Replay-Based Continual Learning

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9 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

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Latest papers 82

May 11, 2026cs.CL

Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm

Continual Pre-Training (CPT) is essential for enabling Language Models (LMs) to integrate new knowledge without erasing old. While classical CPT techniques like data replay have become the standard paradigm, the mechanisms underlying how LMs acquire and retain facts over time, termed as continual Factual Knowledge Acquisition (cFKA), remain unclear. In this work, we present a theoretical framework that characterizes the training dynamics of cFKA using a single-layer Transformer, offering a unified explanation for the behavior of representative CPT methods. Our analysis reveals that regularization-based methods merely adjust the convergence rate of parameters without altering the inherent forgetting tendency, whereas data replay methods succeed in shifting convergence dynamics and stabilizing pretrained knowledge. Building on these insights, we propose a novel generative data replay approach, called \textbf{S}electing \textbf{T}okens via attenti\textbf{O}n \textbf{C}ontribution~(STOC), which identifies influential factual snippets to guide replay data generation. Extensive experiments on both synthetic and real-world datasets validate our findings and demonstrate that STOC effectively enhances cFKA by mitigating catastrophic forgetting.

Haoyu Wang, Yifan Shang, Zhongxiang Sun +3

Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China · School of Artificial Intelligence and Data Science, University of International Business and Economics, Beijing, China

Continual Learning for LLMs Generative Replay Continual Learning Replay-Based Continual Learning Continued Pretraining Catastrophic Forgetting

May 11, 2026cs.LG

UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning

Graph learning research has increasingly shifted toward continual graph learning (CGL), which better reflects real-world scenarios where graphs evolve over time. However, existing CGL methods largely assume clean supervision and overlook a critical challenge: the newly arriving portions of the graph are often noisy, due to annotation errors or adversarial corruption. This mismatch limits their applicability in practice. In this work, we study robust continual graph learning, where models must simultaneously handle catastrophic forgetting and noisy supervision in evolving graph data. We show that label noise introduces a new failure mode, catastrophic remembering, where models persistently reinforce corrupted knowledge across tasks. To address these challenges, we propose a Unified Flow-Oriented framework (UFO). First, UFO models conditional feature distributions via flow-based generative modeling and produces replay representations, mitigating forgetting without storing historical data. Second, UFO estimates instance-level reliability scores to distinguish clean from noisy nodes, reducing the impact of corrupted supervision and alleviating catastrophic remembering. Extensive experiments on four benchmark graph datasets under varying noise ratios demonstrate that UFO consistently outperforms existing methods in both accuracy and forgetting metrics. Code is available at: https://anonymous.4open.science/r/UFO.

Danhui Zhang, Zhe Wang, Qing Qing +6

Jilin University · Adelaide University · RMIT University +1

Replay-Based Continual Learning Flow-Based Generative Modeling Learning with Noisy Labels

May 7, 2026cs.CV

Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study

Continual learning (CL) is essential for deploying medical image segmentation models in clinical environments where imaging domains, anatomical targets, and diagnostic tasks evolve over time. However, continual segmentation still faces three main challenges. First, the scenarios for this task remain insufficiently standardized for real-world clinical settings. Second, existing research has been primarily focused on mitigating forgetting, overlooking the other essential properties such as plasticity. Third, a benchmark work with comprehensive evaluation on existing methods is stll desirable. To address these gaps, we present such benchmark study of continual medical image segmentation. We first define three clinically motivated scenarios, namely Domain-CL, Class-CL, and Organ-CL, to respectively capture the cross-center domain shift, the incremental anatomical structure segmentation, and the cross-organ segmentation. We then introduce an evaluation framework that measures not only general performance and forgetting, but also plasticity, forward generalizability, parameter efficiency, and replay burden. The results, from extensive experiments with representative CL methods, showed that it was still challenging to develop a model that could satisfy all the requirements simultaneously. Nevertheless, these studies also suggested that the replay-based methods achieve the best overall balance between stability and plasticity, the parameter-isolation methods should be effective at reducing forgetting, though at the cost of increased model size, and the forward generalizability remain a significantly understudied aspect of this research field. Finally, we discuss related learning paradigms and outline future directions for continual medical image segmentation.

