新方法解决AI模型灾难性遗忘问题 · 跟踪3个来源

报道来源 [3]

1. arXiv cs.CL TIER_1 English(EN) · Reza Rahimi Azghan, Gautham Krishna Gudur, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh · 2026-07-28 04:00

   Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning

   arXiv:2607.23837v1 Announce Type: cross Abstract: Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially, leading to catastrophic forgetting. To mitigate this, LoRA-based continual learning methods allocate a separa…

2. arXiv cs.LG TIER_1 English(EN) · Grzegorz Rype\'s\'c · 2026-07-28 04:00

   Gradient-Free Continual Learning

   arXiv:2504.01219v2 Announce Type: replace Abstract: Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting. Standard continual learning techniques try to fix this by saving old data or relying on complex gradient …

3. arXiv cs.CV TIER_1 English(EN) · De Cheng, Yue Lu, Lingfeng He, Shizhou Zhang, Xi Yang, Nannan Wang, Xinbo Gao · 2026-07-28 04:00

   Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning

   arXiv:2411.15469v3 Announce Type: replace Abstract: Continual Learning (CL) aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. Recently, State Space Models (SSMs), particularly the Mamba model, have ac…