Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
Authors: Hyunsik Yoo
Ting-Wei Li
SeongKu Kang
Zhining Liu
Charlie Xu
Qilin Qi
Hanghang Tong
Venue: ICLR 2026 Poster (2026)
Keywords: LLM-based generative recommender; continual learning; low-rank adapters
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: While large language models (LLMs) achieve strong performance in recommendation, they face challenges in continual learning as users, items, and user preferences evolve over time. Existing LoRA-based continual methods primarily focus on preserving performance on previous tasks, but this overlooks the unique nature of recommendation: the goal is not to predict past preferences, and outdated preferences can even harm performance when current interests shift significantly. To address this, we propose PESO (Proximally rEgularized Single evolving lOra, a continual adaptation method for LoRA in recommendation. PESO introduces a proximal regularizer that anchors the current adapter to its most recent frozen state, enabling the model to flexibly balance adaptation and preservation, and to better capture recent user behaviors. Theoretically, we show that this proximal design provides data-aware, direction-wise guidance in the LoRA subspace. Empirically, PESO consistently outperforms existing LoRA-based continual learning methods.

Published as a conference paper ICLR 2026
CONTINUAL LOW-RANK ADAPTERS FOR LLM-BASED GENERATIVE RECOMMENDER SYSTEMS
Hyunsik Yoo', Ting-Wei Li", SeongKu Kang Zhining Liu', Charlie Qilin Qi Hanghang Tong"
University of Illinois Urbana- Champaign 'Korea University AAmazon (hy40, twli, liu326, htong] welllinoi.eedn (seongkukang) Okorea ac. ac (caizhx, gilinqi @ a zomazon.com com
ABSTRACT
While large language models (LLMs) achieve strong performance in recommendation, they face challenges in continual learning as users, items, and user preferences evolve over time. Existing LoRA-based continual methods primarily focus on preserving perfor- mance previous tasks, but this overlooks the unique nature of recommendation: the goal is not to predict past preferences, and outdated preferences can even harm performance when current interests shift significantly. To address this, propose PESO (Proximally rEgularized Single evolving IOra), a continual adaptation method for LoRA in recommen- dation. PESO introduces proximal regularizer that anchors the current adapter its most recent frozen state, enabling the model to flexibly balance adaptation and preserva- tion, and to better capture recent behaviors. Theoretically, design provides data-aware. firection-wise guidance in the LoRA subspace. Empirically, PESO consistently outperforms existing LoRA-based continual learning methods. Our code and documentation are available at
show that this proximal
1 INTRODUCTION
Large language models (LLMs) are increasingly used for recommendation by treating the task as sequence generation: given a user's 's interaction history, the model autoregressively generates the next item tokens (Bao et al., 2025a; Cao et al., 2024; Tan et al., 2024; Wang et al., 2024; Bao et al., 2023; Kweon et al., 2025; Lin et al., 2025; 2026; Wei et al., 2025a). In practice, LLM is fine-tuned user histories paired with their next interactions, aligning it with the recommendation objective. However, real-world interaction data are continuously collected and evolve over time: new users and items appear, and user preferences drift (Li et al., 2025a; Qiu et al., 2024). Periodic retraining from scratch on both historical and new data is possible but highly inefficient, making continual learning (i.c., updating the model effectively with new data) a natural and appealing solution.
It well known that a continual model must balance stability (retaining past knowledge) and plasticity (adapting to new knowledge) (Zhu et al., 2021; Arani et al., 2022; Ye et al., 2022; Zhang et al., 2024a; Yuan et al., 2021; Do & Lauw, 2023; Mi et al., 2020; Yoo et al., 2025b). However, continual recommender systems present unique interpretations of these concepts, and bear subtle but critical difference from other domains such as computer vision (Bao al., 2025b; Zeng et al., 2026a). In most other domains, continual tasks typically disjoint and not time-ordered (e.g., cats dogs trucks sedans), and the primary objective is to preserve performance on previous tasks (stability) while adapting to new ones (plasticity). In contrast, the ultimate goal of continual recommendation is accurately capture evolving user preferences in order to predict which items a user will prefer in the near future. That is, recommendation not concerned with predicting past user preferences; in fact, outdated preferences can even hinder performance if current

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Published as a conference paper at ICLR 2026
user interests have shifted significantly (e.g., a user starts preferring romance over action). Thus, stability in recommendation refers to preserving long-term user preferences (e.g.. enduring interests in certain genres or brands) that remain predictive, even if they are not strongly reflected in recent data. Plasticity, on the other hand, is required to overwrite outdated preferences and to capture emerging trends. This distinct setting in turn requires careful model design.
A common recipe for fine-tuning L.Mss in recommendation is Low-Rank Adaptation (LoRA) (Hu et al., 2022; Liu et al., 2025a; Zeng et al., 2025; Qiu et al., 2026), due to simplicity and modularity across com- ponents (c.g., attention layers). LoRA freezes pretrained weights and injects lightweight trainable low-rank matrices. Its efficiency makes LoRA a natural candidate for continual learning, motivating our focus on continual LoRA for LLM-based recommenders. A simple intuitive approach is to maintain a single evolving LoRA: sequentially fine-tuning one adapter, initializing it from the previous stage and optimizing on new data. This provides strong plasticity while parameter inheritance provides partial preservation of past knowl- edge. However, it inevitably overwrites useful past knowledge during fine-tuning, leading to forgetting.
To mitigate forgetting, several works in vision have proposed the family of cumulative LoRA (Wu al,, 2025; Liang & Li, 2024; Lu et al., 2024; Wang et al., 2023a; Liu et al., 2024a), which typically use the sum of the new trainable adapter and all frozen past adapters. This design explicitly enhances stability by reusing prior adapters and expanding LoRA's effective capacity, and it works well when tasks are largely indepen- dent (i.e., with minimal interference), allowing each adapter to encode task-specific knowledge. Intuitively, this might seem beneficial for recommendation, where preserving useful past preferences matters. However, our analysis shows that cumulative LoRA often underperforms the simpler single evolving LoRA. Unlike vi- sion tasks, recommendation involves reappearing users with continuously evolving preferences. The model must therefore capture useful interference across stages, but frozen adapters entangle outdated and relevant preferences, making them hard to disentangle. In addition, adapters accumulate over time, cumulative LoRA incurs growing storage costs and struggles to reflect their relative importance during aggregation.
To address these limitations, adopt two principles: (1) avoid multiple adapters, which implicitly assume task independence, and (2) preserve past knowledge in way that supports understanding of current user be- havior. Guided by this, we propose PESO (Proximally rEgularized Single evolving IOra). which maintains a single evolving LoRA adapter while regularizing it toward its past state with lightweight proximal term. Unlike cumulative LoRA, PESO balances stability and plasticity through the natural competition between the data-fitting loss and the proximal term, allowing the model to decide what to adapt or retain. Theo- retically, we show that this design yields data-aware, direction-wis. guidance in the LoRA subspace. We further instantiate it with a per-module nal module structure rather than treating all parameters equally (i.e., a more nuanced stability mechanism). Empirically, PESO consistently outperforms both cumulative LoRA and the single evolving adapter across multiple weal-world datasets, achieving a more effective stability-plasticin balance for recommendation.
Mullmax-KKllback -Libler (KL) proximal, which preserves inter-
In summary, our main contributions are threefold. (1) Analysis: we identify the distinctive stabil- ity-plasticity challenge in continual recommendation and show empirically that cumulative LoRA, while effective in simulated user-disjoint settings, underperforms in the natural case where user preferences evolve across time stages; (2) Method and Theory: we propose PESO, a proximally regularized LoRA that an- chors each update the previous state, with theory showing direction-wise data-aware guidance and a mor-mo softmax KL instantiation; (3) Experiments: we demonstrate through extensive experiments on real-world datasets that PESO consistently outperforms both single evolving and cumulative LoRA.
2 PRELIMINARY
Notations. We consider an LLM-based recommender that, given user's interaction history, autoregres- sively predicts the next item token. At time stage t E {1...., T), let U4 be the set of active users, I, the item set, and let &,- ((.,,1...., Fu, N.),, denote the collection of user interaction sequences, where
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Ju,n E It. For notational simplicity, we present stage-t data as one prefix-target pair per user:
Nu,NN. Dr {(xu, Vu) Ur No 2 2), X. Ju..i....,,uNNN.
Yu Zu.N.
(1)
In practice, training often uses multiple sliding-window next-item pairs induced from each sequence (see Appendix C.I). Each item is represented by semantic ID obtained by a codebook-based tokenizer (e.g., RQ- VAE, Rajput et al., 2023) trained on item semantic features (e.g.. title/description), yielding fixed number of token IDs for each item. Semantic ID captures hierarchical semantics of items and works well in practice.'
Stability and Plasticity in Continual Recommendation. We assume an initial model is pretrained offline on base data Di, and then fine-tuned sequentially on chronologically arriving blocks D2...., Dr The goal of continual recommendation is to minimize predictive risk on future interactions by balancing stability (re- taining persistent long-term preferences) and plasticity (adapting to new or shifting preferences from recent data), thereby capturing evolving user interests (see Appendix A for formal conceptual model). Concretely, for D., the LLM is fine-tuned with the standard autoregressive cross-cntropy for next-item prediction. Let y (V1,...,, y/M) denote the semantic-ID token sequence of the target item. The stage-t training loss is
L (x,y)),, [ poly =)]]
( x))
II po (ym x, p.y.m.).