Bomin Wang, Hangqi Zhou, Yibo Gao +1

School of Data Science, Fudan University, Shanghai, China.

Interactive Medical Image Segmentation Stability-Plasticity Dilemma Replay-Based Continual Learning Medical Image Segmentation Medical Image Benchmarks

May 7, 2026cs.LG

CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction

In recent years, the integration of non-topological space modeling with temporal learning methods has emerged as an effective approach for capturing spatio-temporal information in non-Euclidean graphs. However, most existing methods rely on static underlying graph structures, which are inadequate for capturing the continuously expanding and evolving patterns in streaming traffic networks. To address this challenge, we propose a simple yet efficient dual-branch continual learning framework for traffic prediction, named CoMemNet. The fast-converging Online branch undertakes the primary prediction tasks, while the momentum-updated Target branch extracts historical information using Wasserstein Distance features to create a Dynamic Contrastive Sampler (DC Sampler). This sampler selects a node set with significant dynamic network feature changes for training, effectively mitigating the issue of catastrophic forgetting. Additionally, the backbone incorporates a lightweight Node-Adaptive Temporal Memory Buffer (TMRB-N) to consolidate old knowledge through memory replay and address the risk of memory explosion. Finally, we provide two newly curated open-source datasets. Experimental results demonstrate that CoMemNet achieves state-of-the-art (SOTA) performance across all three large-scale real-world datasets. The code is available at: https://github.com/meiwu5/CoMemNet.

Mei Wu, Wenchao Weng, Wenxin Su +2

College of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China · College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China · School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Contrastive Learning Traffic Flow Estimation Dynamic Graph Learning Replay-Based Continual Learning Spatiotemporal Forecasting

May 6, 2026cs.LG

Replay-Based Continual Learning for Physics-Informed Neural Operators

Neural operators generally demonstrate strong predictive performance on in-distribution (ID) problems. However, a critical limitation of existing methods is their significant performance degradation when encountering out-of-distribution (OOD) data. To address this issue, this work introduces continual learning into physics-informed neural operators, with particular emphasis on neural operators built upon the Transolver architecture, and proposes a simple yet effective replay-based continual learning strategy. The proposed method is fully physics-informed and does not require labeled data, relying solely on input fields together with physical constraints for training. When new OOD data become available, a small number of past data are incorporated through a distillation-based constraint to preserve previously acquired knowledge and alleviate catastrophic forgetting. Meanwhile, a transfer learning LoRA is employed to enable rapid adaptation to the new data. The proposed framework is systematically validated on three representative physical problems, including the Darcy flow problem in fluid mechanics, a two-dimensional hyperelastic brain tumor problem in biomechanics, and a three-dimensional linear elastic Triply Periodic Minimal Surfaces problem in solid mechanics. The results demonstrate that the proposed method effectively mitigates catastrophic forgetting on previously learned data while maintaining fast adaptability to new data. Compared with conventional joint training strategies, the proposed method significantly improves training efficiency while reducing additional memory usage and computational cost.

Yizheng Wang, Mohammad Sadegh Eshaghi, Xiaoying Zhuang +2

Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China · Institute of Photonics, Department of Mathematics and Physics, Leibniz University Hannover, Germany · Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany

OOD Generalization Replay-Based Continual Learning Physics-Informed Neural Operators Neural Operators

May 5, 2026cs.LG

Memory-Efficient Continual Learning with CLIP Models

Contrastive Language-Image Pretraining (CLIP) models excel at understanding image-text relationships but struggle with adapting to new data without forgetting prior knowledge. To address this, models are typically fine-tuned using both new task data and a memory buffer of past tasks. However, CLIP's contrastive loss suffers when the memory buffer is small, leading to performance degradation on previous tasks. We propose a memory-efficient, distributionally robust method that dynamically reweights losses per class during training. Our approach, tested on class incremental settings (CIFAR-100, ImageNet1K) and a domain incremental setting (DomainNet) adapts CLIP models quickly while minimizing catastrophic forgetting, even with minimal memory usage.

