2025a.
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Kanglei Zhou, Qingyi Pan, Xingxing Zhang, Hubert PH Shum, Frederick WB Li, Xiaohui Liang, and Liyuan Wang. Continual action quality assessment via adaptive manifold-alignes graph reg- ularization. arXiv preprint arXiv:2510.0684,, 2025b.
Huiping Zhuang, Zhenyu Weng, Hongxin Wei, Renchunzi Xie, Kar-Ann Toh, and Zhiping Lin. Acil: Analytic class-incremental learning with absolute memorization and privacy protection. Advances in Neural Information Processing Systems, 35:1 11614, 2022.
Xiaodan Zhuang, Xi Zhou, Mark A Hasegawa-Johnson, and Thomas Huang. Real-world acoustic event detection. Pattern Recognition Letters, 31412))153-1151, 2010.
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A STATEMENT
Acknowledgment. This work was supported by the NSFC Project No. 62406160 and Beijing Nat- ural Science Foundation L.24701..
Ethics statement. acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Reproducibility statement. We have included the source code with clear instructions, and will release them upon acceptance.
Large language models assistance. Large language models were used to polish the manuscript. The authors have thoroughly reviewed and edited all content and take full responsibility for the published work.
B PSEUDOCODE OF IMPROVED FIRST SESSION ADAPTATION
Algorithm 1: Improved First Session Adaptation
Require: Pretrained backbone fu. Ey, Enend, CKA threshold Players learning rates Tob Thead
1: Stage A (detect layer boundary):
2: 6Pte t Bg
1,... Eo do 4: 09 <- 0000 Thb oo ce (X)))
3: for epoch 11,
(PEFT on all layers)
5: end for
6: for == 1,
L do 7: S CJA( (xi)*, fo(x))))
8: end for
9: k* +- arg maxg Sh Player]
10: Stage B (head warm-up, Whead): ):
11: for epoch = 1, ..... Enend do
12: hj < h1 Thcod Vn (hr (fo(
13: end for
14: Stage C (backhone adaptation, 1bb)):
15: Freeze hee, 0o: 01 +- Pg + 0LoRA
16: for epoch 1, Eg do
17: 01 0i+0, - 7hb Co (h. (1)
{only layers k* +1(une in trainable}
end for
19: Stage D (analytic phase):
Freeze f1; Discard h1; Initialize statistics on <(-) fa();: Continue with Sec. 3.3
Algorithm Noutlines the improved first-session adaptation procedure, designed to establish effective yet stable representation transfer in FSA for audio CL. Stage A performs lightweight PEFT update across all layers to probe sensitivity, after which CKA is used to measure the similarity between fi and the pretrained backbone fo each layer. The boundary layer Inuee A + is identified as the point where representation similarity tend to drop below the threshold Player, indicating where proper adaptation should begin. Stage then train a restricted classifier head h; with a smaller learning rate Thead and training epoch Enead. Stage C adapts only the deeper layers beyond k with LoRA-style updates and a larger learning rate Tho. while freezing shallow layers and the head to preserve pretrain knowledge and enhance backbone adaption. Finally, Stage D freezes the adapted backbone, discards the temporary head, and initializes statistics for the analytic phase (see Sec. 3.3)
C RELATED WORKS
Continual learning (CL) enables models to learn continuously while mitigating catastrophic forget- ting of previously learned knowledge (Wang et al. 2024a). Most research in CL has focused on the vision domain, with representative regularization based (Kirkpatrick et al. 2017: Zenke et al.) (017).
rapea (Rolnick et al. 2019 Liu al. 2020b), and architecture- approaches (Douillard
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et al. 2022 Kanakis al. 2020). More recently, CL with pretrained models (PTMs) has attracted growing attention, as PTMs provide strong general-purpose initialization (Zhou et al. 2024a) and obviate the need for training from scratch. This paradigm typically leverages parameter--eftcient
fine-tuning (PEFT) (Rebufh et al. 2017a Li Liang 2021 Hu et al. 2021) strategies by intro- ducing lightweight modules top of frozen backbones (Wang al. 2022c by Tran et. 2025 Le
et al. 2024 Gao et al. |023 Zhao et al. 2024), or combines them with regularization principles (Liu & Chang 2025 Liang Li 2024 Zhou et 2024b). In the vision domain, PTMs encode rel- atively stable and semantically serl-struc representations (Janson et al.). leading to only mild representational shift across learning sessions (see Fig. [(b)). which makes PEFT-based CL highly effective. However, the effectiveness of this paradigm in the audio domain remains underexplored and calls for systematic investigation.