(2)
Low-Rank Adaptation (LoRA), LoRA freezes the pretrained LLM weight Wo E Rduxdd and adds a trainable low-rank update:
AW BA, A Rexds B E Rduxx < min(dim doa);
so that for an input E ded the layer computes (W++AW). x. Only A and are updated during fine-tuning, while Wo remains fixed. This yields substantial parameter savings and modular, laser-wise adaptation (e.g.. on attention projections). In this work, our analysis and method operate entirely within this LoRA subspace and therefore inherit its efficiency. We now formally define our problem.
Problem 1. (Continual adaptation of generative recommender) Given: (1) a pretrained LLM-based rec- ommendation model (fine-tuned with LoRA on D1), ). (2) sequence of chronological data blocks D2...., DT; Goal: learn updates that, at each stage I, adapt the model to D, while retaining useful knowledge from ear- lier stages, achieving high quality next-item recommendation via a balanced stability-plastiity:
3 ANALYSIS OF LORA VARIANTS FOR CONTINUAL RECOMMENDATION
We introduce two primary baselines for our problem: single evolving LoRA and the cumulative LoRA family. Then, we empirically compare them on natural chronological split and user-disjoint split.
Single evolving LoRA. At stage t, the LoRA matrices A, and B₁ are initialized (i.e., parameter inheritance) from the previous stage (Ar-1 and B,-1) and fine-tuned on new data Dr:
We Wo + B,A,
Bi+ A,t AL-1,
12 2,
(4)
where Wo is the pretrained LLM weight (i.c.. not LoRA updates). This baseline is simple and adapts effec- tively new data, while parameter inheritance provides partial preservation of past knowledge at initializa- tion. However, it inevitably overwrites useful past knowledge during fine-tuning, leading to forgetting.
Cumulative LoRA Variants. To mitigate forgetting, cumulative LoRA has been widely used in domains such as vision (Wu et al., 2025; Liang & Li, 2024). At stage t. it reuses frozen adapters from past stages and
Adapting the tokenizer new items over time is an interesting direction; here fix the item tokenizer to isolate continual adaptation of the model (LoRA).
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Table 1: (Left) Design choices; (Right) performance gain vs. single evolving LoRA (w..t. NDCG@5) in
different task settings Instrument dataset.
Design choices
Task settings (2) Natural split
Method
Learnable mag.
Only latest
Param inherit
(I) User-disjoint
DifT. (1)+()
SUMLORAAE
8.13%
26.77%
18.64%
SUMLORApan
X
20%
22.00%%
9.85%
SUMLORA A LIINHBR
33 20%
1.57%
-1822%
SUMLORAL ALRAALLTETTT ALTTETT-NUEETTT
0.00%
2.36%
-2.66%
SD-LLOR LATSSTTNNSRRR
3.25%
0.79%
2.46%
adds new trainable adapter by summing them during both training and inference. The effective update is
W = Wo + Σ a B,A + B,Ac,
t 2 2,
(5)
where Wn the pretrained LLM weight; {B. and {A: are frozen adapters from previous stages: and B₁, Ae are trainable at stage t. Following prior practice, we use normalized directions B; BB/l| and A, A:Il| which improves stability. The scalar o; are fixed or learned magnitudes. This design explicitly enhances stability and expands LoRA's effective capacity, expected too work well when sequen- tial tasks interfere minimally. However, for recommendation where user preferences evolve, this rationale weakens. To examine this, we study SumLoRA, which uses simple summation, in four variants: (i) all, summing all past adapters; (ii) latest, summing only the most recent adapter; (iii) all+inherit, summing all past adapters with parameter inheritance; and (iv) inherit, using only the latest adapter with parameter inheritance. The all variant corresponds to the original design of cumulative LoRA family. We also consider SD-LoRA, which extends summation with learnable magnitudes, with all equivalent to Wu et al. (2025). For analysis, focus the empirically stronger inheri. Table summarizes these design choices.
Two settings. We evaluate methods in the two settings derived from the same user-item interaction data of Amazon Review (Musical Instruments) dataset: (1) Natural chronological split: Interactions are sorted by time; a large portion (e.g., 60%) is used for pretraining (i.e., D1), and the remainder is divided into four equal incremental blocks, yielding D....... Dz- For each Dj, we apply leave-one-our per user (second-to- last item for validation, last item for test). See Appendix C.1 for details. (2) Pseudo user-disjoint split: Users are randomly partitioned into disjoint sets for Dr (4 = 1,....5), 5), with block sizes matched to the chronological split. Item order within each user's sequence is preserved. While similar users may induce some shared preferences across stages, this setting introduces relatively less cross-stage interference than the natural chronological case.
Results. Table I reports (1) the relative gain vs. single evolving LoRA on the user-disjoint split, (2) the relative gain on the chronological split, and (3) their difference (i.e., (1)-(2)). We summarize the findings: First, the Diff. column shows that the original cumulative design (i.e., SUMLORA performs much worse in the natural chronological setting than in the user-disjoint setting, confirming that it is better suited for tasks with minimal interference and ill-suited for recommendation. Second, in the Natural split, SUM- LORA ALL performs worst, followed by latest, all+inherif, and latest+inherif, suggesting that (a) aggregat- ing all past adapters hinders adaptation, and (b) parameter inheritance is essential for gradual, proximal evolution of LoRA with respect to the previous state. Finally, SD-LORALATEST ALTTETT-RUREE fails improve over mixedmmagittude SUMLORALATES A since useful past components are entangled with stale ones, mak- ing weighting ineffective. Overall, continual recommendation requires evolving adapters with controlled stability, rather than rigid reuse of past ones, to capture user preference dynamics.
4 PROPOSED FRAMEWORK: PESO
Our design philosophy is to (1) avoid using multiple LoRA adapters, which implicitly assume task inde- pendence, and (2) preserve past knowledge in way that supports understanding of current user behavior.
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Continuous Recommendation with Preference Shift
Future (unseen)
Capture Evolving User Preferences Balance Stability Plasticity Space Efficient
Historical Distribution
X
Temporal Shift
Latest Distribution
DT+1
D1
D2
3
Ds
1--1
Dr
Aligned
W à
By
AT
Adopt with Proximat Regularizer
A1
A2
A3
Ar-1
A7
History Adapt
W Whst
WT
W
B;
B2
B3
Adapted PESO LoRA
Add
Stage-T Adaptation
Historical Frozen LoRAs
New LORA
Cumulative LoRA
Excessive Stability
Weak Plasticity
Space Heavy
PESO LoRA Adapter
Figure Conceptual overview of Cumulative LoRA and our proposed PESO with proximal regularizer.
Guided by this, we propose PESO (Proximally rEgularized Single evolving IOra), which maintains single evolving LoRA adapter and regularizes each update by keeping the current adapter close the previous one (shown in Figure 1). We begin by presenting the quadratic proximal framework and its theoretical implications, and then instantiate PESO with a softmax- proximal demonstrate its practical effect.
4.1 SINGLE EVOLVING LORA WITH PROXIMAL REGULARIZER
General framework. We maintain single evolving LoRA and anchor each update to the previous adapter with proximal term. Let e E R denote the concatenation all flattened LoRA A/ parameters at time stage t. We partition coordinates into groups 9 {1...., ., G} at the level of LoRA factor matrices, that each group corresponds to the flattened parameters of either A or for one LoRA-injected module (e.g.. attention projections q/ q/k/v/ /k///o and MLP projection layers). We write v(8) for the parameters in group g. The overall loss function for time stage is
L = P. +
(8) 1
Vr f- Ut- at init, H
(6)
8=1
proximal term
where LP is the data-fitting term on Dj (i.e., cross-entropy, Eq. (2)), ||=||3/ 2TH X 0 controls regularization strength, and each H(2) 2 0 is (symmetric) PSD metric that is fixed during stage t; can be constant (e.g., H(O) I, corresponding to the I.2 case) or precomputed at the previous adapter u We initialize t U the proximal penalty starts at zero and grows only as departs from 0t This design leverages the natural competition between the data-fitting loss (which pulls toward the optimal state for D2) and the proximal term (which pulls toward the previous state). Next, theoretically show how this yields data-aware, direction-wiic wise guidance in the LoRA subspace.
[9]
Theoretical setup. To analyze how the proximal term interacts with the data- loss, approximate the data-fitting term. We restrict updates to a fixed m-dimensional LoRA subspace. Let d E Rd be the parameter vector (base LLM and LoRA) after training the first data block (f=1). From t 2, let ((0) 0g + Uv with U E Rdxm and non-LoRA coordinates frozen (i.e., assume U = [Tm 0). For input I (prompt, item sequence) and next-item token y, let s0, x) Linearize once at v- = 0:
be the scalar logit of the ground-truth token.
s(0 + Uv xe 2(fn.x) + (xx) To,
$(x) = U (Vs(((n, ".
