一句话：提出资源高效的自适应表示 HyperSeq，在 IDE 中实时学习并预测开发者动作序列，准确率超过 70%。
随着开发者日益依赖 AI 驱动的工具，集成开发环境（IDE）功能不断增强，但随之而来的是更高的硬件需求以及模型带来的经济与环境成本。本文提出 HyperSeq——一种面向状态预测序列的超自适应表示（Hyper-Adaptive Representation for Predictive Sequencing of States），旨在以资源高效的方式建模开发者的认知状态，支持精确的动作序列预测与用户行为的实时学习。初步结果表明，HyperSeq 在预测动作序列方面表现优异，准确率超过 70%；其在线学习能力在大多数情况下能显著提升预测准确率，并在经过足够迭代适应后增强对用户下一步动作的预测能力。最终目标是在 IDE 中动态利用这些预测来优化和提升用户体验。
In the rapidly evolving world of software development, the surge in developers' reliance on AI-driven tools has transformed Integrated Development Environments into powerhouses of advanced features. This transformation, while boosting developers' productivity to unprecedented levels, comes with a catch: increased hardware demands for software development. Moreover, the significant economic and environmental toll of using these sophisticated models necessitates mechanisms that reduce unnecessary computational burdens. We propose HyperSeq — Hyper-Adaptive Representation for Predictive Sequencing of States — a novel, resource-efficient approach designed to model developers' cognitive states. HyperSeq facilitates precise action sequencing and enables real-time learning of user behavior. Our preliminary results show how HyperSeq excels in forecasting action sequences and achieves remarkable prediction accuracies that go beyond 70%. Notably, the model's online-learning capability allows it to substantially enhance its predictive accuracy in a majority of cases and increases its capability in forecasting next user actions with sufficient iterations for adaptation. Ultimately, our objective is to harness these predictions to refine and elevate the user experience dynamically within the IDE.