arXiv:2605.20247v1 Announce Type: new Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs). Although Mixture-of-Experts (MoE) architectures offer an efficient path to scaling, existing LoRA-based MoE continual learning methods still face a fundamental trade-off: they either isolate experts too aggressively, limiting knowledge transfer across tasks, or allow task-specific updates to overwrite important existing parameters, leading to severe forgetting. To address this, we propose CP-MoE, a continual l
The continuous improvement of large AI models highlights catastrophic forgetting as a core limitation, spurring research into more robust continual learning architectures like MoE. This research emerges as models scale and the need for adaptive, efficient learning increases.
Overcoming catastrophic forgetting is crucial for developing truly adaptive and long-lived AI systems, essential for future autonomous agents and enterprise AI applications. It directly impacts the operational efficiency and deployment capabilities of advanced AI.
The proposed CP-MoE architecture offers a potential pathway to enable large models to continually learn new tasks without performance degradation on previously learned ones. This could lead to more versatile and cost-effective AI model maintenance and deployment.
- · AI developers
- · Enterprises adopting AI
- · Cloud AI providers
- · Current AI model retraining pipelines
- · Models reliant on aggressive task-specific fine-tuning
More efficient and adaptable large language and vision models are developed.
AI agents can learn and adapt to new environments and tasks over extended periods without significant re-engineering.
The total cost of ownership for advanced AI systems decreases, accelerating wider deployment across industries.
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Read at arXiv cs.LG