(7)
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where (xx) is tangent feature of æ. For analysis we use a mean-squared surrogate for Eq. (2) and define the stage-t optimum e7 = arg min, p. (v). A second-order expansion at 27 yields quadratic loss
(v) =
1 20 (v 2
1
Ee (v v,),, x
Σ, = EDND, (x)) 0,
(8)
where Ee is the langent-feature second-moment moment matrix for time stage capturing how much the stage-t data supports different directions in the LoRA subspace (i.e., u Σςu Ep, ( Tu) Vu E R"), See Appendix B.1 for full setup and assumptions. In what follows, we present a general proposition showing that our proximal framework yields direction- wise interpolation between the new optimum and the previous adapter, and then derive its 1.2 corollary to provide intuition into the stability-plasticiyy balance.
Proposition (Generalized eigen interpolation with a quadratic proximal), Ler E, =I = 0. Define the = O, with each H symmetric PSD and independent of during stage t. Under the quadratic approximation in Eq. (8), our loss Eq. (6) is:
block-diagonal proximal metric Ht-1 == blkdiag H "
.........
.....
Li(v) = - - (u-u,) T - + + (v 1 v--) H1-1 vt-1).
(9)
Let 0, min, Lv).. Let [lgk: Pa) p=) be generalized eigenpairs of (E,, H,-1) range(H(-1) (i.e., M P1-H1-19k). normalized by q: 11- dig. where rank(Hl-1). With (u, w) w) ==
u. T STH,- H,-1W,
(be, H.-,
Pk: Pk +
(vt Hk)
Pk + X (k)
k 1
(10)
The proof of Proposition is deferred to Appendix B.2. To build intuition, we specialize Proposition the L2 case by taking H,-1 I. Then the generalized eigenpairs reduce to ordinary eigenpairs of Σ, and (,, becomes the standard inner product, yielding the following corollary.
Corollary 2 (L2 special case of Proposition 1). Take Hi-1 I. If Eigk o6go with {ai} orthonormal,
of
(0e, gk) o + (07 4h)
of + (k)-1,
h= 1, m.
(11)
In nutshell, Corollary 2 shows a data-aware balance between stability and plasticity in our framework. Recall that Σ, = ED, { (1x (x)) summarizes how much the stage-/ data supports different directions the LoRA subspace. Its eigenvectors d/ are principal directions, with eigenvalues of measuring the strength of support along each direction under D.. By Corollary 2, along any Ta the update is a weighted average of of and 1- with weight toward oi equal to o + A). Thus, when of is large (strong support in D:), ig moves toward e." along q (e.g., the user starts engaging more with mystery than sci-fi); when of is small (weak support), d stays close to 0t-1 (e.g., stable brand affinity not observed this week). If of = 0. the component along ds is kept exactly from the previous stage. See Appendix B.3 for a more detailed intuitive explanation.
4.2 SOFTMAX-KL AS A PROXIMAL REGULARIZER
As shown earlier, the L2 proximal (i.c., H(")) = Z) is a special case of our general proximal form with H1-1 However, it penalizes all coordinate changes equally, treating modules uniformly, ignoring internal structure, and not adapting to the previous state 0 To address this, we instantiate the proximal term with a softmax KL proximal that preserves per-module structure and leverages the previous state. Formally, the
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stage-t objective of PESO is:
C
L = L. + Dkl(softmax(1x)) (1))
vt + f 02-1 at init.
(12)
Kua (t))
We first show that the softmax-KL. proximal locally reduces to a quadratic form, and then give corollary that interprets it as p-weighted variance, providing an intuitive view of its module-wise stability.
Proposition 3 (Per-module softmax-KI is locally quadratic). Let ut be the subvector for group g (l.....G) (e.g., LoRA A/B of module), p(9) softmax((4) (1 and A (g) = u691 0--1- For small (
A (Al9) T( (ding(p(o)) pl0))19) ) o(11)13)
Koikk 4-1) (bu((1,,*11)=--
Σ
(13)
2 9=1
A + o(II|13), with H = diag(plo)) - (p(o)) = À A blkdiag ( i-1...... H
2
=:1--1
The proof of Proposition deferred to Appendix B.4. Proposition 3 shows the softmax-KL proximal is locally equivalent to the quadratic form Ž llu; et- H with H,-1 = blkcdiag( (H ( H()))
Hence, Proposition applies directly, suggesting data-aware balance of stability and plasticity.
Corollary 4 (Softmax-KL equals p-weighted variance). With notation as above, up to an additive constant,
Kbik(V,, 11-1) = Var Yarp.o(A(i))
(14)
8=1
where Var p(u) - Pi (9) (A m(0022 and p( = Pi (9)
A
Corollary 4 shows that the softmax KL proximal can be interpreted as p-weighted variance of parameter changes. Consequently, it penalizes (1) reshuffling (i.e., relative (centered) changes) within each module's s LoRA factor, and (2) deviations more strongly for coordinates with higher prior mass. This yields module- wise, previous-state-awann stability without killing plasticity: updates still move toward new optima where data provides strong support (as in Proposition 1), while staying close to the previous state otherwise.
5 EXPERIMENTS
We design experiments to answer four key questions: RQ1: To what extent does PESO outperform competi- tors? RQ2: Which proximal regularizer works best PESO? RQ3: How effectively does PESO balance stability and plasticity under different user drift patterns? RQ4: How do hyperparameters affect performance PESO? RQ5: How does PESO compare to traditional continual recommenders?
Please refer Appendix for additional experimental results and analyses, including distribution-shifi quantification, comparisons with additional baselines (full-parameter fine-tuning, O-LoRA, AM-LoRA, LSAT etc.), experiments with other backbones and datasets, and an efficiency analysis.
5.1 EXPERIMENTAL SETTINGS
Datasets. We use the real-world Amazon Review dataset, which contains user reviews (treated as implicit interactions) products over time. We focus three categories: Musical Instruments, Movies & TV, and
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Table 2: Recommendation performance averaged across time stages for PESO and continual competitors. The best and second-best results marked in bold and underline, respectively.
Instruments
Movies
TVs
Books
Methods
H@5
1@
N@5
N@
H@5
1@
N@5
N@
H@5
H@:0
N@s
N@ 10
PRETRAIN
0.0166
0,0216
0.0115
0.0131
0.0166
0.0231
0.0111
0.0132
0.0258
0.0283
0.0196
0.0204
SINGLE EVOLVING LORA
00181
0,0253
0.0127
0.0150
0.0155
0.0247
0.0116
0.0557
0.0308
0.0344
Cumulative Family
TRR...
60056
01024
016105
66124
0013
0.0067
0.0079
00023
D00193
INFLORAsast
0.0131
0.0167
0.0090
0.0102
0.0073
0.0092
0.0047
0.0054
0.0152
0.0197
0.0108
0.0123
0.0149
0,0219
0.0104
0.0249
0,0324
0,0171
0.0195
INFLORA AugTSAReee
0.0137
0.0202
0.0095
0.0116
0.0094
0,0132
0,0060
0.0072
0.0225
0,0288
0.0153
0.0174
STMM A
6014
0.T025
0.0093
071122
0.0130
0.0067
0.0076
00035
00030
01001 76
D'022
SUMLOR ALAEET
0.0143
0.0221
0.0099
0.0124
0.0102
0.0130
0.0067
0.0076
0.0246
0.0354
0.0161
0.0196
SUMLORA
0.0182
0.0260
0.0129
0.0154
0.0160
0.0234
0.0107
0.0131
0,0409
0,0514
0.0287
0.0321
SUMLORAN ALATST OREENTT
0.0185
0.0255
0.0130
0.0152
0.0172
0.0237
0,0114
0,0135
0,0433
0,0542
0.0306
0.0341
SD-LORAALL
0.0156
0.0226
0.0107
0.0129
0.0094
0.0133
0.0061
0.0074
0,0238
0,0351
0.0162
0.0198
SD-LoRAwarT
0.0156
0.0218
0.0102
0.0123
0.0101
0.0142
0.0069
0.0082
0.0241
0.0327
0.0159
0.0186
SD-LOR A
0.0176
0,0238
0,0124
0.0144
0.0118
0.0171
0,0077
0.0094
0.0332
0.0412
0.0234
0.0260
SD-LORA annntweenn
0.0184
0,0254
0.0128
0.0150
0.0530
0.0308
0.0340
PESO
0,0193
0.0268
0.01338
0.0162
0.0180
0,0251
0,0118
0,0141
0,0448
0.0569
0.0311
0,0351
Performance Gain (%)
vs. SINGLE EVOLVING LORA
6.63%
5.93%
8.66%
8.00%
2.86%
1.62%
1.72%
2.1%%
0.00%
2.15%
0.97%
2.03%
vs. SOMLoRRALSSOOS wissOrT
4.32%
5.10%
6.15%
6.58%
5.91%
3.51%
3.46%
4.98%
1.63%
2.97%.
Vs. SD-LORAAAaa
4.89%
5.51%
7.81%
8.00%
9.09%
6.81%
8.26%
7.63%
3.70%
7.36%
0.97%
3.24%
B 2 Detailed preprocessing steps and dataset statistics are provided in Appendix C.I. The processed data yield (Di...., Ds), J, where Dj is large pretraining set and D2, D1 are smaller incremental sets.
