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    <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 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% over zero-shot setting and even outperforms the strongest self-evolving baseline, 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.</abstract>
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%0 Conference Proceedings
%T Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents
%A Kim, Kangsan
%A Kang, Minki
%A Kim, Taeil
%A Yang, Yanlai
%A Ren, Mengye
%A Hwang, Sung Ju
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kim-etal-2026-memory
%X 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 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% over zero-shot setting and even outperforms the strongest self-evolving baseline, 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.
%U https://aclanthology.org/2026.emnlp-main.2666/
%P 60677-60697

Markdown (Informal)

[Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents](https://aclanthology.org/2026.emnlp-main.2666/) (Kim et al., EMNLP 2026)

• Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents (Kim et al., EMNLP 2026)

ACL

• Kangsan Kim, Minki Kang, Taeil Kim, Yanlai Yang, Mengye Ren, and Sung Ju Hwang. 2026. Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 60677–60697, Budapest, Hungary. Association for Computational Linguistics.