Ryan King, Gang Li, Bobak Mortazavi +1

Department of Computer Science Texas A&M University College Station, TX 77843 · Texas A&M University College Station, TX 77843

Class-Incremental Learning Contrastive Learning Vision-Language Models VLM Adaptation Replay-Based Continual Learning Continual Learning for VLMs

May 5, 2026cs.LG

A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability

In recent years, machine learning has made significant progress in clinical outcome prediction, demonstrating increasingly accurate results. However, the substantial resources required for hospitals to train these models, such as data collection, labeling, and computational power, limit the feasibility for smaller hospitals to develop their own models. An alternative approach involves transferring a machine learning model trained by a large hospital to smaller hospitals, allowing them to fine-tune the model on their specific patient data. However, these models are often trained and validated on data from a single hospital, raising concerns about their generalizability to new data. Our research shows that there are notable differences in measurement distributions and frequencies across various regions in the United States. To address this, we propose a benchmark that tests a machine learning model's ability to transfer from a source domain to different regions across the country. This benchmark assesses a model's capacity to learn meaningful information about each new domain while retaining key features from the original domain. Using this benchmark, we frame the transfer of a machine learning model from one region to another as a domain incremental learning problem. While the task of patient outcome prediction remains the same, the input data distribution varies, necessitating a model that can effectively manage these shifts. We evaluate two popular domain incremental learning methods: data replay, which stores examples from previous data sources for fine-tuning on the current source, and Elastic Weight Consolidation (EWC), a model parameter regularization method that maintains features important for both data sources.

Ryan King, Conrad Krueger, Ethan Veselka +2

Computer Science & Engineering, Texas A&M University, College Station, United States

Clinical Outcome Prediction Domain-Incremental Learning Replay-Based Continual Learning Cross-Dataset Generalization

May 4, 2026cs.LG

Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning

Electroencephalography (EEG) signals provide millisecond-level temporal resolution but their analysis is limited by remarkable noise and inter-subject variability, making robust personalization difficult under limited annotations. Unsupervised Individual Continual Learning (UICL) has been proposed to address this practical challenge, where a model pretrained on a labeled cohort must adapt online to unlabeled subject streams under strict memory constraints. However, existing UICL methods typically store full past samples, which undermine the continual learning goal of avoiding retraining. Observing that EEG signals exhibit well-structured morphologies to be exploited via morphology-aware selection, compression, and reconstruction, here we propose Adaptive Data Compression and Reconstruction (ADaCoRe) for UICL. This is a memory-efficient pipeline composed of saliency-driven keyframe protection, rational polyphase compression, adjoint reconstruction with verbatim overwrite on protected indices, and prototype-confidence selection for adaptive exemplar maintenance. Across three representative benchmarks, ADaCoRe consistently outperforms recent strong baselines under tight buffer regimes (eg., the performance gains are at least +2.7 and +15.3 ACC on ISRUC and FACED datasets, respectively). Ablation studies quantify compression-fidelity trade-offs and highlight the contribution of each design, while visualizations confirm the preservation of key EEG morphology during compression and reconstruction.

Chengcheng Xie

Beihang University

Electroencephalography Continual Learning Replay-Based Continual Learning

May 1, 2026cs.LG

Continual Learning of Feedback-based Molecular Communication

This paper proposes and evaluates a new performance estimation method that leverages continual learning (CL) algorithms to carry out sequential simulation experiments for a feedback-based molecular communication protocol. As the protocol is sequentially examined in various experimental settings, the proposed CL-based performance estimators incrementally learn a series of unexperienced estimation tasks without compromising those that have been learned in the past. They are designed to work on a standard neural network architecture by customizing regularization and replay strategies in the loss function. Experimental results demonstrate that the proposed estimators can effectively learn on a continuous stream of simulation results and enhance the baseline neural network by improving estimation accuracy at a variety of computational costs. This paper's contribution is to establish the implications of CL in the field of molecular communication.