Audio recognition covers tasks such as speech recognition (Abdel-Hamid et al. 2014 Bai et al.
2024), acoustic event detection (Yuan et al., 2025 Li al. 2025b), and music understanding (Li
al. 2024b). Recent pretrained models (Gong al. 2022
Chen et al. 2024 Alex et al. 2025
Liu et al. [2025] Chang et al., 2025) have achieved strong performance across these domains, and regularization signals in audio generation tasks (Li
show potential for providing conditioning
jet al. 2025a 2222a; Jiang et al. (025). Yet these advances usually assume offline training with full data access, overlooking the evolving, non-stationary distributions of real-world settings (Bhanl ct al. 2024). Emerging studies on audio CL (Mulimani & Mesaros, 2025 Cappellazzo et al.] 2024
Singh et al. 2024 Mo al. 2023 Pian al.J 2023) often borrow from vision or use simplified settings, without tackling audio's unique challenges. Unlike images, audio depends on fine-grained spectral cues, making representations highly sensitive to distribution shifts and prone to catastrophic forgetting (see Fig. I(a). This calls for audio-specinf CL strategies compatible with pretrained backbones and aligned with the spectral nature of audio.
D ADDITIONAL EXPERIMENTAL DETAILS
All experiments are conducted on an NVIDIA A800 GPU. The input size is 512 128. Each audio clip is truncated to the first 5.12 seconds, with batch size of 24. We set Tob 0.05, /head 0.01 for all tasks, and the number of training epochs is selected via grid search. For each task, we report the average accuracy over all tasks, i... Acc 1 Σ Acc,, as the primary measure of CL performance. For hypermarameters, we set Enend = 1. Player 0.94 for improved First-Session Adaption, Psvd = 0.99. Nstop = 220 for Multi- Session Adaption, Np 20, Pp 0.3, = 0.25 for Boundary-Aware Perturbation and Drroj 8192 for our Analytic Classifier. We also provide detailed statistics of the datasets along with their corresponding training epochs Eg in Table 5
Table 5: Statisties of datasets used in our experiments.
Total Train Samples
Total Test Samples
Dataset
Epoch
Classes
Session
ESC-50 Piczak
2015)
10
50
10
1600
400
US8K (Salamon et al.
2014)
15
10
5
8000
2000
SC2 (Wrdeen 2018)
1
35
7
84651
21178
TIMIT-2 (Garofolo et al.
1993
30
630
315
5040
1260
TIMIT-3
GGroooooo et al.
1993
30
630
210
5040
1260
VocalSet (Wilkins al. 2018)
6
16
1216
304
E ADDITIONAL EXPERIMENTAL RESULTS
E. STOP SESSION FOR MSA
As we set Naop to estimate the truncation point, we obtain a specific cutoff for each task, as shown in Table 6 To further assess the effectiveness of this task- stopping mechanism,
conduct
ablations on neighboring sessions around the chosen T₃ = t" (i.e., t" 1, t* 2, t" 1, t" + 2). We observe that /" yields locally optimal results most datasets (US8K, SC2, TIMIT-3, VocalSet),
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Dataset
Chosen 8*
w/o FSA
FSA
2
t'
t" +1
t' +2
ESC-50
2
92.50
92.25
N/A
92.25
91.75
91.75
92.00
US8K
96.49
97.08
N/A
N/A
97.08
95.15
81.44
SC2
81.22
90.53
N/A
N/A
90.53
89.46
81.14
TIMIT-2
14
75.87
85.63
88.49
88.73
89.21
90.40
90.23
TIMIT-3
10
75.87
89.92
92.78
93.73
93.73
93.41
93.41
VocalSet
2
61.51
62.82
N/A
61.51
66.78
64.80
62.83
Table 6: Results of different datasets under FSA and MSA (w/o Leg w/o Improved FSA) with different adaptation sessions.
substantially outperforming both w/o FSA and vanilla FSA, while maintaining more stable perfor- mance across longer session sequences in realistic CL settings (TIMIT-2/3). Although t' is not always the global optimum on ESC-50 and TIMIT-2, applying MSA in a naive manner does not lead to significant degradation compared to the optimal, thus validating the necessity of stop- ping strategy. Additionally, is worth noting that we generally observe a decline in performance as the number of sessions for adaptation grows, which becomes more pronounced when the ses- sions are relatively small. We attribute this to the backbone having already sufficiently adapted to the downstream domain, such that the marginal gains from additional adaptation are outweighed by cumulative representation drifts that induce forgetting on earlier tasks, underscoring the importance of stopping criterion.