Evaluation. For each Dr, we apply Icave-one-out evaluation per user, reserving the last item for testing. Following (Wang al., 2024; Bao et al., 2025a), we construct multiple training pairs (J) per user using a sliding window of size 20. Starting from the LLM pretrained on D1, at each stage t = ........ 5 the model is fine-tuned and then generates 10 items via constrained beam search restricted valid item tokens. We report Hit @ 5/10 and NDCG@ @ 5/10, averaged over D D4. Full evaluation details are in Appendix C.I.
Compared methods and implementation details. We compare PESO with several LoRA- baselines for continual learning, all using the same cross-entrope loss and Llama-3.2 IB (Grattafiori et al., 2024) as backbone. The bottom baseline is PRETRAIN, trained on Dj and directly evaluated at t .............................. 4. Among continual methods, we consider: (1) single evolving LoRA; and (2) the cumulative family, which combines past and current adapters: SumLoRA, SD-LoRA (Wu et al., 2025), and InfLoRA (Liang & Li, 2024). SD- LoRA learns magnitudes for normalized past adapters, while InfLoRA precomputes LoRA-A via SVD of the input covariance and trains only B, to better align with current data and reduce task-interference. As discussed in Section 3, original cumulative designs use all past udupters without inheritance (all). For recommendation, we further test three variants: latest (most recent only), all+inherit (all with inheritance), and latest+ (latest with inheritance). For hyperparameters, À is searched over [0.5, 1.0, 2.0, 5.0, 8.0] (set to 2.0 for Instruments, 5.0 for Movics&TV and Books). SD-LoRA magnitudes start at 1.0.
5.2 EXPERIMENTAL RESULTS AND DISCUSSION
Main Results (RQ1). Table reports results across four metries and three datasets in continual settings. First, all continual learning methods consistently outperform PRETRAIN, highlighting the importance of adapting to new data to capture evolving user preferences, even when incremental data is much smaller (e-g.. 10%) than the pretraining data. Second, neither single evolving LoRA nor the cumulative family dominates, while PESO consistently achieves the best results, with average gains of 3.71%, 4.62%, and 6.26% over the best competitors (SINGLE EVOLVING LORA, SUMLORA, A.TALLLRAATTSSSTNEEETTT and SD-LORA A.LORAAAATER ALTTESTTNEEEE Cumulative LoRA, though more complex and storage-heavy, often underperforms or only matches single evolving LoRA, as rigidly reusing frozen adapters overly constrains adaptation to evolving user preferences.
2htps//aamaazonreviiws222222... github..o
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Single Evelving LoRA (iee. A-01
PESO
Single Evolving LoRA. Orthogonalty
Lont-OutputKLL Movies & TVs
Pen-Rank-KL
PESO
Performance
Instruments
Movies TVs
Books
0.0426
Performance
Instruments
Books
0.0195
0.0180
0.0190
0.0170
0.0180
0.0170
0,0160
0.0550
0.0400
0.0165
0.0402
0.0160
0.0150
0.0320
Figure 2: Performance comparison of different reg- ularization methods against the previous LoRA.
Figure 3: Impact of the scaling weight À for the prox- imal term on PESO performance.
By contrast, PESO uses flexible proximal regularization toward the latest state, allowing the data-fitting loss and proximal term to jointly decide what to preserve or update. Third, as discussed in detail in Sec- tion 3, regarding SumLoRA and SD-LoRA, original cumulative designs (using all past adapters without inheritance) perform worst, while variants with inheritance only the latest adapter do better. Notably, some non-inheritance variants even fall below PRETRAIN, showing that without gradual evolution, contin- ual learning harm more than help. InfLoRA yields the weakest results overall, likely because, although it incorporates input data covariance information, freezing A prevents inheritance and gradual adaptation across time, both of which are crucial in continual recommendation.
Analysis on Proximal Regularizer (RQ2). Unless otherwise noted, all subsequent subsections report aver- age performance across four metries (Hit@5, Hit@ 10, NDCG@5, NDCG@ 10). We compare PESO with four alternative regularizers on the previous adapter: orthogonality, 1.2 proximal, LoRA- Per-Rank KL (Figure 2). Orthogonality, an cenfffrrnncemmmimmiaatiii strategy common in vision, performs far worse than all methods, showing that minimizing interference across stages is harmful in continual rec- ommendation. 1.2 proximal, which penalizes the L.2 distance between current and previous parameters, often comparable to single evolving LoRA but worse than PESO, suggesting that uniform constraints are insufficient. LoRA-Output KL (softmax-K() KL applied in LoRA output, i.e., function space) and Per-Rank KL (softmax-KL KL applied on each rank of LoRA matrcies, i.e., finer parameter granularity) are slightly worse or comparable to PESO, suggesting that regularization directly in the parameter space with module-aware structure is more effective, at least sufficient, compared to output-level overly fine-grained constraints.
KI., and
Plastiity Analysis via User Groups (RQ3). To examine how PESO balances long-term interests with newly evolved preferences, we eval- uate the final model on two user groups in the In- struments dataset, which serve as proxies for differ- ent drift patterns: (1) Dormant Users: Users who were active in earlier blocks, absent in intermedi- ate blocks, and return in D₃. This tests stability
Table 3: Performance (NDCG@5) across user groups representing stability and plasticity tests.
Method
Dormant Users New Users
Single Evolving LoRA (Plasticity) Cumulative LoRA (Stahility) PESO (Balanced)
0.0154 0.0164
0.0116
0.0101
0.0170
0.0122
(retention of long- term preferences). (2) New Users: Users who appear only in Da. This tests plastic- ity (adaptation to new signals). Table 3 illustrates the trade-off: Single Evolving LoRA excels on New Users but performs poorly on Dormant Users due to forgetting, whereas Cumulative LoRA (i.e., SUM- LORA ( preserves stability but adapts less effectively to New Users. PESO achieves the best performance on both groups, indicating strong stability-plasticin balance.
Hyperparameter Analysis (RQ4). (a) Scaling parameter for proximal term PESO. Figure 3 shows performance as À varies. Starting from À = 0 (i.e.. single evolving LoRA), performance improves as X increases, then either decreases or platcaus, confirming that À serves as a tunable trade-off between stability and plasticity: too small harms stability; too large harms plasticity. In addition, performance is not highly sensitive to X, as results remain stable across a broad range of values. (b) Learning rate for continual stages. See Appendix C.2 for full results and discussion. Since incremental datasets are much smaller
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than the pretraining set, performance is highly sensitive to learning rate. Our results show that using the pretraining rate leads to overfitting, while scaling the rate down (> 0.05-0.1x) yields the best performance.
Comparison with Traditional Continual Recommenders (RQ5). Details are in Appendix C.3; Table 4 shows subset (top: traditional, bottom: LLM--baed). L.LM-based methods generally outperform traditional two-tower models, except on Instruments, where explicit dual modeling of users and items helps. While PESO achieves higher absolute performance, continual methods like PISA (Yoo et al., 2025a) yield larger relative gains in two-tower models, reflecting the advantage of explicit user embeddings in capturing preference drift and the challenge of doing so with LLMs.
Table 4: Comparison of traditional and LLM-based methods.
Method
Instruments
Movies & TVs
Books
Pretrain Fine-tuming PISA
0.0153
0.0028
0.0041
0.0180
0.0114
0.0218
0.0194
0.0106
0.0301
Pretrain
0.0157
0.0160
0.0235
Finc-tuning
0.0178
0.0169
0.0414
PESO
0.0190
0.0173
0.0422
6 RELATED WORKS
LLM-based Generative Recommender Systems. Recent advances in large language models (LLMs) have inspired generative approaches recommendation, where the task is framed sequence generation. Instead of ranking items from a candidate set, the model autoregressively generates the next item token given a user's s interaction history. Variants of this paradigm includes zero-shot prompting (Lyu et al., 2023), ID-token generation (Tan et al., 2024; Wang et al., 2024), data-efficient fine-tuning (Lin et al., 2024b), uncertainty- aware decoding (Kweon et al., 2025), and alignment techniques for recommendation objectives (Cao et al., 2024; Bao et al., 2025a; Chen et al., 2024). These works demonstrate that LLMs can flexibly leverage textual and structural signals for recommendation, but they typically assume static data. In contrast, real-world interactions arrive continuously, requiring models that can adapt to evolving user preferences without costly retraining. Our work addresses this gap by studying continual adaptation of generative LLM recommenders.
Continual Learning for Foundational Models and LoRA. Classical continual recommenders use param- eter regularization (Wang et al., 2021; 2023b; Yoo al., 2024), replay buffers (Ahrabian et al., 2021; Zhang et al., 2024b; Zhu et al., 2023; Zou et al., 2025), or dynamic architectures (He et al., 2023; Zhang et al., 2023; Lee et al., 2025b). With large foundational models, parameter-efficient fine-tuning (PEFT) has be- come central, with LoRA (Hu et al., 2022) as standard choice. In vision, several continual extensions have been proposed, such as cumulative aggregation of frozen adapters (Liang & Li, 2024; Lu et al., 2024) and learnable magnitude scaling (SD-LoRA) (Wu et al., 2025), which effective when tasks interference is minimal. However, these methods are less suitable for recommendation, where user preferences evolve over time. Our work differs by proposing a proximal single evolving LoRA that avoids the forgetting of single evolving LoRA and the rigidity of cumulative LoRA, better suiting the continual recommendation setting.
