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[2602.21611] Structurally Aligned Subtask-Level Memory for Software Engineering Agents
Summary
The paper presents Structurally Aligned Subtask-Level Memory, a novel approach for enhancing software engineering agents by improving memory mechanisms for better task-specific reasoning.
Why It Matters
As software engineering increasingly leverages AI, enhancing the memory capabilities of software engineering agents is crucial for improving their performance in complex tasks. This research addresses a significant gap in existing memory models, potentially leading to more efficient and effective AI-driven software development processes.
Key Takeaways
- Proposes a new memory model that aligns with task decomposition.
- Demonstrates improved performance over traditional instance-level memory.
- Shows significant gains in long-horizon reasoning for complex software tasks.
- Empirical results indicate a consistent performance boost across various AI models.
- Highlights the importance of tailored memory mechanisms in AI applications.
Computer Science > Software Engineering arXiv:2602.21611 (cs) [Submitted on 25 Feb 2026] Title:Structurally Aligned Subtask-Level Memory for Software Engineering Agents Authors:Kangning Shen, Jingyuan Zhang, Chenxi Sun, Wencong Zeng, Yang Yue View a PDF of the paper titled Structurally Aligned Subtask-Level Memory for Software Engineering Agents, by Kangning Shen and 4 other authors View PDF HTML (experimental) 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...