AGENTIC-SYSTEMSApril 17, 20261 min read9 views

Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents

Researchers investigate Memory Transfer Learning (MTL) for coding agents, demonstrating that sharing abstract meta-knowledge across different programming domains can improve performance by 3.7% and enable cross-model knowledge transfer.

Computer Science > Artificial Intelligence

Title:Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents

View PDF HTML (experimental)Abstract: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: this https URL

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Source: arXiv cs.AI Recent

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