Siddhant Setia, Junichi Suzuki, Tadashi Nakano

Department of Computer Science, University of Massachusetts, Boston Boston, MA, 02125, USA · Graduate School of Informatics, Osaka Metropolitan University Osaka, Japan

Continual Learning Replay-Based Continual Learning

Apr 27, 2026cs.LG

Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning

Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspective is incomplete, because uncertainty reliability can degrade earlier and more sharply than top-1 performance. We study this empirically by measuring conformal coverage and calibration error on sequentially fine-tuned models across three model families and eight task sequences drawn primarily from classification and multiple-choice benchmarks. Across the classification-style settings we study, coverage loss exceeds accuracy loss by a factor of roughly

3.4\times \pm 0.5\times

on average across seeds; in the most pronounced case, coverage drops from

0.92

to

0.61

, while accuracy remains within three points of baseline. Standard continual-learning methods that preserve accuracy do not automatically preserve coverage, and naive calibration baselines recover only part of the gap. We propose calibration replay, a lightweight post-hoc procedure that maintains a task-specific held-out buffer and refits a task-specific conformal threshold under the current model after each update. It adds no training-time gradient cost, uses less than one percent of the memory of ordinary experience replay, and typically restores coverage to within two points of nominal at buffer size

m = 200

. We accompany the empirical study with a drift decomposition, a finite-sample recovery theorem showing exact conformal validity under exchangeability, and a mixture-validity proposition explaining why pooled thresholds do not suffice. Our guarantees are stated for classification-style tasks with task-specific buffers; extensions to open-ended generation are exploratory.

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Department of Computer Science, Iowa State University · Department of Civil, Construction & Environmental Engineering, Iowa State University

Continual Learning for LLMs Conformal Prediction Language Model Calibration LLM Reliability Replay-Based Continual Learning

Apr 22, 2026cs.LG

Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

Federated continual learning (FCL) allows distributed autonomous fleets to adapt collaboratively to evolving terrain types across extended mission lifecycles. However, current approaches face several key challenges: 1) they use uniform protection strategies that do not account for the varying sensitivities to forgetting on different network layers; 2) they focus primarily on preventing forgetting during training, without addressing the long-term effects of cumulative drift; and 3) they often depend on idealized simulations that fail to capture the real-world heterogeneity present in distributed fleets. In this paper, we propose a lifecycle-aware dual-timescale FCL framework that incorporates training-time (pre-forgetting) prevention and (post-forgetting) recovery. Under this framework, we design a layer-selective rehearsal strategy that mitigates immediate forgetting during local training, and a rapid knowledge recovery strategy that restores degraded models after long-term cumulative drift. We present a theoretical analysis that characterizes heterogeneous forgetting dynamics and establishes the inevitability of long-term degradation. Our experimental results show that this framework achieves up to 8.3% mIoU improvement over the strongest federated baseline and up to 31.7% over conventional fine-tuning. We also deploy the FCL framework on a real-world rover testbed to assess system-level robustness under realistic constraints; the testing results further confirm the effectiveness of our FCL design.

Beining Wu, Jun Huang

Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, 57006 SD

Mobile Robotics Federated Continual Learning Replay-Based Continual Learning Federated Learning Catastrophic Forgetting

Apr 19, 2026cs.LG

Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights

Continual learning (CL) is concerned with learning multiple tasks sequentially without forgetting previously learned tasks. Despite substantial empirical advances over recent years, the theoretical development of CL remains in its infancy. At the heart of developing CL theory lies the challenge that the data distribution varies across tasks, and we argue that properly addressing this challenge requires understanding this variation--dependency among tasks. To explicitly model task dependency, we consider nonlinear regression tasks and propose the assumption that these tasks are dependent in such a way that the data of the current task is a nonlinear transformation of previous data. With this model and under natural assumptions, we prove statistical recovery guarantees (more specifically, bounds on estimation errors) for several CL paradigms in practical use, including experience replay with data-independent regularization and data-independent weights that balance the losses of tasks, replay with data-dependent weights, and continual learning with data-dependent regularization (e.g., knowledge distillation). To the best of our knowledge, our bounds are informative in cases where prior work gives vacuous bounds.

Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu +2

University of Pennsylvania

Continual Learning Replay-Based Continual Learning Statistical Learning Theory

Apr 17, 2026cs.CV

Continual Hand-Eye Calibration for Open-world Robotic Manipulation

Hand-eye calibration through visual localization is a critical capability for robotic manipulation in open-world environments. However, most deep learning-based calibration models suffer from catastrophic forgetting when adapting into unseen data amongst open-world scene changes, while simple rehearsal-based continual learning strategy cannot well mitigate this issue. To overcome this challenge, we propose a continual hand-eye calibration framework, enabling robots to adapt to sequentially encountered open-world manipulation scenes through spatially replay strategy and structure-preserving distillation. Specifically, a Spatial-Aware Replay Strategy (SARS) constructs a geometrically uniform replay buffer that ensures comprehensive coverage of each scene pose space, replacing redundant adjacent frames with maximally informative viewpoints. Meanwhile, a Structure-Preserving Dual Distillation (SPDD) is proposed to decompose localization knowledge into coarse scene layout and fine pose precision, and distills them separately to alleviate both types of forgetting during continual adaptation. As a new manipulation scene arrives, SARS provides geometrically representative replay samples from all prior scenes, and SPDD applies structured distillation on these samples to retain previously learned knowledge. After training on the new scene, SARS incorporates selected samples from the new scene into the replay buffer for future rehearsal, allowing the model to continuously accumulate multi-scene calibration capability. Experiments on multiple public datasets show significant anti scene forgetting performance, maintaining accuracy on past scenes while preserving adaptation to new scenes, confirming the effectiveness of the framework.

Fazeng Li, Gan Sun, Chenxi Liu +3

School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China · State Key Laboratory of Robotics, the Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China · University of Chinese Academy of Sciences, Beijing 100049, China

Hand-Eye Calibration Camera Pose Estimation Robotic Manipulation Replay-Based Continual Learning

Apr 10, 2026cs.RO

Towards Lifelong Aerial Autonomy: Geometric Memory Management for Continual Visual Place Recognition in Dynamic Environments

Robust geo-localization under changing environmental and operational conditions is critical for long-term aerial autonomy. Aerial visual place recognition (VPR) commonly uses pre-acquired remote-sensing imagery of the intended operating area, so the geographic label space can remain fixed while successive airborne missions introduce substantial visual distribution shifts. Continual adaptation to these shifts can cause catastrophic forgetting. We therefore formulate aerial VPR as a mission-based domain-incremental learning (DIL) problem and develop a heterogeneous memory framework. Before sequential adaptation, the satellite reference dataset is used once to train the initial model and construct a static satellite exemplar memory; a bounded replay buffer then retains selected airborne observations across missions. For replay management, we compare loss- and diversity-based selection criteria and introduce DBS-Hybrid, which combines prototype-based diversity trimming with representative-first feature-space coverage. Experiments on 21 visible and infrared UAV missions evaluate generalization to held-out missions, immediate adaptation, and knowledge retention. Under the primary Forward mission order, DBS-Hybrid achieves the highest mean final average accuracy, generalization, and knowledge retention among the evaluated methods, improving over the Random baseline by

5.06

,

5.32

, and

6.33

percentage points, respectively, and improving backward transfer from -6.41% to 1.07%. Across five additional random mission orders, DBS-Hybrid ranks second in mean final average accuracy, backward transfer, generalization, and knowledge retention. Overall, heterogeneous memory and diversity-aware replay provide an effective basis for continual aerial VPR in mapped operating areas.

Xingyu Shao, Zhiqiang Yan, Liangzheng Sun +5

Cross-View Geo-Localization Visual Place Recognition Domain-Incremental Learning Replay-Based Continual Learning

Apr 9, 2026cs.LG

Leveraging Complementary Embeddings for Replay Selection in Continual Learning with Small Buffers

Catastrophic forgetting remains a key challenge in Continual Learning (CL). In replay-based CL with severe memory constraints, performance critically depends on the sample selection strategy for the replay buffer. Most existing approaches construct memory buffers using embeddings learned under supervised objectives. However, class-agnostic, self-supervised representations often encode rich, class-relevant semantics that are overlooked. We propose a new method, Multiple Embedding Replay Selection, MERS, which replaces the buffer selection module with a graph-based approach that integrates both supervised and self-supervised embeddings. Empirical results show consistent improvements over SOTA selection strategies across a range of continual learning algorithms, with particularly strong gains in low-memory regimes. On CIFAR-100 and TinyImageNet, MERS outperforms single-embedding baselines without adding model parameters or increasing replay volume, making it a practical, drop-in enhancement for replay-based continual learning.

