# 记忆迁移学习：编程智能体中的跨域记忆迁移机制

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-15 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo125ffd008eslzfj6wimt88
- 原文链接：https://arxiv.org/abs/2604.14004

## AI 摘要

研究人员提出记忆迁移学习方法，利用跨异构域的统一记忆池突破编程智能体的单域限制。在6个代码基准测试中，跨域记忆平均提升性能3.7%，主要转移元知识（如验证例程）而非任务特定代码。研究表明抽象程度决定可迁移性：高层洞察泛化良好，低层轨迹因过度具体常引发负迁移。迁移效果随记忆池规模扩大而提升，且记忆可在不同模型间转移。该研究为跨域记忆利用确立了经验设计原则。

## 正文

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\%, 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/