Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization to homogeneous task domains, failing to leverage the shared infrastructural foundations, such as runtime environments and programming languages, that exist across diverse real-world coding problems. To address this limitation, we investigate \textbf{Memory Transfer Learning} (MTL) by harnessing a unified memory pool from heterogeneous domains. We evaluate performance across 6 coding benchmarks using four memory representations, ranging from concrete traces to abstract insights. Our experiments demonstrate that cross-domain memory improves average performance by 3.7\%, primarily by transferring meta-knowledge, such as validation routines, rather than task-specific code. Importantly, we find that abstraction dictates transferability; high-level insights generalize well, whereas low-level traces often induce negative transfer due to excessive specificity. Furthermore, we show that transfer effectiveness scales with the size of the memory pool, and memory can be transferred even between different models. Our work establishes empirical design principles for expanding memory utilization beyond single-domain silos. Project page: https://memorytransfer.github.io/

翻译：基于记忆的自我进化已成为编码智能体领域的一种崭新范式。然而，现有方法通常将记忆利用限制在同质任务域内，未能充分利用现实编码问题中普遍存在的共享基础设施基础（如运行时环境和编程语言）。为突破这一局限，我们通过构建异构域统一记忆池，系统研究了**记忆迁移学习**（MTL）。我们采用四种记忆表征（从具体轨迹到抽象洞见），在6个编码基准测试上评估了性能。实验表明，跨域记忆使平均性能提升3.7%，主要归功于元知识（如验证例程）的迁移，而非特定任务代码。重要的是，我们发现抽象程度决定可迁移性：高层级洞见具有良好的泛化能力，而低层级轨迹因过度特异性常引发负迁移。此外，我们证实迁移效果随记忆池规模扩大而增强，且记忆甚至可在不同模型间实现迁移。本研究为突破单域记忆利用的局限性建立了实证设计原则。项目主页：https://memorytransfer.github.io/