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arxiv: 2606.02606 · v1 · pith:NUOM6KBEnew · submitted 2026-05-23 · 💻 cs.LG · cs.AI

ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services

Yang Xu , Zihuai Xu , Hongli Xu , Yunming Liao , Zhiwei Yao , Xitong Fu This is my paper

Pith reviewed 2026-06-30 15:02 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords LoRA adaptationLLM servicesknowledge reuseBayesian optimizationscheduled regularizationmodel evolutiontask adaptersre-adaptation
0
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The pith

ReLoRA restores task LoRA adapters after base LLM updates by fusing prior adapter knowledge into a Bayesian-optimized starting point and then applying scheduled regularization during fine-tuning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to show that providers of LLM-based services can avoid full retraining of every task adapter each time the base model changes. It claims this is possible through a two-step process that first builds a better initial adapter by blending the old adapter with signals from the model update, then fine-tunes under a regularization schedule that tightens early and loosens later. A sympathetic reader would care because repeated full retraining is too slow and expensive when many downstream services must stay current, while simply reusing the old adapter often breaks performance due to mismatch with the new base. If the method works, service rollout after base-model releases becomes feasible at much lower cost and with comparable or better accuracy on the original tasks.

Core claim

ReLoRA comprises two key optimization steps: Adaptive LoRA initialization leverages Bayesian optimization to construct a compatibility-aware starting point by fusing information from both the previously deployed task adapter and the base model's evolution; Fine-tuning with scheduled regularization first rapidly steers the adapter to a high-quality region via strong regularization, followed by relaxed regularization for task-specific refinement. This design enables rapid service-quality recovery with reduced re-adaptation overhead.

What carries the argument

The adaptive initialization step that uses Bayesian optimization to fuse the old task adapter with base-model evolution information, together with the subsequent scheduled-regularization fine-tuning phase.

If this is right

  • ReLoRA reduces time-to-readiness by up to 8.9 times compared with training each adapter from scratch after a base-model update.
  • Task accuracy improves by up to 4.6 percent over standard baselines while the adapter reaches service quality.
  • Service providers managing many downstream LoRA services incur substantially lower re-adaptation compute when base models evolve.
  • Task performance is preserved or improved rather than degraded by simple reuse of an incompatible old adapter.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same initialization-plus-scheduling pattern might transfer to other parameter-efficient methods that must track base-model changes.
  • Frequent base-model releases could become practical in production pipelines if the overhead of adapter refresh drops this sharply.
  • The approach still requires the service provider to retain the previous adapter weights, which may not hold in every deployment scenario.

Load-bearing premise

Bayesian optimization can reliably produce a useful starting adapter by combining the old task adapter with information about how the base model has changed.

What would settle it

An experiment in which the Bayesian-optimization initialization is replaced by a naive copy of the old adapter and the time to reach target accuracy exceeds the claimed reduction relative to training from scratch would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.02606 by Hongli Xu, Xitong Fu, Yang Xu, Yunming Liao, Zhiwei Yao, Zihuai Xu.

Figure 1
Figure 1. Figure 1: Workflow of ReLoRA for fast rollout of evolving LLM [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The results of preliminary experiments. (a) Performance variation when applying the previously fine-tuned LoRA [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Time-to-readiness of different methods to reach the [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Time-to-readiness under different update sources and [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
read the original abstract

Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Rank Adaptation (LoRA) adapters. For service providers managing numerous downstream model services, retraining each LoRA adapter from scratch for every updated base model is computationally prohibitive and delays service rollout. Meanwhile, the simpler alternative, i.e., naively applying the original LoRA adapter to the updated base model, often leads to degraded service quality due to adapter-backbone incompatibility. To address this problem, we propose ReLoRA, a knowledge-reusing re-adaptation framework that efficiently restores service-ready LoRA adapters for evolving LLM services while preserving or improving task performance. Specifically, ReLoRA comprises two key optimization steps: 1) Adaptive LoRA initialization leverages Bayesian optimization to construct a compatibility-aware starting point by fusing information from both the previously deployed task adapter and the base model's evolution; 2) Fine-tuning with scheduled regularization first rapidly steers the adapter to a high-quality region via strong regularization, followed by relaxed regularization for task-specific refinement. This design enables rapid service-quality recovery with reduced re-adaptation overhead. Extensive experiments demonstrate that ReLoRA reduces time-to-readiness by up to 8.9$\times$ and improves accuracy by up to 4.6\% compared to baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes ReLoRA, a re-adaptation framework for LoRA adapters when base LLMs are updated in deployed services. It consists of (1) Adaptive LoRA initialization via Bayesian optimization to fuse the prior task adapter with base-model evolution information into a compatibility-aware starting point, and (2) fine-tuning under scheduled regularization (strong then relaxed) for rapid steering to high-quality regions. The central empirical claim is that this yields up to 8.9× reduction in time-to-readiness and up to 4.6% accuracy gains versus baselines for evolving LLM services.

