Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents
- Published
- Source
- arXiv
- Paper number
- 147
- Field
- Agents / Memory
- arXiv ID
- 2604.14004
Key points
- Existing self-evolving AI agents for coding tasks usually restrict memory use to homogeneous task domains and overlook the shared infrastructural substrate, such as programming languages and runtime environments.
- This limitation prevents agents from leveraging transferable knowledge across diverse real-world coding problems, which hurts generalization and robust performance.
- Prior work lacked a systematic study of how memory transfers across domains and of the design principles for effective memory in these heterogeneous settings.
- The paper develops Memory Transfer Learning, a two-stage process consisting of offline memory generation from agent reasoning trajectories and online memory retrieval while solving new tasks.
- Using an LLM for generation, it creates four distinct memory representations, Trajectory, Workflow, Summary, and Insight, spanning low-level task details through high-level generalized meta-knowledge.
- It retrieves the most relevant memory from heterogeneous domain memory pools using embedding-based cosine similarity and supplies it to the coding agent's system prompt.
Paper links
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