E.2 LEARNING VS. FORGETTING
To clarify how stability and plasticity interact during continual updates, evaluate multi-session adaptation (MSA) with and without gradient projection (GP) on the VocalSet benchmark. We report five key indicators: (1) Forgetting over sessions S T3, (2) Plasticity measured by the average max- imum accuracy. (3) Average Accuracy before Ts, (4) Average Accuracy after T3, and (5) Backward Transfer (BWT). Results are shown Table
Table 7: Comparison of MSA with and without gradient projection on VocalSet.
Setting
Forgetting
Plasticity
Ave Acc S T3)
Ave Ace (1 > Ta)
BWT
FSA
27.63
92.10
64.48
60.52
-10.90
MSA w/o GP
57.90
92.10
34.21
66.67
-9.02
MSA (w/ GP)
23.69
88.16
64.47
70.62
-7.14
The results indicate that gradient projection greatly reduces forgetting while maintaining plasticity. Although both MSA variants reach comparable maximum accuracy, removing GP causes significant representation drift, resulting in high forgetting and degraded BWT. In contrast, GP-stabilized MSA maintains stable carly-session performance and achieves higher accuracy in later sessions, demon- strating that projection effectively constrains destructive feature shifts while preserving adaptabil- ity.
E.3 ADDITIONAL BENCHMARKS ON NON-HUMAN AUDIO AND EVALUATION
To more comprehensively assess CL performance under diverse audio conditions, two additional benchmarks are introduced: (1) fine-grained non-human audio dataset and (2) cross-domain sound-speech dataset. These benchmarks target scenarios characterized by substantial intra-class variation and distributional mismatch, which central the challenges addressed in this work.
GTZAN: Fine-Grained Non-Human Audio. Many instrument-related datasets (e.g., GTMUSIC Sturm 2 exhibit coarse semantic granularity. Preliminary analyses show that non-music pretrained backbone, such as EAT, combined with a single-session adaptation step, can reach up to 99.8% accuracy, indicating minimal distribution complexity and limited suitability for CL. evalua-
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tion. In contrast, the GTZAN dataset (Sturm 2013) contains 10 musical genres with richer intra- class diversity, offering more realistic fine- tne-grained distribution shift that better reflects the evolving semantic structure in non-human audio CL.
ESC-Speech: Cross-Domain Sound-Speed Benchmark. To evaluate robustness under hetero- geneous domain compositions, synthetic cross-domain dataset named ESC-Speech is constructed by combining ESC-50 (environmental sounds) and SpeechCommande mixture introduces substantial domain mismatch between non-verbal acoustic events and human speech, forming a challenging scenario for pretrained audio models.
V2 (spoken words). This
Experimental Protocol. Both benchmarks are evaluated under CL settings tailored their charac- teristics: GTZAN uses a 5-session split with 20 samples per class to capture its fine- ene-grained musical variability, whereas ESC-Speech adopts a 10-session split with 50 samples per class to reflect its cross-domain (sound-speec)) shifts. The results are presented in Table B
Table 8: CL results on more diverse benchmarks.
Dataset
Attribute
L2P
HiDe-Prompt
RanPAC
PACE
GTZAN
Non-human Voice (Instrument)
10.00
51.00
73.00
78.00
ESC-Speech
Cross-domain (ESC SC2)
21.50
52.58
57.00
72.17
Observations. Across both benchmarks, PACE achieves the highest performance, demonstrating strong resilience to both intra-class variation (GTZAN) and cross-domain distribution shift (ESC- Speech). The musical-domain evaluation further reveals substantial representational shift in pre- trained audio models, which intensifies the difficulty of continual adaptation. Methods originally designed for vision-based CL (e.g.. L2P) show limited effectiveness in such settings. In contrast, the multi-session adaptation strategy in PACE enables progressive alignment of the evolving semantic space, producing consistent improvements under both intra- and inter-domain distribution changes.