Significance. If the reported speedups and accuracy improvements hold under rigorous controls, the work addresses a practically important engineering problem for providers managing many downstream LoRA services on frequently updated base models. The combination of knowledge reuse via BO initialization and scheduled regularization could reduce re-training overhead in production settings, provided the method generalizes beyond the evaluated cases.

major comments (2)
  1. [Adaptive LoRA initialization (method description)] The central claim of 8.9× time-to-readiness reduction rests on the Adaptive LoRA initialization step producing a superior starting point via Bayesian optimization. However, neither the abstract nor the method description supplies the compatibility objective, the search space over LoRA parameters, the acquisition function, or the number of BO evaluations performed. Without these, it is impossible to determine whether the step is low-overhead and reliable or merely adds compute that offsets later savings.
  2. [Experiments / results] Table or experimental results section: quantitative claims of 8.9× speedup and 4.6% accuracy improvement are presented without accompanying details on experimental setup, baseline implementations (including how naive transfer and from-scratch training were realized), statistical significance tests, number of runs, or data exclusion criteria. This absence prevents verification that the data support the cross-method comparison.
minor comments (1)
  1. [Abstract] The abstract states gains 'compared to baselines' but does not name the baselines; this should be clarified in the abstract for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments highlighting the need for greater methodological and experimental transparency. We will revise the manuscript to incorporate the requested details, which will strengthen the verifiability of our claims without altering the core contributions.

read point-by-point responses

  1. Referee: [Adaptive LoRA initialization (method description)] The central claim of 8.9× time-to-readiness reduction rests on the Adaptive LoRA initialization step producing a superior starting point via Bayesian optimization. However, neither the abstract nor the method description supplies the compatibility objective, the search space over LoRA parameters, the acquisition function, or the number of BO evaluations performed. Without these, it is impossible to determine whether the step is low-overhead and reliable or merely adds compute that offsets later savings.

    Authors: We agree that the method section currently lacks explicit specification of the Bayesian optimization components. In the revised manuscript we will add a dedicated subsection detailing the compatibility objective (a weighted combination of task performance on held-out data and parameter-space distance to the prior adapter), the search space (low-rank updates constrained to the delta between old and new base-model weights), the acquisition function (Expected Improvement), and the evaluation budget (30 iterations per task). These additions will confirm that the initialization overhead remains negligible relative to the reported fine-tuning savings. revision: yes

  2. Referee: [Experiments / results] Table or experimental results section: quantitative claims of 8.9× speedup and 4.6% accuracy improvement are presented without accompanying details on experimental setup, baseline implementations (including how naive transfer and from-scratch training were realized), statistical significance tests, number of runs, or data exclusion criteria. This absence prevents verification that the data support the cross-method comparison.

    Authors: We concur that the experimental reporting is insufficiently detailed. The revision will include an expanded experimental setup subsection specifying: (i) baseline implementations (naive transfer applies the original adapter directly; from-scratch uses standard LoRA training with identical hyperparameters), (ii) five independent random seeds per configuration, (iii) paired t-tests for significance (p < 0.05 reported), and (iv) no data exclusion. Updated tables will reference these controls. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical method is self-contained

full rationale

The paper proposes ReLoRA as an applied engineering technique with two optimization steps (Bayesian optimization for adaptive initialization and scheduled regularization for fine-tuning), validated through experiments showing time and accuracy gains. No equations, derivations, or self-citations are shown that reduce the claimed results to fitted inputs or prior author work by construction. The contribution is framed as empirical rather than a closed-form prediction, leaving the derivation chain independent of the patterns that would indicate circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated beyond the high-level description of the two optimization steps.

