Structurally Aligned Subtask-Level Memory for Software Engineering Agents

• URL: http://arxiv.org/abs/2602.21611v1
• Date: Wed, 25 Feb 2026 06:13:25 GMT
• Title: Structurally Aligned Subtask-Level Memory for Software Engineering Agents
• Authors: Kangning Shen, Jingyuan Zhang, Chenxi Sun, Wencong Zeng, Yang Yue,
• Abstract summary: Large Language Models (LLMs) have demonstrated significant potential as autonomous software engineering (SWE) agents.<n>Recent work has explored augmenting these agents with memory mechanisms to support long-horizon reasoning.<n>We propose Structurally Aligned Subtask-Level Memory to align memory storage, retrieval, and updating with the agent's functional decomposition.
• Score: 15.239652771593663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated significant potential as autonomous software engineering (SWE) agents. Recent work has further explored augmenting these agents with memory mechanisms to support long-horizon reasoning. However, these approaches typically operate at a coarse instance granularity, treating the entire problem-solving episode as the atomic unit of storage and retrieval. We empirically demonstrate that instance-level memory suffers from a fundamental granularity mismatch, resulting in misguided retrieval when tasks with similar surface descriptions require distinct reasoning logic at specific stages. To address this, we propose Structurally Aligned Subtask-Level Memory, a method that aligns memory storage, retrieval, and updating with the agent's functional decomposition. Extensive experiments on SWE-bench Verified demonstrate that our method consistently outperforms both vanilla agents and strong instance-level memory baselines across diverse backbones, improving mean Pass@1 over the vanilla agent by +4.7 pp on average (e.g., +6.8 pp on Gemini 2.5 Pro). Performance gains grow with more interaction steps, showing that leveraging past experience benefits long-horizon reasoning in complex software engineering tasks.

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