E.4 COMPUTATIONAL COST AND TRAINING OVERHEAD
The computational overhead of PACE arises primarily from the early-stage backbone updates, which required to obtain semantically aligned representation. After the first few adaptation sessions, the method transitions to an analytic classifier together with lightweight cosspac-orttooonal up- dates, resulting in highly efficient training for all subsequent sessions.
On coarse- coarse-grained benchmarks, improved FSA provides sufficient alignment, and no additional backbone updates are needed. In these settings, PACE achieves near joint-training performance with essentially the same computational cost as standard fine-tuning.
On fine-graines benchmarks, PACE introduces moderate overhead relative RanPAC, but remains substantially more efficient than prompt-based approaches such as HiDe-Prompt, which repeatedly optimize prompts or low-rank adapters across all sessions. The results are reported in Table
Table 9: Training time and training time ratios on fine-grained datasets. Training time is calculated on a single GPU and ratios are computed relative RanPAC.
Dataset
Training Time (sec/sample)
PACE/ RanPAC HiDe-Prompt RanPAC
VocalSet
0.22
1.22
5.44
TIMIT-3
0.12
2.96
146.98
TIMIT-2
0.31
3.13
124.19
These results show that PACE delivers significant gains in accuracy while introducing only modest computational overhead. In contrast, HiDe-Prome incurs 5x-40x higher cost despite yielding in- ferior performance. Although our method requires more training time than RanPAC on fine- fine-graing grained datasets, it still reaches average speed of about 0.2 sec/sample single GPU, which is highly efficient in practice. Overall, PACE provides a favorable sarcciincy-effcctiveee trade-off, particu- larly in fine-grained continual learning scenarios.
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E.5 ADDITIONAL PRETRAINED BACKBONES
To evaluate the generality of PACE beyond a single pretrained checkpoint, we further benchmarked the framework using SSLAM (Alex et al. 2025), recent source-aware pretrained audio model trained on polyphonic mixtures and sharing the same ViT backbone as EAT (Chen et al.) (024). Experiments were conducted two coarse-grained and two fine-grained datasets, and PACE was compared against L2P, HiDe-Promp and RanPAC. Results are reported in Table 10
Table 10: CL. performance with the SSLAM backbone on coarse- and fine-grained datasets.
Dataset
Granularity
Backbone
L2P
HiDe-Prompt
RanPAC
PACE
ESC-50
Coarse
SSLAM
40.50
82.00
95.75
96.25
SC2
Coarse
SSLAM
15,24
37.85
88.59
90.39
VocalSet
Fine
SSLAM
17.76
47.22
63,83
68.42
TIMIT-2
Fine
SSLAM
0.32
46.24
90.08
93.81
The results demonstrate that PACE consistently outperforms all baselines across both pretrained backbones and granularity levels. This indicates that the challenges identified in pretrained audio models, such as representation saturation and semantic misalignment, are not specific to EAT, but also arise in more recent models like SSLAM. Moreover, the strong performance across all settings highlights the robustness and general applicability of PACE as a CL framework for audio.
E.6 HYPERPARAMETER SELECTION AND SENSITIVITY ANALYSIS
This work introduces several continuous variables in the design of PACE. To make these selection procedures explicit, we provide a comprehensive sensitivity analysis.
SVD threshold Psvd Following prior PEFT-based CL studies (Liu & Chang 2025 2021]. we directly adopt the same SVD energy threshold. Across all datasets, varying ped within the range [0.90, 0.99) yields nearly identical results, confirming that this hyperparameter mainly affects numerical rank selection and has minimal influence on the learning dynamics.
Wang et al.
Stopping threshold Nsto and freezing threshold Player. These two hyperparameters govern MSA and were jointly tuned on the fine-grained VocalSet and TIMIT-3 datasets. Using only FSA, we searched values in {0.90, 0.92, 0.94, 0.96, 0.98, 1.00] and selected the value that best balanced adap- tation and stability. With Player fixed, we tuned Nsoop using MSA without regularization. As indicated in Figs. 7(a) and [bb] model performance plateaus between 200 and 250 gradient steps. Although Naup 250 slightly exceeds the optimal T3 on VocalSet, it still improves over FSA by approxi- mately 2%, suggesting that the method is resilient to small deviations.
Validation of Player- As shown in Fig. 6 the selected value Player = 0.94 successfully avoids prema- ture freezing of adaptable deeper layers while preserving stable low-level feature extraction, espe- cially for fine-grained datasets such as TIMIT-3.