pith-pipeline@v0.9.1-grok · 5787 in / 1190 out tokens · 65173 ms · 2026-06-30T15:02:58.505705+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

44 extracted references · 26 canonical work pages · 13 internal anchors

  1. [1]

    A brief overview of chatgpt: The history, status quo and potential future development,

    T. Wu, S. He, J. Liu, S. Sun, K. Liu, Q.-L. Han, and Y. Tang, “A brief overview of chatgpt: The history, status quo and potential future development,”IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 5, pp. 1122–1136, 2023. IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL., NO., MAY . 2026 12

    2023

  2. [2]

    Llama 2: Open Foundation and Fine-Tuned Chat Models

    H. Touvron, L. Martin, K. Stone, P . Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P . Bhargava, S. Bhosaleet al., “Llama 2: Open foundation and fine-tuned chat models,”arXiv preprint arXiv:2307.09288, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  3. [3]

    A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt,

    Y. Cao, S. Li, Y. Liu, Z. Yan, Y. Dai, P . S. Yu, and L. Sun, “A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt,”arXiv preprint arXiv:2303.04226, 2023

    work page arXiv 2023

  4. [4]

    LoRA: Low-Rank Adaptation of Large Language Models

    E. J. Hu, Y. Shen, P . Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,”arXiv preprint arXiv:2106.09685, 2021

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  5. [5]

    Gemini: A Family of Highly Capable Multimodal Models

    G. Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millicanet al., “Gemini: a family of highly capable multimodal models,”arXiv preprint arXiv:2312.11805, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  6. [6]

    Amazon sagemaker,

    AWS, “Amazon sagemaker,” https://aws.amazon.com/cn/ sagemaker/

  7. [7]

    Together AI,

    “Together AI,” https://www.together.ai, 2023, accessed: 2023-10- 15

    2023

  8. [8]

    Qlora: Efficient finetuning of quantized llms,

    T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer, “Qlora: Efficient finetuning of quantized llms,”Advances in neural informa- tion processing systems, vol. 36, pp. 10 088–10 115, 2023

    2023

  9. [9]

    {Cost-Efficient}large language model serving for multi-turn conversations with{CachedAttention},

    B. Gao, Z. He, P . Sharma, Q. Kang, D. Jevdjic, J. Deng, X. Yang, Z. Yu, and P . Zuo, “{Cost-Efficient}large language model serving for multi-turn conversations with{CachedAttention},” in2024 USENIX Annual Technical Conference (USENIX ATC 24), 2024, pp. 111–126

    2024

  10. [10]

    Amazon ec2 pricing,

    AWS, “Amazon ec2 pricing,” https://aws.amazon.com/ec2/ pricing/

  11. [11]

    Portllm: Personalizing evolving large language models with training-free and portable model patches,

    R. M. S. Khan, P . Li, S. Yun, Z. Wang, S. Nirjon, C.-W. Wong, and T. Chen, “Portllm: Personalizing evolving large language models with training-free and portable model patches,”arXiv preprint arXiv:2410.10870, 2024

    work page arXiv 2024

  12. [12]

    Oral: Prompting your large-scale loras via conditional recurrent diffu- sion,

    R. M. S. Khan, D. Tang, P . Li, K. Wang, and T. Chen, “Oral: Prompting your large-scale loras via conditional recurrent diffu- sion,”arXiv preprint arXiv:2503.24354, 2025

    work page arXiv 2025

  13. [13]

    The Llama 3 Herd of Models

    A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al- Dahle, A. Letman, A. Mathur, A. Schelten, A. Vaughanet al., “The llama 3 herd of models,”arXiv preprint arXiv:2407.21783, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  14. [14]

    Recyclable tuning for continual pre-training,

    Y. Qin, C. Qian, X. Han, Y. Lin, H. Wang, R. Xie, Z. Liu, M. Sun, and J. Zhou, “Recyclable tuning for continual pre-training,”arXiv preprint arXiv:2305.08702, 2023

    work page arXiv 2023

  15. [15]

