Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents
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 av...
| Authors | Kangsan Kim, Minki Kang, Taeil Kim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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