7 CONCLUSION
We have studied the problem of continual adaptation for LLM-based generative recommender systems, where user interactions arrive over time and preferences evolve. Single evolving LoRA offers strong plas- ticity but suffers from forgetting, while cumulative LoRA improves stability but entangles outdated signals. Our proposed PESO strikes a better balance by maintaining a single adapter and regularizing it toward its prior state, allowing the model to decide what to adapt and what to preserve. Our theoretical analysis has shown that the proximal design provides data-aware, dtrection-wiss guidance in the LoRA subspace, and our instantiation with per-module softmax-K, KI further preserves internal parameter structure. Empirical results across multiple real-world datasets confirm that PESO consistently outperforms existing baselines, achieving a superior stabiiit-pllastcit balance. Future directions include drift-aware and more personalized regular- ization, as well as integrating our approach with other parameter-efficient tuning methods to further improve continual adaptation large foundation models.
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ACKNOWLEDGEMENTS
This work is supported by NSF (2324770) and AFOSR (FA9550-24-1-0002) The content of the information this document does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
ETHICS STATEMENT.
This work focuses on continual learning methods for large language model (LLM)-based recommender systems. It does not involve human subjects, sensitive personal data, private user information. All exper- iments are conducted on publicly available benchmark datasets (Amazon Reviews). We followed standard preprocessing protocols, and personally identifiable information mender systems can influence user exposure to content, this study is purely methodological and does not deploy or interact with real users. We acknowledge the potential societal risks of recommendation technolo- gies, such as reinforcing biases or filter bubbles, and we emphasize that our method (PESO) is designed as a modular continual learning technique, independent of any particular application domain societal factors.
used released. While recom-
REPRODUCIBILITY STATEMENT.
The paper provides: (1) detailed descriptions of datasets, preprocessing steps, and evaluation protocols (Sec- tion 5.1, Appendix C.1); (2) clear definitions of baselines, the proposed method (PESO), and its theoretical analyses (Sections 3, Appendix B); and (3) hyperparameter settings, search ranges, and sensitivity anal- yses (Section 5). Results are reported across multiple datasets and metrics for robustness. Full proofs are included Appendix B. will release our implementation and data-processing scripts upon publication to ensure reproducibility.
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Zhichen Zeng, Ruizhong Qiu, Wenxuan Bao, Tianxin Wei, Xiao Lin, Yuchen Yan, Tarek F. Abdelzaher, Jiawei Han, and Hanghang Tong. Pave your own path: Graph gradual domain adaptation on fused Gro- mos-Wasserstenn geodesics. Transactions on Machine Learning Research, 2026b.
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A CONCEPTUAL MODELING OF EVOLVING USER PREFERENCES
We assumee an initial model is pretrained offline on base data D1, and then the-ttned sequentially on chrono- logically arriving blocks D2....; Dr.
Let xr denote u's interaction history available before stage and let P.(y 1) be the conditional distribution of the next item y during stage I, representing user preferences. continual recommendation, these distributions evolve over time, which can conceptually modeled as
Pely --=1) x a, Pr | " + (1 at) Qr(v | 1
(15)
where Pt-1 captures stability (persistent long-term preferences), Qs captures plasticity (new shifting preferences estimated from new data), and a, E [0, 1] controls the balance. The goal is to minimize expected risk upcoming interactions by balancing stability and plasticity.
B DETAILED THEORETICAL ANALYSIS
B.1 SETUP AND ASSUMPTIONS
Assumption (Parameters and LoRA subspace). Let A E IRd denote the vectorized concatenation of all model parameters (base LLM and LoRA). Let On be the parameter vector after training on the first data block (1=1). From L 2 2, restrict updates to a fixed m-dimensional LoRA subspace spanned by columns of U mdxm and write
(16) with all non-LoRA coordinates frozen. Without loss of generality, assume U = [Im 0), i.e., the LoRA subspace is the first m coordinates.
0= 0o + U',
V E
R
Assumption 2 (Linearization and tangent features.). Let s(f, E R be the scalar logit of the ground-truth next item. We linearize s at v = 0 (i.e., at 8 = oo):
800 + Uv,x) s(0o, x) + + Vos(O0,,)) so(x) + (Tx)).
(17)
with tangent features of
((x) = U Vos(Bo. E R...
(18)
Assumption 3 (Data and loss.). Let (x.y) D. be examples in block In recommendation, (prompt, item sequence) and E V is the next-item token. Training typically uses cross-entropy on log- its; for analysis, we use a mean-sques sqaa--quaredderror (MSE) surrogate. Define the block-t risk
(P)()) = B(E.,)
[s(00 (..../)(((000
+Uv re(a, v))
-r(x,))))
(19)
where re (((2)) y) E IR is calibrated target score for the ground-trui truth next item.
Note that under the linearization, this yields a quadratic risk with positive-semideffnite curvature. All later proofs use only this PSD curvature, not the exact form of r.. Assumption 4 (Quadratic form under the linearization.). Substituting s(0o v, ~ so(x) + T gives, up to an additive constant,
LP (v)
"E 6. + 1 2
b (o=((((()(())) rx( (x)), E (o==),{((0)0((1 2 0.
(20)
Define stage-t optimum of any minimizer of (P)
arg min T, (v).
(21)
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A second-order Taylor expansion of L. (p) (P(((() + (vt))) + 2/ --)) V (vx) (v 10-17 o/).
ot gives
(22)
Dropping the constant term, the centered quadratic risk used throughout is LP((v) = (v o5) Σ. (v - 2/),
(23)
where E, is the tangent-feature second-moment matrix for time stage I, capturing how much the stage-l data
supports different directions in the LoRA subspace (i.e., Eeu ED, [+(z) u)² Yu E ). Also note
that we fix the linearization at fg: s(0g + Uv, x)) = so(x) + (ax) To with ((x) U Vas z). Although ((x) fixed across t, the Σ, E., (+)))(()) varies with the data block distribution, so drift is captured via E, and the shifting optimum v/--
Remark: relinearization per block. If desired, one may instead relinearize 0g + UU--1 replacing (xx) by ((-1(z) = U U Vos(0o + UV,-1. I) and Σg by 1 All propositions and closed forms carry over with these substitutions; the only change that the curvature reflects the anchor Ut-1 of the current block. We found fixed linearization sufficient and notationally lighter.
B.2 PROOF OF PROPOSITION 1.
Assumption (Complementarity (no doubly- flat directions)). On the LoRA subspace R", ler E, 2 0 and H,-1 2 0 be symmetric and fixed with respect to u. Assume
kcr(E,) kor (H {0].
(24)
Equivalently, for any Rm (0), x E5 0 or x² Hz-1 0.
Proof. Recall that in denotes the minimizer of (v) in Eq. (9).
(i) Closed form of the minimizer. Differentiating yields
(v) = (v v)) + AHi-1 11-1).
(25)
Setting ((t) = 0 gives the normal equation
(E, XH, é E + XH1-100-11
(26)
Under Assumption 5, for any #0, 0,
x T( + AHI- x EeI + Ax HI-1 > 0,
(27)
E, AH 0 and Eq. (26) has the unique solution
0 = (E. + XH -1 (E,,, XH1-10t-1)-
(28)
(ii) Direction-wise interpolation in generalized eigenbasis. Let {lgk, Pk) [=)]k= be generalized eigenpairs of (E,, H₂-1) on range H, i.e., Eng, = pI: H1k1 normalized by q. H:-19j = 8.z. Left-multiply
Eq. (26) by 9% to obtain
Is
T E + Aujle H. 14 = 1 Ev + Agple H H
(29)
Using the symmetry of E, and H₃-1, together with E,qr- = Pv: HigH1-10 9% and (u, w) H = T H,-1w, we obtain
(pk + (0 9k) ph (vi (v) H.-1 + (11-1.9k)H. Dividing by Pk + yields the claimed interpolation for k = 1, ,.
(30)
(iii) Note directions in ker( (H,1)) If H > O. then r m and the above holds for all directions. If H is singular, the statement is posed on range( HH,1); uniqueness of 0g is still guaranteed by Assump- tion 5, which rules out doubly-flat directions in ker (E,) n ker 1).
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B.3 INTUITIVE EXPLANATION OF COROLLARY 2
Here, we provide an intuitive explanation of how PESO offers data-aware, direction-wise guidance.
Semantics of directions (qk): decoupled preference axes. The eigenvectors qu of the tangent- feature second-moment matrix E, represent principal directions in the LoRA subspace. Intuitively, these directions can be viewed as approximately independent latent axes of user behavior (e.g., one axis may correspond to "sci-fi affinity" while another reflects "price sensitivity"), ) Since the q 's form orthogonal basis (in the L2 case), PESO can adjust the model along axis with minimal interference to others, helping preserve long-term knowledge while adapting to new signals.
Mechanism (of): signal strength as a gate. The eigenvalue of measures how strongly the current block D, supports updates along direction Ts. Corollary 2 shows that PESO interpolates between the new-data optimum ot and the previous adapter vt-1 with weight / + A):
Large of (strong support + plasticity). When D. provides strong evidence along a direction (e.g., the user recently engages heavily with mystery content), the model is encouraged to move toward Vy along qt- enabling rapid adaptation to evolving interests.