    Elle: Efficient lifelong pre-training for emerging data,

    Y. Qin, J. Zhang, Y. Lin, Z. Liu, P . Li, M. Sun, and J. Zhou, “Elle: Efficient lifelong pre-training for emerging data,”arXiv preprint arXiv:2203.06311, 2022

    work page arXiv 2022

  16. [16]

    S2orc: The semantic scholar open research corpus,

    K. Lo, L. L. Wang, M. Neumann, R. Kinney, and D. S. Weld, “S2orc: The semantic scholar open research corpus,”arXiv preprint arXiv:1911.02782, 2019

    work page arXiv 1911

  17. [17]

    Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,

    R. He and J. McAuley, “Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,” inproceedings of the 25th international conference on world wide web, 2016, pp. 507–517

    2016

  18. [18]

    Defending against neural fake news,

    R. Zellers, A. Holtzman, H. Rashkin, Y. Bisk, A. Farhadi, F. Roes- ner, and Y. Choi, “Defending against neural fake news,”Advances in neural information processing systems, vol. 32, 2019

    2019

  19. [19]

    Measuring Massive Multitask Language Understanding

    D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language under- standing,”arXiv preprint arXiv:2009.03300, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  20. [20]

    Don’t stop pretraining: Adapt language models to domains and tasks,

    S. Gururangan, A. Marasovi ´c, S. Swayamdipta, K. Lo, I. Belt- agy, D. Downey, and N. A. Smith, “Don’t stop pretraining: Adapt language models to domains and tasks,”arXiv preprint arXiv:2004.10964, 2020

    work page arXiv 2004

  21. [21]

    A probabilistic analysis of the rocchio algorithm with tfidf for text categorization,

    T. Joachimset al., “A probabilistic analysis of the rocchio algorithm with tfidf for text categorization,” inICML, vol. 97. Citeseer, 1997, pp. 143–151

    1997

  22. [22]

    Teknium”, “Openorca: An open dataset of gpt augmented flan reasoning traces,

    W. Lian, B. Goodson, E. Pentland, A. Cook, C. Vong, and “Teknium”, “Openorca: An open dataset of gpt augmented flan reasoning traces,” 2023

    2023

  23. [23]

    A Tutorial on Bayesian Optimization

    P . I. Frazier, “A tutorial on bayesian optimization,”arXiv preprint arXiv:1807.02811, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  24. [24]

    Checkpoint merging via bayesian optimization in llm pretraining,

    D. Liu, Z. Wang, B. Wang, W. Chen, C. Li, Z. Tu, D. Chu, B. Li, and D. Sui, “Checkpoint merging via bayesian optimization in llm pretraining,”arXiv preprint arXiv:2403.19390, 2024

    work page arXiv 2024

  25. [25]

    Gaussian processes for machine learning,

    M. Seeger, “Gaussian processes for machine learning,”Interna- tional journal of neural systems, vol. 14, no. 02, pp. 69–106, 2004

    2004

  26. [26]

    SGDR: Stochastic Gradient Descent with Warm Restarts

    I. Loshchilov and F. Hutter, “Sgdr: Stochastic gradient descent with warm restarts,”arXiv preprint arXiv:1608.03983, 2016

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  27. [27]

    D. S. Chaplot, “Albert q. jiang, alexandre sablayrolles, arthur men- sch, chris bamford, devendra singh chaplot, diego de las casas, florian bressand, gianna lengyel, guillaume lample, lucile saulnier, l´elio renard lavaud, marie-anne lachaux, pierre stock, teven le scao, thibaut lavril, thomas wang, timoth ´ee lacroix, william el sayed,”arXiv preprint ar...