Sensitivity analysis. We additionally examine the sensitivity of all remaining continuous hyper- parameters. To improve readability, present only the average performance and highlight the selected configurations. The results are reported in Tables to 14
Overall, PACE exhibits strong robustness across a wide range of hyperparameter configurations. The selected values serve as stable defaults and require minimal tuning, underscoring the practicality of the framework for CL across diverse audio scenarios.
E.7 RATIONALE FOR TIME- TIME-FREQEENCY MASKING IN BOUNDARY-AWARE REGULARIZATION
The choice of time-freee frequenyyy masking is motivated by the spectral structure of audio signals and the requirements of boundary- aware regularization. SpecAugment-styl/ masking (Park et al., 2019) is adopted as label- preserring perturbation applied directly in the feee-ffeeuency domain. By selec- tively removing narrow temporal or spectral bands, this operation introduces localized distortions to the spectrogram while preserving the global semantic identity of the signal. Such perturbations
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Table 11: Sensitivity of the MSA stopping threshold Nstop across three fine-grained datasets.
Method
TIMIT-2
TIMIT-3
VocalSet
Avg.
FSA
85.63
89.92
62.82
79.46
MSA w/ 150
88.25
92.78
62.82
81.28
MSA w/ 175
89.05
93.24
66.78
83.02
MSA w/ 200
88.49
93.33
66.78
82.87
MSA w/ 210
89.21
93.33
66.78
83.11
MSA w/ 220
89.21
93.73
66.78
83.24
MSA w/ 230
90.39
93.73
66.78
83.63
MSA w/ 240
90.23
93.73
64.80
82.92
MSA w/ 250
90.23
93.41
64.80
82.81
Table 12: Sensitivity of the layer-freezing threshold Player across datasets..
Player
TIMIT-3
SC2
VocalSet
ESC-50
US8K
Avg.
0.90
83.41
90.96
62.82
94.50
97.38
85.01
0.91
83.41
90.96
62.82
94.50
97.38
85.01
0.92
85.63
90.96
62.82
94.50
97.38
86.26
0.93
85.63
90.96
62.82
94.50
97.38
86.26
0.94
85.63
90.96
62.82
94.50
97.38
86.26
0.95
85.63
90.54
62.82
94.50
97.38
86.17
0.96
85.63
90.54
62.82
92.25
97.38
85.72
0.97
85.63
90.54
60.23
92.25
97.38
85.21
0.98
85.63
90.54
60.23
92.25
97.08
85.15
0.99
85.63
90.53
60.23
92.25
97.08
85.14
1.00
85.63
90.53
60.23
92.25
97.08
85.14
w/o
85.63
90.53
60.23
92.25
97.08
85.14
Table 13: Sensitivity of the boundary ratio Pratio on the VocalSet benchmark.
Ratio
0.20
0.25
0.30
0.35
0.40
0.45
0.50
Acc
65.79
67.43
67.43
69.08
68.75
64.47
64.47
Table 14: Sensitivity of the augmentation strength on the VocalSet benchmark.
Strength
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
Acc
65.79
66.12
64.80
63.49
69.08
68.10
65.79
64.47
61.51
enable controlled exploration of the neighborhood around each instance, supporting the construction of boundary-pront pronne samples without violating class consistency.
To assess whether the proposed regularization term Lreg affects robustness, we further evaluated models on VocalSet with and without time-free frequeer masking applied at time. The results are summarized in Table [3] Models trained with Lrgg exhibit substantially smaller performance degra- dation under masked test inputs (1.83% vs. 6.26%), indicating that boundary-awan regularization enhances robustness rather than compromising it.
E.8
CASE STUDY FOR COARSE-GRAINED AND FINE-GRAINED AUDIO CL
To further clarify the differences between fine- and coarse -rerseegriieddd datasets, we provide case studies in Fig. 10 Using PEFT-FT both types of datasets, we track the prediction dynamics of three cat- egories from the first session across the following three sessions, while also reporting the maximum inter-class distance in the pretrained model (measured in the first session) and the average feature
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Table 15: Evaluation of robustness time-frequency masking (TFM) on the VocalSet dataset.
