A revised arXiv preprint examines how small language models deployed on edge devices can be personalized over time without losing prior knowledge. The work focuses on monitoring stability during sequential adaptation, a known risk when models are updated repeatedly. It is a research contribution rather than a product or model release.
A revised arXiv preprint describes a continual learning approach that leverages pre-trained models to help systems keep earlier knowledge while picking up new tasks. The work targets catastrophic forgetting, where performance on previously learned tasks degrades as new ones are acquired. The paper is listed as a replacement submission, with no peer-reviewed venue indicated.
A new arXiv paper introduces TypiCore, a method that combines active learning queries with class-incremental learning for time series data. The approach targets settings such as healthcare and manufacturing, where models must adapt to distribution shifts as new classes arrive over time. The work is posted as a cross-listing replacement on arXiv and has not yet undergone peer review.
A new arXiv preprint introduces a method called Homeostatic Continual Learning that aims to let an AI agent keep learning as its environment changes without losing previously acquired knowledge. The approach targets catastrophic forgetting, a long-standing problem in continual learning research. The abstract provides only a brief description of the method's core mechanism.
A new study examines how autoregressive speech recognition systems that output timestamps as decoded tokens can gradually lose alignment during long stretches without speech. The authors propose a replay-based distribution editing approach that corrects this drift while limiting forgetting of previously learned behavior. The work targets timestamped transcription without relying on frame-level aligners or inference-time fixes.
A new arXiv preprint proposes using separate domain-specific expert subnetworks, combined with parameter isolation, to handle domain-incremental learning for audio classification. The approach targets settings where a previously trained model must keep adapting as new data domains arrive over time, such as streaming prediction and sensing control. It is a cross-listed machine learning paper and has not yet been peer reviewed.
A new arXiv paper tackles the stability-plasticity dilemma in continual reinforcement learning by progressively expanding a DQN agent guided by a curriculum. The approach is applied to adaptive train scheduling, where conditions shift over time and earlier knowledge must be retained. It aims to let the agent grow more complex behaviors without overwriting what it already learned.
A preprint on arXiv introduces an approach called the Metric Slingshot, which frames navigational reuse as a way to achieve width-optimal structural decoupling in continual learning. The work draws on neuroscience findings about grid cells, place cells, and hippocampal indexing, which the brain uses for both spatial and non-spatial tasks. It argues that reusing navigation-related circuitry can help neural networks avoid interference across sequential tasks. Only the abstract excerpt is available, so full results and benchmarks are not yet assessed.
A new arXiv paper introduces Sylvas, a scheduling method for federated continual learning that selects which devices contribute updates based on their estimated learning value. The approach targets distributed, non-stationary data streams in Internet of Things settings such as intelligent transportation and industrial monitoring. The work appears under both cs.AI (cross) and cs.LG (new) listings as arXiv:2609.15763v1.
A new arXiv paper examines where and how much neural networks forget when trained sequentially on gynecological medical imaging tasks. The authors analyze forgetting layer by layer to identify which parts of a segmentation model should be preserved and which can be adapted. The work targets scenarios where consecutive clinical tasks vary in imaging modality and anatomy.
A new arXiv paper argues that deciding whether to accept a policy update in continual embodied learning should be judged on two fronts: rejecting harmful changes and preserving useful learning. The authors propose auditing update admission at a fixed interaction budget, since overly strict validation can block beneficial adaptation. They call for evaluation that measures both error control and the learning opportunities retained.