Small of (weak support = stability). When D. provides little evidence along a direction (e.g.. stable preference that does not appear in recent interactions), updates along q are wown-weighted and the solution stays close to 1 preventing long-term interests from being overwritten by weak noisy signals.
B.4 PROOF PROPOSITION
To prove Proposition 3, we first establish the following proposition for arbitrary and 1 and then extend it to the blockwise case. Proposition 5 (Local quadratic form of softmax-KL, proximal), Let p == softmax(v:-1) 01-1- Define
R" and A
K(A)= =
DKu(ssftmax((==-1 + A) softmax(rt-1))
(31)
Then KC(0) VK(()) = O, and the second-order Taylor expansion al A is
X(A)= = 1 A (ddiag(p) - pp ppp) A + ||)
(32)
Equivalently
K(A)= = 2 1 Pi (A, 11))) + o(1)))))
Σ p A
(33)
Var,(A)
Proof. Write r(A): = softmax(") + A) E IR" and p r(0) = soft By definition,
n K(A)= Σ
(i( log Ai
(34)
i=1
(i) At A = 0 we have r(0) = p, so
KC()) Σ pi loglpl////pp... 0.
(35)
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For the gradient, differentiate using the scalar identity d log(x/c)] log(x/c) +
ax
Or
log + 11
(36)
a
aA o
Pi
i=1
Evaluating at A = 0 gives log(ri/p;) 0 and hence
VK(O). Σ
3r:
0
ri(A))
0
(1) 0,
(37)
aA
A=
asn
A=0
AA
i=1
i=1
since softmax outputs sum to one for all A.
(ii) Differentiate the gradient once more:
OCC
0r;
(log +1 1) pi
dr: Òr:
(38)
aA UZ,
At A 0, the first sum becomes
E; Pr: (since log(ri/pe) 0), which is zero because
>, (A) = 1 for all A. Thus,
V2K(0) =
Ôr: LA
Dr: Lad, A=0
(39)
ab
pj
A=0
It remains to compute the Jacobian of softmax at 1z-1:
,r:
θ e%
Jic
Lad
A=0
Dva
Σ,
E
1=
= (1(i a} -Pa).
(40)
Therefore,
a Σ
n
V V2((0)]
Jia Jeb
pi (1fi a} (a) = b} pb).
(41)
ab
1=1 pi
i=1
Expanding the sum gives
1p a} 1f;= = b) pb Σ a(== - Pa Ep: pi -iii=b] + PaPb Σ p:.
(42)
Since E.P: = 1 and E, Pi 1fi = a. = -aa this equals
8ab Pa PaPt
PaPs + PaPs Sab Pa PaPb.
(43)
i.e.
V2K(0) = diag(p) pp
(44)
(iii) By Taylor's theorem,
K(A)= 1/2 A T (diag(p) -pp A + o(1A|19).
(45)
Finally, note the algebraic identity (weighted variance):
(diag(p) - ppp] )p Σ PA
Σ P:A.) = Σ Pj (A. - -1)3, ;== Σ A.A.
(46)
1=1
1=1
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Table 5: Dataset statistics.
Total Users
New Users
Total liems
New Items
Total Interactions
Avg Seg Len
Sparsity
D1
17,046
17,046
40.471
40,471
141,788
8.32
0,9998
D2
1,772
1,183
8,346
2,900
13,197
7.45
0,9991
D3
1,821
1,265
8,325
2,909
13,334
7.32
0,9991
Instruments
D4
2,289
1,684
9,617
3,864
18,811
8.22
0,9991
D5
2,238
1,699
9.131
3,365
17,573
7.85
0.9991
D1.
22,877
NA
53,509
NA
204,703
NA
NA
D1
17,928
17,928
39,228
39,228
190,411
10.62
0,9997
D2
1,866
1,141
11.612
1,479
17,665
9.47
0,9992
D3
2,106
1,200
12,658
1,926
19,874
9.44
0.9993
Movies & TVs
D2
2,284
1,357
13,788
1,882
22,929
10.04
0.9993
D3
2,332
1,552
13,491
1,559
22,225
9.53
0.9993
DE:S
23,178
NA
46.074
273,104
NA
NA
D1
15,406
15,406
35,984
35,984
164,858
10.7
0.9997
D2
1,807
618
7,155
2,711
13,918
7.7
0.9989
Da
1,672
6.484
2,278
12,395
7.41
0.9989
Books
D4
1,948
7.154
2,657
14,824
7.61
0.9989
Da
1,652
1,025
2,274
7.26
0.9988
D1.5
18,318
NA
45,904
NA
217,985
NA
NA
Now we prove Proposition 3. Since the blockwise softmax- KL. regularizer acts independently on each group
9.
Kolk (D)
E Dku(softmax((/_) A softmax( (u!a)))
(47)
9=1
where (0) = =/ (9) Applying Proposition 5 to each group yields block Hessians
( diag(pls)) - pl9) (p(o))T,
(48)
which assemble into the block- diagonal
blkdiag (1) ,.... H(G)).
(49)
The variance identity holds within each group
EXPERIMENTS
C.I EXPERIMENTAL SETUP
Datasets. We the real-world temporal Amazon Review dataset, which contains user reviews (treated implicit interactions) on Amazon products over time.² We focus on three categories: Musical Instruments, Movies & TV, and Books. For Instruments and Movies & TV, we use data from 2019-2023; for Books, we use 2022-2023, We take 60% of the data as pretraining D1 and split the remaining 40% into four equal incremental stages, D....., Ds. For each incremental stage, we filter out users with fewer than five interactions. This ensures leave-one-our evaluation is feasible and makes incremental data even smaller than pretraining data, simulating real-world scenarios. Table summarizes dataset statistics, including the number of users, items, and interactions at each stage, average sequence length, and sparsity.
hhtpss/aamazoon-rvviee:-2222...3 github.io
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Pretrain
Single Evolving LoRA
Instruments
0.0180
0.0160
0.0140
0.0120
1r
=r5
1r*
0.05
1r*00.01
Irt
Learning rate continual stage
Figure 4: Impact of the learning rate for continual data model performance.
Evaluation. For each Dr, we apply leave-one-out per user: the second-to-last item used for validation and the last item is reserved for testing. Following prior work (Wang et al., 2024; Bao et al., 2025a), we construct multiple training pairs (Ia, yo) per user u using a sliding window of size 20. The LLM trained on D serves the pretrained model for all compared methods. At each stage autoregressively generates 10 items given the user history in the test pair. Generation uses constrained beam search restricted to valid item tokens, making it efficient and widely adopted in prior work (Wang al., 2024; Rajput et al., 2023). With these 10 items, we evaluate against the ground-truth item and report @it@5, @it@ 10, NDCG@5, and NDCG@ 10, averaged over D2,.... D4.
2, 5, after fine-tuning, the LLM
Metrics. Hit@k l: measures whether the ground-truth item appears among the generated items. For a user with ground-truth item yv, and ranked list of predictions Ra.
1 if yo R, k).
Hit@k(u)=
0 otherwise.
NDCG@k & (Normalized Discounted Cumulative Gain) additionally accounts for the position of the ground- truth item, giving higher credit when it appears closer to the top:
loga (rank())))) if yo E R,, k).
NDCG@K(W)
otherwise,
Hit@ & captures whether the correct item is recommended at all, while NDCG@ rewards ranking it higher in the list. We report averages of Hit@ & and NDCG@ l across all users, with i E 5, 10.
C.2 LEARNING RATE FOR CONTINUAL STAGES
Incremental blocks are much smaller than the pretraining set Dj (see Appendix C.I), making performance sensitive the learning rate. Figure 4 reports results for Single Evolving LoRA under different learning rates on incremental blocks. Using the pretraining rate (0.0002; 1 I - -1.0) performs worse than not updating on new data, likely due to overfitting. The best performance is achieved with =0.005 or =r000.1, which aligns with the relative block size |Dal/|Da x 0.1. This suggests that learning rates for incremental blocks should be scaled with respect to data size.
C.3 COMPARISON WITH TRADITIONAL CONTINUAL RECOMMENDER SYSTEMS
We compare LLM--bsed methods (pretrain, single evolving LoRA, and PESO) against two-tower meth- ods with LightGCN (He et al., 2020) as backbone, including Pretrain, Fine-tuning, Contrastive (Wang et al., 2021), Po (Wang et al., 2023b), and PISA (Yoo et al., 2025a). Two-tower models use explicit
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Table 6: Comparison of LLM-based and traditional methods continual recommendation.
Instruments
Movies & TVs
Books
Pretrain
0.0153
0.0028
0.0041
Fine-tuning
0.0180
0,0114
0.0218
Traditional two-tower
Contrastive
0.0177
0.0101
0.0272
Contrastive PIW
0.0193
0.0113
0,0243
PISA
0.0194
0.0106
0.0301
Pretrain Fine-tuning (w/ LoRA) PESO
0.0157
0.0160
0,0235
LLM-based
0.0178
0.0169
0.0414
0.0190
0.0173
0.0422
user and item embeddings, and their continual methods mitigate forgetting by regularizing user embeddings against past versions: Contrastive maximizes mutual information between past and current embeddings, Contrastive+PIW further adapts the regularization weights per user, and PISA combines stability and plas- ticity regularization.