Evaluation
Test w/o TFM
Test w/ TFM
Train w/o TFM
60.52 66.78
Train w/ TFM
69.08
67.25
shift between sessions. It is evident from Fig. 10(a) that coarse-grained datasets exhibit relatively large spectral patterns (e.g., the harmonic structure in Cat or the periodic energy bursts in Door wood knock). Such pronounced differences yield large inter-class separations, making easier to capture them within the first session, which is reflected in the near joint-training performance of our improved FSA in Table 2 Consequently, when applying naive PEFT-FT, the model can adapt with- out substantial semantic changes in the representations, which corrssoonds to smaller cross sessson feature shifts compared to the inter-class distances. Notably, categories with highly distinctive struc- resistant forgetting under such naive adaptation.
tures (e.g., Door wood knock)
When it comes to fine-grained scenarios, as shown in Fig. TO(b) we observe that different categories share highly similar spectral patterns. In this setting, the model required discriminate among 630 speakers, where time-frey details alone are insufficient to yield discriminative informa- tion. Instead, the model must adjust its representations within each session, using only limited data, to shape more discriminative deep features. This results in relatively small inter-class separations (nearly half those coarse-grained datasets) but necessitates larger representational changes. The ultimate impact is catastrophic: although the model initially maintains high plasticity and achieves good accuracy within the current session, naive PEFT-FT lacks the memory capacity to retain prior knowledge, causing severe misclassification in subsequent sessions. This highlights the necessity of our PACE method, which effectively leverages data across multiple sessions to continually adapt the network while simultaneously constraining prior representations remain stable.
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(a) Coarse-grained Dataset (ESC-50)
MMaximum class distance: 16.01, Shifting across sessions: 4.70]
Cow
M X
x
M X X
Cat
X
Door wood knock
X
(b) Fine-grained Dataset (TIMIT)
[Maximum class distance: 9.88, Shifting across sessions: 8.06]
Speaker: FAJWO
X
M X
V x
Speaker: FALKO
X X
XXXX
V XXxX
Speaker: FBJLO
X
XXXX
XXXX
Figure 10: Case studies on coarse-grained dataset (ESC-50) and fine-grained dataset (TIMIT-3): linear amplitude spectrograms of three classes from the first session, along with their performance trajectories under naive PEFT-FT.
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Reviews:
[{"review_id": "L9y7uESyiY", "reviewer": "Reviewer_gWXH", "summary": "This work studies continual learning on audio pretrained encoders. The authors argue that continual learning techniques designed for vision do not transfer well to audio and propose **PACE**, a continual learning framework tailored for audio pretrained models. \n\n**Contributions**\n\n1. **Improved first session adaptation.** Freeze early layers and adapt only deeper and more task sensitive layers before moving to analytic inference.  \n2. **Adaptive subspace orthogonal PEFT for later sessions.** Used to reduce forgetting and follows LoRA subtraction and null space continual learning ideas, so this part is adapted rather than fully novel.  \n3. **Spectrogram boundary aware perturbations.** Encourage intra class compactness and inter class margin enlargement.  \n4. **Regularized analytic classifier without rehearsal.** Uses an analytic classifier that avoids storing historical data.", "questions": "1. You showed that vision continual learning methods do not transfer well to audio. Do you expect PACE itself to transfer to vision or are parts of it truly audio specific?  \n2. It was a bit hard to situate PACE among existing audio continual learning works. Is this mainly because there are very few strong audio continual learning baselines?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Well written and easy to follow.  \n- Comparisons are fair and support the claims.  \n- Addresses area of audio continual learning.", "weaknesses": "- **Learning vs forgetting.** Different contributions such as  projection based update that protects past tasks can also limit learning on the current task. Please add an analysis that separates how much performance comes from stability and how much is lost in plasticity.  \n- **Fine grained tasks are mostly human voices.** Current fine grained results are on speech or voice like data for example TIMIT and VocalSet. A non voice fine grained audio task such as environmental or musical instrument classification would make the claim stronger.  \n- **Compute and overhead.** PACE introduces extra stages and training which add compute and time requirements compared to RanPAC.\n- **Single dataset sessions.** All sessions are constructed from the same dataset. Please add a cross dataset or cross domain setting to show robustness.\n- **Only one pretrained Encoder (EAT) is used.** The experiments use only one audio pretrained encoder (EAT). Adding music oriented models such as MERT and speech specific encoders would show that PACE is not tied to a single backbone.", "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": "VTUbEBpNgX", "reviewer": "Reviewer_ViWM", "summary": "This paper proposes PACE, an audio continual learning system that combines multiple techniques, including a low learning rate for head learning, Later Layer LoRA for backbone finetuning, Multi-Session Adaptation for continual learning, and Boundary-Aware Perturbation for enlarging the inter-class margin. It shows good results on various benchmarks.