Danit Yanowsky, Daphna Weinshall

School of Computer Science and Engineering The Hebrew University of Jerusalem

Experience Replay Replay-Based Continual Learning Catastrophic Forgetting

Mar 31, 2026cs.CL

APEX-EM: Non-Parametric Online Learning for Autonomous Agents via Structured Procedural-Episodic Experience Replay

LLM agents rerun full reasoning for every task, even one they solved moments earlier. We introduce \textbf{APEX-EM}, a non-parametric experience memory that stores complete procedural-episodic traces in a typed Procedural Knowledge Graph (PKG) and retrieves them through three channels: semantic search, structural-signature matching over abstract operation sequences, and graph traversal. A Plan-Retrieve-Generate-Iterate-Ingest (PRGII) workflow produces, quality-gates, and commits experiences, indexing both successes and failures so the agent learns what to reuse and what to avoid. No weights change during deployment. We evaluate on five benchmarks: BigCodeBench, KGQAGen-10k, HLE, Lifelong Agent Bench, and ALFWorld. Because prior work uses different backbones, we base our claims on same-backbone comparisons that hold model capability fixed. On held-out BigCodeBench transfer with a shared GPT-4o backbone, APEX-EM gains +7.6,pp over the no-memory baseline,

3.3\times

MemRL's +2.3,pp under the identical setup. On Lifelong Agent Bench with a shared GPT-4o-mini backbone, it gains +1.4,pp (OS) and +1.0,pp (DB) cumulative success. On KGQAGen-10k, frozen memory transfers to a blind 1{,}079-question test split at 73.7% versus 42.0% with no memory, approaching an oracle handed the ground-truth subgraph (84.9%). Across three Opus scales the memory gain stays at +27 to +32,pp, so it adds to model capability rather than substituting for it. Component analysis shows no single mechanism dominates: teacher feedback is negligible for code but adds +10.3,pp on structured queries, structural signatures give

3.3\times

the transfer of semantic-only retrieval, and within-epoch iteration recovers most of the gain when rich feedback is unavailable. These results argue for modular memory composed per domain.

Pratyay Banerjee, Masud Moshtaghi, Ankit Chadha

Amazon, AGI / Sunnyvale, USA

Continual Learning for LLM Agents Persistent Memory for Language Models Graph-Based Agent Memory Replay-Based Continual Learning Agent Memory Management Memory-Augmented Language Model Agents

Jan 27, 2026cs.LG

Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap

Federated Continual Learning (FCL) leverages inter-client collaboration to better balance new knowledge acquisition and old knowledge retention on non-stationary data. However, existing FCL methods struggle to adapt to streaming scenarios where sequential and ephemerally accessible data chunks lack task identifiers and exhibit arbitrary class overlap, leading to confusion between old and new knowledge and an inability to sustain local inference on all encountered classes. To address this, we propose FedKACE with three components: 1) an adaptive mechanism that determines when to switch the inference model from the local to the global one to improve client-side inference performance; 2) a responsive gradient-balanced replay scheme that utilizes the ratio of the squared L2 gradient norms to balance client-specific knowledge between new acquisition and old retention; 3) a holistic buffer maintenance strategy that preserves highly informative and boundary-significant samples to enhance knowledge retention under class overlap.Experiments across multiple scenarios and theoretical analysis demonstrate the effectiveness of FedKACE.

