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

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

Memory Transfer Learning (MTL) enables coding agents to utilize a unified memory pool from heterogeneous task domains, addressing limitations of existing approaches that restrict memory to homogeneous domains. This method, evaluated across 6 coding benchmarks using four memory representations (Trajectory, Workflow, Summary, Insight), improves average performance by 3.7%. The study finds that cross-domain memory primarily transfers meta-knowledge, such as validation routines, rather than task-specific code. Abstraction is critical, with high-level insights generalizing well and low-level traces often causing negative transfer due to excessive specificity. MTL's effectiveness scales with memory pool size and can transfer knowledge between different models, including GPT-5-mini, DeepSeek V3.2, and Qwen3-Coder-480B-A35B-Instruct, demonstrating broad applicability and outperforming self-evolving methods like ReasoningBank and AgentKB.

Key takeaway

For Research Scientists developing self-evolving coding agents, you should integrate Memory Transfer Learning to enhance agent performance across diverse programming tasks. Focus on generating and transferring high-level, abstract "Insight" memories, as these provide generalizable meta-knowledge (e.g., structured workflows, validation routines) that significantly improves cross-domain applicability and efficiency, outperforming single-domain memory approaches. Be mindful of potential negative transfer from overly specific, low-abstraction memories.

Key insights

Cross-domain memory transfer, especially with abstract insights, significantly boosts coding agent performance by sharing meta-knowledge.

Principles

• Abstraction dictates memory transferability.
• Meta-knowledge transfers better than task-specific code.
• Larger, more diverse memory pools improve transfer effectiveness.

Method

Generate memories from diverse coding tasks in four formats (Trajectory, Workflow, Summary, Insight), then retrieve top-N relevant memories via embedding similarity to augment agent prompts.

In practice

• Prioritize abstract "Insight" memories for cross-domain transfer.
• Expand memory pools with diverse task domains.
• Focus on meta-knowledge like validation routines for transfer.

Topics

• Memory Transfer Learning
• Coding Agents
• Self-Evolving Agents
• Meta-Knowledge Transfer
• Memory Abstraction

Code references

• laude-institute/harbor

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.