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The experiment results are impressive.\n2. Analysis of the challenges of audio continual learning is clear and understandable.\n3. The illustrations in the paper are good.", "weaknesses": "1. The technical points are scattered:\n\nThe techniques presented in this paper are rather fragmented, jumping from one idea to another without a unified narrative. This makes it difficult for readers to clearly understand the motivation behind each technique.\n\n2. Limited novelty of the proposed methods:\n\nThe idea of using a lower learning rate for training the head seems more like an empirical tuning strategy rather than a fundamentally new contribution, and the concept of Multi-Session Adaptation is not novel.\n\n3. Insufficient experiments:\n\nThe ablation study is not comprehensive enough. For fine-grained datasets, the paper lacks ablation results for each proposed component, and the accuracy V.S. session curves (similar to Figure 2(c)) are also missing.", "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": "cQMjYhVB14", "reviewer": "Reviewer_FS1X", "summary": "This paper studies continual learning for audio tasks with pretrained models. It argues that directly using parameter-efficient fine-tuning methods from vision does not work well for audio, since audio backbones focus on low-level spectral cues while continual learning needs high-level semantic adaptation. The authors provide a benchmark and analysis showing two failure modes: representation saturation on coarse-grained tasks and representation shift on fine-grained tasks. They propose PACE, which improves first-session adaptation, applies LoRA only to later layers, uses a subspace-orthogonal strategy for multi-session updates, and adds a boundary-aware regularizer. Experiments across six audio continual learning benchmarks show consistent gains over prior work and a reduced gap to joint training.", "questions": "1. The stopping threshold for Multi-Session Adaptation (MSA) is determined when the cumulative number of seen samples exceeds $N_{stop} = 220$, which then dictates the final adaptation session $T_3$ (Table 7). $N_{stop}$ appears to be a fixed hyperparameter across all six datasets. Could the authors elaborate on the selection process for the specific value of $220$?  Was this value chosen based on a grid search across datasets?\n\n\n2. In the improved First-Session Adaptation (Section 3.2), the paper proposes an asymmetric training scheme where the head's learning rate ($\\eta_{head}$) is set significantly lower than the backbone's ($\\eta_{bb}$). The authors note this is opposite to methods like LAE and SLCA, which suppress backbone updates to mitigate forgetting. Could you specifically elaborate on why this \"opposite\" approach is beneficial for audio PTMs? I assume it has to do with \"compelling the backbone to absorb most gradient signals\", which aligns with the finding that adaptation should be restricted to the deeper, semantic layers ($\\ell \\geq L_{tune}$). Does this suggest that the task-specific semantic information (required by downstream audio tasks) is encoded so poorly in EAT's deeper layers that significant, forced fine-tuning via a high $\\eta_{bb}$ is mandatory for effective transfer, unlike in pretrained vision models where freezing often is enough?\n\n\n3. The boundary-aware regularization component (Section 3.3) uses time-frequency masking ($Q(x_{i,t}, r_T, r_F)$) to generate perturbed samples near decision boundaries (approximating $B_t$). The loss term $\\mathcal{L}_{reg}$ is shown to further enhance performance gains provided by MSA (Figs. 6(c) and 6(d)). Given that many audio augmentations exist (e.g., adding noise, pitch shifting), is the choice of time-frequency masking critical to effectively generating the required \"boundary-prone samples\" $B_t$? A discussion or small ablation comparing time-frequency masking against other standard augmentation techniques for defining $B_t$ would confirm that the choice of perturbation method is specifically aligned with the spectral nature of audio features.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. AFAIK, this paper is the first to provide a deep dive into the unique challenges of audio CL with PTMs. The empirical analysis that distinguishes the difficulties in coarse-grained vs. fine-grained audio scenarios (Findings 1, 2, and 3 in Section 2) is a major strength and provides a clear motivation for the proposed method.\n2. Their PACE framework is technically sound and its components directly address the problems identified.\n3. Their empirical evaluation is thorough. The authors benchmark PACE on six diverse audio datasets, with different granularities and domains. The comparison against a wide range of state-of-the-art CL methods (Table 2) is thorough and convincingly demonstrates the superiority of the method.