Sixing Tan, Xianmin Liu

Faculty of Computing Harbin Institute of Technology

Continual Learning Federated Continual Learning Replay-Based Continual Learning Federated Learning

Dec 22, 2025cs.LG

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models

Catastrophic forgetting poses a fundamental challenge in continual learning, particularly when models are quantized for deployment efficiency. We systematically investigate the interplay between quantization precision (FP16, INT8, INT4) and replay buffer strategies in large language models, revealing unexpected dynamics. While FP16 achieves superior initial task performance (74.44% on NLU), we observe a striking inversion on subsequent tasks: quantized models outperform FP16 by 8-15% on final task forward accuracy, with INT4 achieving nearly double FP16's performance on Code generation (40% vs 20%). Critically, even minimal replay buffers (0.1%) dramatically improve retention - increasing NLU retention after Math training from 45% to 65% across all precision levels - with INT8 consistently achieving the optimal balance between learning plasticity and knowledge retention. We hypothesize that quantization-induced noise acts as implicit regularization, preventing the overfitting to new task gradients that plagues high-precision models. These findings challenge the conventional wisdom that higher precision is always preferable, suggesting instead that INT8 quantization offers both computational efficiency and superior continual learning dynamics. Our results provide practical guidelines for deploying compressed models in continual learning scenarios: small replay buffers (1-2%) suffice for NLU tasks, while Math and Code benefit from moderate buffers (5-10%), with quantized models requiring less replay than FP16 to achieve comparable retention. Code is available at https://github.com/Festyve/LessIsMore.

Michael S. Zhang, Rishi A. Ruia, Arnav Kewalram +3

Algoverse

LLM Quantization Continual Learning for LLMs Replay-Based Continual Learning Catastrophic Forgetting

Nov 7, 2025cs.LG

ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids

Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and monitoring is extended to new grid zones. Retraining from scratch on all accumulated data is costly, while naively updating the model on new data causes catastrophic forgetting. To address this problem, we formulate fault type classification and fault zone localization as continual learning (CL) problems and design four evaluation scenarios on the IEEE 13-node test feeder, three class-incremental and one domain-incremental. We then propose Prototype-based Dark Experience Replay (ProDER), which extends DER++ with prototype attraction and prototype-level repulsion losses that stabilize the feature space, temperature-scaled logit distillation, and a prototype-aware replay memory that retains both core and boundary samples of each class. ProDER achieves the highest accuracy among the tested CL methods in all scenarios, with an average accuracy of 58.2%, 6.6 points above the strongest competing method (DPDMR, 51.6%) and only 3.2 points below joint training (61.4%). Per scenario, it improves over the strongest competitor by 4.2 to 7.4 points and closes the gap to joint training to as little as 1.0 point in fault type classification, while matching it in fault zone localization. Moreover, it remains the best method when the replay buffer is substantially reduced. These results show that prototype-guided replay is an effective, memory-bounded way to keep fault diagnosis models up to date as the grid evolves, while validation on field measurements remains a necessary next step.

Emad Efatinasab, Nahal Azadi, Davide Dalle Pezze +3

Department of Information Engineering, University of Padova. · School of Computing, Newcastle University.

Class-Incremental Learning Prototype-Based Classification Replay-Based Continual Learning Fault Localization Fault Diagnosis Power Systems

Oct 20, 2025cs.MM

Taming Modality Entanglement in Continual Audio-Visual Segmentation

Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings while preserving performance on previously learned ones. However, existing methods mainly focus on coarse-grained tasks, with limitations in addressing modality entanglement in fine-grained continual learning settings. To bridge this gap, we introduce a novel Continual Audio-Visual Segmentation (CAVS) task, aiming to continuously segment new classes guided by audio. Through comprehensive analysis, two critical challenges are identified: 1) multi-modal semantic drift, where a sounding objects is labeled as background in sequential tasks; 2) co-occurrence confusion, where frequent co-occurring classes tend to be confused. In this work, a Collision-based Multi-modal Rehearsal (CMR) framework is designed to address these challenges. Specifically, for multi-modal semantic drift, a Multi-modal Sample Selection (MSS) strategy is proposed to select samples with high modal consistency for rehearsal. Meanwhile, for co-occurence confusion, a Collision-based Sample Rehearsal (CSR) mechanism is designed, allowing for the increase of rehearsal sample frequency of those confusable classes during training process. Moreover, we construct three audio-visual incremental scenarios to verify effectiveness of our method. Comprehensive experiments demonstrate that our method significantly outperforms single-modal continual learning methods. Code can be seen at https://github.com/cqu-student/CAVS-CMR.

