arXiv AI By Jiaqi Zheng, Yi Hu, Yadong Mu, Muhan Zhang

Knowledge Interaction in Hypernetwork-based Continual Learning: Conflict and Composition

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arXiv Machine Learning
6d ago

FedSEAM: Federated Continual Learning of LLMs via Subspace Expert Agreement Merging

FedSEAM is a federated continual learning framework that trains large language models across multiple clients without sharing data, using sparse mixture-of-experts to adapt selected parameters on a frozen backbone. It introduces task-aware tile selection for new tasks and a subspace-based compatibility test to decide which expert tiles to reuse or replace. On the server, Subspace Expert Agreement Merging (SEAM) aligns client updates only along agreed subspaces, while additional protections mitigate routing drift and merge erasure, preserving prior knowledge during training.

By Fatema Siddika, Abhijit Chunduru, Zilinghan Li, Ali Jannesari, Ravi Madduri
arXiv Machine Learning
Oct 16

HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning

HERO is a heterogeneous continual graph learning framework that preserves historical knowledge at both structural and semantic levels while adapting to new tasks. It uses DiSCo to select representative target nodes and reconstruct compact heterogeneous contexts, and applies multi-level knowledge distillation to align task-conditioned predictions with semantic responses. Experiments on four datasets with three HGNN backbones show HERO achieves best or tied-best average performance while maintaining competitive forgetting.

By Guiquan Sun, Xikun Zhang, Jingchao Ni, Dongjin Song
arXiv Machine Learning
Oct 9

From a Prompt to Repertoires: Evolving Functional REpertoires Enable LLM Continual Learning

The paper introduces Evolving Functional REpertoires (EFRE), a method that replaces a single prompt with a dynamic set of functions to enable large language models to learn new tasks sequentially without forgetting. EFRE refines existing functions when updates are compatible and creates new ones when updates conflict, thereby mitigating catastrophic forgetting and overfitting. Experiments on a three‑task continual‑learning stream show EFRE outperforms GRPO by 7.50 percentage points and reduces performance drop on a subsequent task by only 1.56 percentage points compared to base prompt optimization.

By Fengyuan Liu, Yue Wang, Hangxi Guo, Fengyuan Liu, Chenxu Wu, Yanguang Liu, Mengnan Du
arXiv Machine Learning
Oct 22

Combating Catastrophic Forgetting in Continual Domain Adaptation via Knowledge Recasting

The paper introduces Skill Schema Transport (SST), a post‑training framework that mitigates catastrophic forgetting in continual domain adaptation by translating new domain knowledge into a model’s existing skill space. SST abstracts each example type into a reusable skill schema, preserving essential operations while removing domain‑specific surface forms, thereby turning knowledge updates into procedural reuse rather than trajectory memorization. Experiments on two 3B instruction‑tuned backbones show that SST consistently reduces forgetting and improves generalization, achieving up to a 5.75‑point performance gain and a dramatic drop in maximum forgetting from 42.41 to 7.10 points on the Qwen‑3B MATH stream.

By Zijie Liu, Zhen Tan, Charles Fleming, Tianlong Chen