\n4. The paper well written. The figures are effective at conveying their ideas. The structured presentation of findings makes the paper easy to follow and understand.", "weaknesses": "We appreciate the detailed analysis and strong empirical results achieved in this submission. To maximize the impact and clarity of the work, we suggest the authors address the following points:\n\n1. The central claim of the paper is about addressing a \"fundamental property of audio backbones\" in continual learning. Despite this broad claim, all experiments are exclusively conducted using the EAT backbone, which is a spectrogram-based masked prediction model. Demonstrating that the observed challenges (representation shift) and the effectiveness of the PACE solutions hold true for a distinct audio PTM architecture (e.g., BEATs, AST) would significantly strengthen the paper's claims of generality.\n\n2. No explanation on how $\\rho_{layer}=0.94$, $\\rho_{svd}=0.99$, and $N_{stop}=220$ were chosen. There were multiple continuous variables that were defined without a proper analysis of their impact to the method's performance.\n\n3. The Multi-Session Adaptation (MSA) stage is essential for fine-grained performance. The authors adopt complex strategies, including LoRA Subtraction and subsequent SVD on the uncentered covariance matrix $X^{ucov}_t$, specifically to ensure \"computational efficiency\" against methods with \"extensive storage overheads\". However, to fully validate this optimization, a quantitative analysis detailing the increase in memory footprint or training time (e.g., time per session) of the full PACE pipeline compared to simpler, stable FSA baselines (such as RanPAC or ACL) is required.", "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": "4u1cVjb6Ah", "reviewer": "Reviewer_4KNg", "summary": "This paper investigates the challenges of continual learning with pretrained audio models. Through a systematic benchmark, the authors identify that a mismatch between pretraining objectives and downstream tasks leads to representation saturation in coarse-grained scenarios and severe representation shifts in fine-grained ones. To address this, they propose PACE, a framework that improves upon First-Session Adaptation (FSA) with three main contributions: (1) a restricted adaptation strategy using later-layer LoRA to prevent saturation , (2) an adaptive multi-session, subspace-orthogonal PEFT to enable stable learning across tasks , and (3) a boundary-aware perturbation regularizer to enhance class separability. Experiments across six diverse audio benchmarks demonstrate that PACE substantially outperforms existing state-of-the-art methods.", "questions": "Could the authors clarify the design choices and associated trade-offs of the boundary-aware perturbation mechanism? The method's reliance on the previous model to identify boundary samples seems to imply a memory overhead from storing past models, which could be a significant drawback in long-sequence CL scenarios. Furthermore, time-frequency masking is typically employed as data augmentation to encourage invariance by pulling perturbed samples toward their class centroid. By contrast, the proposed method uses these perturbations to define a boundary to push features away from. Could the authors elaborate on the rationale for this specific design and discuss whether it potentially compromises the model's learned robustness to these common audio transformations?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Problem Formulation and Analysis: The paper excels in its methodical diagnosis of the problem. The identification of distinct failure modes for coarse-grained (representation saturation) and fine-grained (representation shift) tasks is a key insight that strongly motivates the design of the PACE framework.\n- Novelty of the Proposed Method: PACE is a novel combination of several well-reasoned ideas. The adaptive multi-session subspace-orthogonal PEFT is a clever solution to balance plasticity and stability, and the later-layer LoRA adaptation is well-justified by both empirical results and intuition about hierarchical representations in neural networks.", "weaknesses": "- Clarity of Experimental Reporting: The paper's primary weakness lies in the fragmented and unclear presentation of its ablation studies, which makes it difficult to systematically assess the contribution of each component of the proposed PACE method. The validation results are scattered between the main text and the appendix, and even within the main paper, the connection between experiments and claims is not always explicit. For example, the caption for Table 3 is too general, forcing the reader to cross-reference the methodology to understand that it validates the \"restricted head learning\" strategy. A consolidated \"Ablation Studies\" section that clearly and methodically evaluates each of PACE's innovations would significantly strengthen the paper's clarity and persuasive impact.\n- Hyperparameter Sensitivity: The proposed method introduces several new hyperparameters (e.g., the CKA threshold, the SVD threshold, the MSA stopping criterion, boundary misclassification threshold, ...). While the values are provided, the paper lacks a sensitivity analysis. It would be beneficial to understand how robust the method's performance is to variations in these hyperparameters.", "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)