清华团队提出FiUni框架，用Fisher信息矩阵解决无任务持续学习，比传统方法更高效。
研究人员提出FiUni框架，用于批级别任务检测和参数高效持续适应。该框架基于预训练模型的Fisher信息矩阵K-FAC近似的主子空间正交性观察，能反映不同任务间的相似性。FiUni构建FIM衍生的冻结子空间引导LoRA适配，同时匹配每个传入批窗口的Fisher主子空间与历史子空间。实验显示FiUni能有效推断潜在批级别任务关联，并在更少可训练参数下达到与先进任务感知CL方法相当的性能。
Unifying Detection and Adaptation in Task-Free Continual Learning
To mitigate catastrophic forgetting in downstream continual learning (CL) for large language models (LLMs), existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, these methods often rely on explicit task boundaries during training, limiting their applicability to realistic task-free scenarios. In this paper, we propose a \textbf{Fi}sher-guided \textbf{uni}fied (\textbf{FiUni}) framework for batch-level task detection and parameter-efficient continual adaptation. FiUni is motivated by a key observation about the Fisher information matrix (FIM) of pre-trained models: the orthogonality among the principal subspaces of its Kronecker-Factored Approximate Curvature (K-FAC) approximation, estimated from a small number of downstream task samples, can reflect the similarity between different tasks. Based on this observation, FiUni constructs FIM-derived frozen subspaces to guide low-rank adaptation (LoRA), while matching the Fisher principal subspace of each incoming batch window with historical subspaces. This enables FiUni to adaptively determine whether to reuse existing knowledge, expand a related subspace, or create a new subspace, dynamically balancing knowledge sharing and task isolation. Experiments show that FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware CL methods with fewer trainable parameters.