Table 6 reports results averaged across time stages and metrics. First, LLM-based recommenders (both pretrain and continual) generally outperform traditional methods, highlighting the generalization ability and knowledge transfer benefits of LLMs. On Instruments, however, the performance gap is smaller, suggesting that explicit dual modeling of users and items still provides benefits for capturing collaborative signals. worth noting that there also remains considerable headroom for L..M-based models if larger beam sizes used during generation.
Second, While PESO outperforms traditional continual methods in absolute terms, the relative gains of continual techniques over their respective pretraining baselines are larger in traditional settings. This is likely because two-tower methods explicitly capture preference shifts through user embeddings, supporting our view that modeling user preference drift is crucial in continual recommendation. At the same time, it underscores the difficulty capturing such dynamics LLM-based methods, pointing to an important direction for future research.
C.4 QUANTIFICATION OF DISTRIBUTION SHIFT
To validate the realism of our continual learning formulation, explicitly quantified the user preference drift between data blocks on the Instruments dataset. We employed a domain discrimination approach:
1. We embed user interaction sequences into fixed-dimensional vectors using pretrained codebooks.
2. For each pair of blocks (t - 1) and (t), we train a binary logistic regression classifier to distinguish samples from the two blocks.
3. We compute the Drift Score ö(t 1,t) 2(AUC 0.5) [0, 1], where 0 implies identical distributions and implies completely separable distributions.
Table 7 reports both the step- dise drift 6(t - 1,t) and the cumulative drift from the base block 8(0, t). The results show non-trivial step-wise drift and, erucially, steady increase in cumulative drift (reaching 0.457 at f = 4). This confirms that user preferences structurally evolve away from the initial state, validating our experimental setup.
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Table Quantification of distribution shift (Drift Score) on the Instruments dataset.
Measure
t=
2
3
4
Step-wise 6t 1.t)
0.200
0.060
0.240
0.090
Cumulative 5(0,
0.200
0.311
0.342
0.457
C.5 COMPARISON WITH ADDITIONAL BASELINES
C.5.1 COMPARISON WITH STANDARD TRAINING PARADIGMS (FULL-PARAMETER FINE-TUNING & RETRAINING)
To validate the effectiveness of our LoRA-basee sequential fine-tuning approach, we compared it against two traditional training paradigms:
1. Full-Parameter Fine- Tannnnnnng:: Updating all L.M parameters sequentially on new data blocks.
2. Retraining (with LoRA): At each stage t, we restart from the same pretrained LLM (i.e., without warm-starting from stage t 1-1) and fine-tune it on the cumulative dataset (D1 U U... U,).
As shown in Table 8, Single Evolving LoRA consistently outperforms both approaches.
Full- pallmeerrmeter updates suffer from dilemma: high learning rates (2e-5) 5) lead to catastrophic forgetting, while low rates (2e-6) result in insufficient adaptation. LoRA acts structural regularizer, mitigating forgetting while enabling effective adaptation.
Full-Parameter Fine-Tuning:
vs. Retraining: While full retraining outperforms static pretraining. underperforms sequential fine-tuning. This aligns with prior work (Yoo et al., 2025a), suggesting that sequential updates naturally prioritize recent preference signals, whereas full retraining treats old and new data equally, diluting the signal of evolving interests.
Table 8: Comparison with Standard Training Paradigms (Full Fine-Tuning and Full Retraining).
Method
Hit@5
@it@ 10
NDCG@5
NDCG@ 10
Pretrain (Static)
0.0166
0,0216
0.0115
0,0131
Full Retraining (Cumulative Data)
0.0170
0,0231
0.0121
0,0141
Full Fine-Tuming (Ir = 2e-5)
0,0142
0,0228
0.0099
0.0127
Full Fine-Tuning (Ir 2e-6)
0.0171
0.0254
0.0122
0,0149
Single Evolving LoRA (L.oA Fine-Tuning)
0.0181
0.0253
0.0127
0.0150
C.5.2 COMPARISON WITH ADDITIONAL CONTINUAL LoRA METHODS
We compared PESO against O-LoRA (Wang et al., 2023a), AM-LoRA (Liu al., 2024a), and LSAT (Shi al., 2024) the Instrument dataset. O-LoRA and AM-LoRA belong to the cumulative family but orthogonality or attention mechanisms to combine adapters, while LSAT utilizes adapter interpolation. As shown in Table 9, PESO consistently outperforms all of them. This supports claim that explicitly maintaining discrete adapters is less effective for gradual preference drift than our proximal regularization approach.
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Table 9: Comparison with recent Continual PEFT methods on Instruments.
Method
Hit@5
Hit@
NDCG@5
NDCG 10
Single Evolving LoRA
0.0181
0.0253
0.0127
0.0150
Cumulative LoRA
0.0182
0.0260
0.0129
0.0154
O-LoRA
0.0191
0.0259
0,0134
0.0156
AM-LoRA
0.0182
0,0240
0,0125
0.0144
LSAT
0.0164
0.0250
0,0117
0.0144
LSAT (+ Param Inheritance)
0.0183
0.0254
0.0130
0.0153
PESO
0.0193
0.0268
0.0138
0.0162
C.6 PERFORMANCE ON LC-REC BACKBONE
To demonstrate robustness across architectures, we evaluated PESO using the LC-REC backbone (Zheng al., 2024). As shown in Table 10, PESO maintains its superiority over baselines.
Table Performance comparison using the LC-REC backbone.
Method
Hit@5
Hit@ 10
NDCG@5
NDCG@10
Single Evolving LoRA
0.0164
0.0249
0.0119
0.0146
Cumulative LoRA
0,0178
0.0249
0.0122
0.0145
SD-LoRA
0.0185
0.0256
0.0127
0.0150
PESO
0.0179
0.0266
0.0130
0.0158
C.7 PERFORMANCE ON NON-E-COMMERCE DATASET (YELP)
To further explore non-e-commerce domains, we evaluated PESO on the Yelp dataset, where interactions correspond user check-ins at locations, using the same data-splittnng strategy as our main experiments. As shown in Table 11, PESO consistently outperforms strong competitors, including Single Evolving LoRA and SD-LoRA.
This result is particularly notable given that, unlike Amazon products which feature detailed textual descrip- tions, Yelp locations often lack deep semantic content (consisting primarily of names like "Pizza Hut" or coarse categories like "Pizza" or "Restaurant"). This demonstrates the robustness of our method, showing it remains highly effective even in settings with limited semantic richness.
Table 11: Performance comparison on the Yelp dataset.
Methods
Hit@5
Hit@ 10
NDCG@5
NDCG@
Pretrain
0.0201
0.0309
0.0126
0.0161
Single Evolving LoRA
0.0290
0.0442
0.0190
0.0239
SD-LoRA
0,0279
0.0432
0,0168
0,0230
PESO
0.0302
0.0454
0.0199
0.0248
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Published as conference paper ICLR 2026
C.8 EXPLICIT MEASUREMENT OF FORGETTING
We measured the performance drop on past blocks to analyze forgetting behavior. Table 12 shows the difference between the final model's performance on Dr and its initial performance at time t. While PESO shows selective forgetting intermediate blocks (allowing to shed obsolete trends), it achieves the highest overall performance and best retrieval for dormant users, indicating that this forgetting is benign and adaptive rather than catastrophic.
Table 12: Performance drop past blocks (lower is strictly less forgetting, but may imply rigidity).
Method
Drop on Du
Drop on D₃
Drop on D2
Drop on D3
Single Evolving LoRA
0.0062 0.0060 0.0062
0.0087
0.0042
0.0031
Cumulative LoRA
0.0062
0.0035
0.0060
PESO
0.0107
0.0048
0.0045
D EFFICIENCY ANALYSIS
PESO introduces negligible overhead compared to baselines:
Storage Complexity: PESO stores only one previous LoRA adapter, resulting in O(1) storage complexity relative to the number of stages. In contrast, Cumulative LoRA grows linearly O(T) as must store all past adapters.
Computational Complexity: PESO adds only a lightweight quadratic/KL penalty to the loss. This requires no additional forward passes. In practice, we observed no measurable slowdown in training time compared to standard Single LoRA fine-tuning.
E DISCUSSION ON PROMPT TUNING VS. LORA
Prompt-tunine based PEFT methods typically learn prompt pool and dynamically retrieve the most rele- vant prompts for each input, inserting them into the input or intermediate representations without updating backbone weights (Wang et al., 2022) This introduces inference overhead because the model must com- pute query features and perform similarity matching over a growing prompt pool at inference time. The inference-inefffiiennyy
more severe in generative recommendation setting: autoregressive gen- eration requires many forward passes per prediction, and each step would need repeated prompt retrieval. Recent studies in vision (Wu et al., 2025; Liang & Li, 2024) also report that LoRA- -aRe- methods generally outperform poompt-based approaches large-scale tasks, making LoRA the preferred PEFT technique.
ADDITIONAL RELATED WORK
modern machine learning research including recommender systems, personalized LLM agents, and agentic memory (Wei et al., 2025b; 2026; Bei et al., 2026; Ning et al.; Liu et al., 2025b;e;d; 2024b; Li al., 2025b;e; Lin et al., 2024a; Qiu et al., 2025a;b;c; He et al., 2026; Lee et al., 2025a; Xu et al., 2024) temporal adaptation under distribution shift (Zeng al., 2026b) has been extensively studied. Our work focuses on parameter-efffcieet tuning and regularization/ddiiilltttoo to mitigate forgetting and lock-in in continual recommendation settings. It complements this line of research by maintaining single evolving LoRA adapter and applying a proximal regularizer that provides data-aware control of the cability-plasticee trade-off.
