Knowledge Interaction in Hypernetwork-based Continual Learning: Conflict and Composition
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?
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.
arXiv:2606. 07500v1 Announce Type: cross Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge.
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.
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.
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.