RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via
  Hierarchical Model Merging
Authors: name: Bowen Wang, name: Haiyuan Wan, name: Liwen Shi, name: Chen Yang, name: Peng He, name: Yue Ma, name: Haochen Han, name: Wenhao Li, name: Tiao Tan, name: Yongjian Li, name: Fangming Liu, name: Yifan Gong, name: Sheng Zhang
Organization: Tsinghua University

We unveil that internal representations in large language models (LLMs) serve
as reliable proxies of learned knowledge, and propose RECALL, a novel
representation-aware model merging framework for continual learning without
access to historical data. RECALL computes inter-model similarity from
layer-wise hidden representations over clustered typical samples, and performs
adaptive, hierarchical parameter fusion to align knowledge across models. This
design enables the preservation of domain-general features in shallow layers
while allowing task-specific adaptation in deeper layers. Unlike prior methods
that require task labels or incur performance trade-offs, RECALL achieves
seamless multi-domain integration and strong resistance to catastrophic
forgetting. Extensive experiments across five NLP tasks and multiple continual
learning scenarios show that RECALL outperforms baselines in both knowledge
retention and generalization, providing a scalable and data-free solution for
evolving LLMs.