发表机构
Beijing Institute of Technology; BIGAI; Wuhan University; Shenzhen MSU-BIT University（北京理工大学; 北京智源人工智能研究院; 武汉大学; 深圳北理莫斯科大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对GUI智能体持续学习中的知识干扰问题，提出基于激活条件的神经元级梯度操控方法，实现选择性知识保留，缓解灾难性遗忘并保持对新应用的适应能力。
AI 中文摘要
持续学习是图形用户界面（GUI）智能体适应不断演化的应用、同时保留从先前应用获得的知识的一项关键能力。此类应用流提出了一个具有挑战性的知识建模问题：新应用通常与旧应用共享底层知识，但也引入了不得干扰历史知识的特定知识。在本文中，我们提出了基于激活条件的选择性知识控制方法，这是一种通过神经元级梯度操作实现选择性知识保留的轻量级方法。我们的方法维护一个紧凑的历史知识状态，以保护保留先前知识的高激活MLP神经元。当新应用到来时，它基于前向激活执行实时梯度修正。具体而言，受保护的神经元被分为两类：持有特定知识的未激活神经元，其梯度被截断以防止干扰；以及持有共享知识的激活神经元，其梯度被正交投影以在保持稳定性的同时实现适应。在每个应用阶段之后，新识别的关键神经元被合并到历史状态中以供未来学习。在多应用顺序基准上的实证评估表明，我们的方法有效缓解了先前应用上的灾难性遗忘，同时保持了对新应用的稳健适应。
英文摘要
Continual learning is a crucial capability for Graphical User Interface (GUI) agents to adapt to evolving applications while retaining knowledge acquired from previous applications. Such application streams pose a challenging knowledge modeling problem: new applications often share underlying knowledge with past ones, yet also introduce specific knowledge that must not interfere with historical knowledge. In this paper, we propose activation-conditioned selective knowledge control, a lightweight method that achieves selective knowledge retention via neuron-level gradient manipulation. Our method maintains a compact historical knowledge state to protect highly activated MLP neurons that preserve previous knowledge. When a new application arrives, it performs real-time gradient surgery conditioned on forward activation. Concretely, the protected neurons are categorized into two types: unactivated neurons holding specific knowledge, whose gradients are truncated to prevent interference; and activated neurons holding shared knowledge, whose gradients are orthogonally projected to preserve stability while enabling adaptation. After each application stage, newly identified critical neurons are merged into the historical state for future learning. Empirical evaluations on multi-app sequential benchmark demonstrate that our method effectively mitigates catastrophic forgetting on prior applications while sustaining robust adaptation to new ones.