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Published as conference paper ICLR 2026
G PROMPT
We show below the template used in all experiments. Notably, <a. [b1]><b_[j1]><c [[1]> represents one user-item interaction encoded as four semantic-ID tokens. For instance, is such tuple describing single interacted item (Rajput
et al., 2023; Wang et al., 2024).
Below is instruction that describes task. Write a response that appropriately completes the request. An\n
### Instruction:la
Based the items that the has interacted with: "..11>>>e_11DDxxx\\11>>111111113 [a__12)>=e_[12pp>___]]}244181
{
can you determine what item would recommended the next?\nkn
### Response:
H USE OF LARGE LANGUAGE MODELS
LLMs used only for writing polish (grammar and clarity). All content reviewed and approved by the authors. LLMs did not contribute to research ideation, algorithm design, implementation, or analysis.
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Reviews:
[{"review_id": "cdZMYB9yqi", "reviewer": "Reviewer_PErP", "summary": "This paper studies continual learning in LLM-based generative recommender systems. The authors argue that existing LoRA-based continual learning methods emphasize preserving past performance but overlook that recommendation requires modeling evolving user preferences rather than retaining outdated ones. To address this, they propose PESO, which maintains a single evolving LoRA adapter regularized toward its previous state. Theoretically, the authors show that the proximal term provides data-aware, direction-wise guidance in the LoRA subspace. Empirical results on Amazon datasets demonstrate consistent improvement over existing LoRA-based continual learning methods.", "questions": "1. What are the advantages of using LoRA compared to other parameter-efficient fine-tuning methods, such as prompt tuning?\n\n2. Can the authors elaborate on the intuition of how data-aware, direction-wise guidance in the LoRA subspace helps balance long-term and evolving user preferences?\n\n3. How would the method perform in non-e-commerce or multi-domain continual recommendation scenarios?\n\n4. Is there any case studies on how the proposed method balances the long-term interests and current evolved user preferences?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper tackles continual learning in recommendation, a fundamental and practical problem.\n\n2. The motivation is clear, with comprehensive analysis of single vs. cumulative LoRA and their limitations in the recommendation setting.\n\n3. The theoretical justification for the proximal regularization providing data-aware, direction-wise guidance in the LoRA subspace is well presented.\n\n4. Experiments are systematic and demonstrate consistent gains over baselines.", "weaknesses": "1. The motivation for using LoRA as the primary PEFT technique is not fully convincing. Comparison or discussion with alternatives such as prompt tuning or layer pruning is missing.\n\n2. The paper lacks discussion and comparison with recent works on continual or incremental learning for LLM-based generative recommendation [1].\n\n3. The experimental datasets lack diversity, all drawn from the Amazon Review corpus in the e-commerce domain, limiting the generalization of conclusions.\n\n4. While the theoretical formulation is elegant, the intuitive explanation of how direction-wise guidance relates to long-term versus short-term preference adaptation could be elaborated.\n\n[1] Shi et al., Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems. CIKM'24.", "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": "vJhfYgeaGw", "reviewer": "Reviewer_4gyJ", "summary": "This paper introduces PESO, a new method for continually adapting LLM-based recommender systems to evolving user preferences. Traditional methods struggle by either forgetting past preferences or rigidly holding on to outdated ones. PESO addresses this by maintaining a single evolving LoRA adapter and using a proximal regularizer to anchor it to its most recent state. This design allows the model to flexibly balance adapting to new interests (plasticity) with preserving long-term preferences (stability), enabling it to better capture the dynamic nature of user behavior.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written and easy to follow, with a solid theoretical analysis for the proposed PESO.\n2. The choice of a modern, semantic ID-based generative recommendation as the experimental backbone is highly relevant and makes the results more convincing.", "weaknesses": "1. A key weakness is the practical relevance of the problem formulation. The paper partitions data into discrete, chronological blocks to simulate a continual learning scenario. However, in real-world RS that are updated frequently (often in near real-time), the data distribution shift between consecutive updates is typically small and gradual. The paper fails to quantify the distribution shift in its experimental data splits, making it unclear if the problem it solves reflects realistic deployment conditions.\n2. In Equations 6 and 12, could you provide more details on how the parameters of LoRA are partitioned into groups, and how the number of groups G impacts the performance of PESO?\n3. The experimental results in Table 2 are missing a comparison to a standard full fine-tuning baseline.", "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": "UWP7bh1H1F", "reviewer": "Reviewer_VejX", "summary": "The paper proposes CLoRA (Continual Low-Rank Adaptation), a novel method designed to enable continual learning for large pre-trained models without retraining or catastrophic forgetting. Traditional fine-tuning methods require storing large model checkpoints for each new task, leading to significant storage and computation overhead. CLoRA builds upon LoRA (Low-Rank Adaptation) by introducing a continual learning mechanism that efficiently integrates knowledge from new tasks while retaining performance on previously learned ones.\n\nThe key idea is to dynamically allocate and merge low-rank adapters for each new task through orthogonal subspace projection, which minimizes interference between tasks. Additionally, CLoRA introduces a knowledge preservation constraint that stabilizes updates and ensures consistent performance across tasks. Extensive experiments on NLP benchmarks (e.g., GLUE, SuperGLUE, and continual text classification datasets) show that CLoRA achieves competitive or superior performance compared to existing continual learning and parameter-efficient fine-tuning methods, all while maintaining low storage costs.", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Novel problem framing:** The paper clearly articulates how continual recommendation differs from general continual learning, emphasizing the role of evolving user preferences instead of task retention. This perspective grounds the LLM-based recommendersa in realistic recommender settings.\n\n2. **Simple but Effective:** PESO avoids multiple adapters (reducing storage and interference) and introduces a lightweight proximal term that yields theoretically grounded, data-aware stability—achieving simplicity without sacrificing effectiveness.\n\n3. **theoretical foundation:** The paper provides formal analysis linking proximal regularization to direction-wise interpolation in the LoRA subspace, offering mathematical clarity about why PESO balances stability and plasticity.", "weaknesses": "1. **Limited modeling of long-term preference dynamics：** PESO is evaluated on short-term chronological splits (four-stage Amazon data). How would it behave under nonlinear or cyclical preference drifts over long horizons? Would the single-step proximal constraint still be sufficient, or would multi-timescale or memory-based mechanisms be required?\n\n2. **Unanalyzed efficiency trade-offs.** PESO maintains previous adapter states for proximal computation. What is the actual storage and computational overhead compared with single or cumulative LoRA?\n\n3. **No explicit measurement of catastrophic forgetting:** The study reports only global Hit@K and NDCG metrics. Without tracking performance decay on past data (e.g., forgetting ratio), it is hard to disentangle whether PESO’s gains come from genuine stability or from overfitting recent tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TwlpJfWy7l", "reviewer": "Reviewer_qc7d", "summary": "This paper has focused on the continual learning in LLM-based recommender systems. The authors found that existing cumulative-LoRA-based methods cannot capture the preferences well contained in frozen past adapters. Besides, they face the challenges of growing storage costs. To address these issues, this paper facilitates the continual learning methods specified for LLM-based RS, including a proximal regularizer. The extensive experiments have validated the effectiveness of the proposed method.", "questions": "All my questions have been included in the weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ S1. This paper is well-organized and -written, making it easy to follow.\n+ S2. Extensive experiments have been conducted.\n+ S3. The code is released, making it easy to reproduce.", "weaknesses": "- W1. More illustration of the importance of continual learning in recommender systems is needed. In general, the models used in industry are retrained periodically.\n- W2. The authors have claimed that existing cumulative LoRA methods face the challenge of adapter entanglement for the recommendation tasks. However, it seems that not all PEFT methods in continual learning belong to cumulative LoRA, such as O-LoRA [1] and AM-LoRA [2]. Do they face the same challenges as the cumulative LoRA methods?\n- W3. The performance of the model training on the full dataset should be revealed to show the merit of continual learning.\n- W4. Only one basic LLM-based recommendation model is experimented with in this paper. I suggest that the authors add more up-to-date LLM-based RS models, such as LLaRA and BIGRec, to further validate the robustness of the proposed method.\n\n\n\n[1]. Wang, Xiao, et al. \"Orthogonal Subspace Learning for Language Model Continual Learning.\" *The 2023 Conference on Empirical Methods in Natural Language Processing*.\n\n\n\n[2]. Liu, Jialin, et al. \"Learning attentional mixture of loras for language model continual learning.\" *arXiv preprint [arXiv:2409.19611](https://arxiv.org/abs/2409.19611)* (2024).\n\n\n\n[3]. Liao, Jiayi, et al. \"Llara: Large language-recommendation assistant.\" *Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval*. 2024.\n\n\n\n[4]. Bao, Keqin, et al. \"A bi-step grounding paradigm for large language models in recommendation systems.\" *ACM Transactions on Recommender Systems* 3.4 (2025): 1-27.", "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)