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  28. [28]

    GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

    A. Wang, “Glue: A multi-task benchmark and analysis plat- form for natural language understanding,”arXiv preprint arXiv:1804.07461, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  29. [29]

    Character-level convolutional networks for text classification,

    X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,”Advances in neural information processing systems, vol. 28, 2015

    2015

  30. [30]

    A large annotated corpus for learning natural language inference

    S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning, “A large annotated corpus for learning natural language inference,”arXiv preprint arXiv:1508.05326, 2015

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  31. [31]

    Instruction Tuning with GPT-4

    B. Peng, C. Li, P . He, M. Galley, and J. Gao, “Instruction tuning with gpt-4,”arXiv preprint arXiv:2304.03277, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  32. [32]

    Platypus: Quick, cheap, and powerful refinement of llms,

    A. N. Lee, C. J. Hunter, and N. Ruiz, “Platypus: Quick, cheap, and powerful refinement of llms,”arXiv preprint arXiv:2308.07317, 2023

    work page arXiv 2023

  33. [33]

    Fedpetuning: When federated learning meets the parameter- efficient tuning methods of pre-trained language models,

    Z. Zhang, Y. Yang, Y. Dai, Q. Wang, Y. Yu, L. Qu, and Z. Xu, “Fedpetuning: When federated learning meets the parameter- efficient tuning methods of pre-trained language models,” in Annual Meeting of the Association of Computational Linguistics 2023. Association for Computational Linguistics (ACL), 2023, pp. 9963– 9977

    2023

  34. [34]

    Ferrari: A personalized federated learning framework for heterogeneous edge clients,

    Z. Yao, J. Liu, H. Xu, L. Wang, C. Qian, and Y. Liao, “Ferrari: A personalized federated learning framework for heterogeneous edge clients,”IEEE Transactions on Mobile Computing, 2024

    2024

  35. [35]

    Dora: Weight-decomposed low-rank adaptation,

    S.-Y. Liu, C.-Y. Wang, H. Yin, P . Molchanov, Y.-C. F. Wang, K.- T. Cheng, and M.-H. Chen, “Dora: Weight-decomposed low-rank adaptation,” inForty-first International Conference on Machine Learn- ing, 2024

    2024

  36. [36]

    Scaling down to scale up: A guide to parameter-efficient fine-tuning,

    V . Lialin, V . Deshpande, and A. Rumshisky, “Scaling down to scale up: A guide to parameter-efficient fine-tuning,”arXiv preprint arXiv:2303.15647, 2023

    work page arXiv 2023

  37. [37]

    Parameter- efficient transfer learning for nlp,

    N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Larous- silhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter- efficient transfer learning for nlp,” inInternational conference on machine learning. PMLR, 2019, pp. 2790–2799

    2019

  38. [38]

    Conditional lora parameter generation,

    X. Jin, K. Wang, D. Tang, W. Zhao, Y. Zhou, J. Tang, and Y. You, “Conditional lora parameter generation,”arXiv preprint arXiv:2408.01415, 2024

    work page arXiv 2024

  39. [39]

    Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

    E. Yang, L. Shen, G. Guo, X. Wang, X. Cao, J. Zhang, and D. Tao, “Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities,”arXiv preprint arXiv:2408.07666, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  40. [40]

    Editing Models with Task Arithmetic

    G. Ilharco, M. T. Ribeiro, M. Wortsman, S. Gururangan, L. Schmidt, H. Hajishirzi, and A. Farhadi, “Editing models with task arith- metic,”arXiv preprint arXiv:2212.04089, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  41. [41]

    Merging models with fisher- weighted averaging,

    M. S. Matena and C. A. Raffel, “Merging models with fisher- weighted averaging,”Advances in Neural Information Processing Systems, vol. 35, pp. 17 703–17 716, 2022

    2022

  42. [42]

    Dataless knowl- edge fusion by merging weights of language models,

    X. Jin, X. Ren, D. Preotiuc-Pietro, and P . Cheng, “Dataless knowl- edge fusion by merging weights of language models,”arXiv preprint arXiv:2212.09849, 2022

    work page arXiv 2022

  43. [43]

    Adamerging: Adaptive model merging for multi-task learning.arXiv preprint arXiv:2310.02575,

    E. Yang, Z. Wang, L. Shen, S. Liu, G. Guo, X. Wang, and D. Tao, “Adamerging: Adaptive model merging for multi-task learning,” arXiv preprint arXiv:2310.02575, 2023

    work page arXiv 2023

  44. [44]

    Evolutionary op- timization of model merging recipes,

    T. Akiba, M. Shing, Y. Tang, Q. Sun, and D. Ha, “Evolutionary op- timization of model merging recipes,”Nature Machine Intelligence, vol. 7, no. 2, pp. 195–204, 2025

    2025