Yuyang Hong, Qi Yang, Tao Zhang +5

School of Artificial Intelligence, UCAS · 2MAIS, Institute of Automation · School of Intelligent Science and Technology, University of Science and Technolog Beijing

Audio-Visual Understanding Replay-Based Continual Learning

Aug 3, 2025cs.CV

Beyond Discrete Samples: High Information Density Replay for Efficient Lifelong Person Re-Identification

Lifelong Person Re-Identification (LReID) typically resists catastrophic forgetting by replaying historical samples, rehearsing domain distributions, or distilling previous model knowledge. Among these, data replay is favored for its simplicity and efficiency, as it fundamentally relies on storing discrete raw images. Although often claimed to be efficient, repeatedly training on an accumulating replay buffer with complex selection strategies across sequential domains is actually highly inefficient. Furthermore, this discrete selection severely restricts historical data coverage and results in low information density, inevitably leading to poor generalization on evolving domains and causing these methods to gradually fall behind other approaches. In this paper, we rethink LReID replay and shift the paradigm from sample selection to information compression, proposing a High Information Density Replay (HiDeR) framework. Rather than saving sparse instances, we continually consolidate historical data into a compact, fixed-budget memory. Specifically, we introduce a complexity aware allocation mechanism to dynamically assign memory quotas based on intra-class variance, alongside a metric guided condensation objective that directly preserves essential identity topologies. Furthermore, since highly compressed synthetic samples exhibit artifact styles unsuitable for current domain training, we introduce a cross modality adaptation strategy. By bidirectionally translating styles between synthetic and real samples, this strategy bridges the modality gap to mitigate optimization conflicts, while also enriching stylistic diversity for better generalization. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods in retaining historical knowledge and improving overall generalization, while substantially reducing the cumulative replay cost.

Mingyu Wang, Haojie Liu, Zhiyong Li +3

College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Replay-Based Continual Learning Dataset Condensation Person Re-Identification

Apr 14, 2025cs.CV

Exploiting Stability-Plasticity Asymmetry in Pretrained Detectors for Incremental Object Detection

Pretrained model-based incremental object detection (PTMIOD) leverages the rich detection priors of pretrained detectors to learn new categories incrementally while preserving detection ability on previously learned ones. Existing methods mainly exploit pretrained detectors as a whole, without explicitly distinguishing which components should remain stable and which require plastic adaptation. In this paper, we revisit PTMIOD from a component-wise stability-plasticity perspective. Our analysis of pretrained DETR-based detectors reveals a clear asymmetry: localization heads preserve transferable geometric priors across tasks, whereas classification-related representations require greater plasticity to handle new categories, especially in cross-domain scenarios where downstream data deviate from the pretraining domain. Based on this finding, we propose a selective adaptation and retention framework that freezes explicit localization heads to preserve localization stability, while adapting transformer representations with parameter-efficient fine-tuning and updating classification heads for classification-oriented plasticity. To alleviate classification-side forgetting, we pioneer the use of pseudo-feature replay in PTMIOD and design Quality-aware Gaussian Feature Replay, which estimates reliable class-wise feature distributions from high-quality matched object features and replays sampled pseudo features to maintain old-class decision boundaries. Since continual adaptation can shift the feature space and undermine replayed distributions, we further develop Two-stage Consistent Distillation to align teacher and student representations at both proposal generation and refinement stages.Extensive experiments on COCO, VOC, and TT100K show that our method achieves state-of-the-art performance, demonstrating a favorable balance between old-class retention and new-class adaptation.

Songze Li, Qixing Xu, Tonghua Su +3

Harbin Institute of Technology, Harbin, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China · Chongqing Research Institute of HIT, Chongqing, China +2

Class-Incremental Learning Detection Transformer Replay-Based Continual Learning Object Detection