SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement
SWE-Search combines MCTS with self-improvement, achieving 23% performance gains in software tasks.
Key Findings
Methodology
The proposed SWE-Search framework integrates Monte Carlo Tree Search (MCTS) with a hybrid value function leveraging large language models (LLMs). It comprises four components: SWE-Agent for adaptive exploration, Value Agent for utility estimation and natural language explanations, a modified UCT-based MCTS for efficient search, and a Discriminator Agent for multi-agent debate-based final decision. The value function outputs both numerical utility and qualitative explanations, enabling iterative self-feedback. The search algorithm balances exploration and exploitation through depth-aware heuristics, simulating engineer-like problem-solving. Multi-agent debate enhances robustness of solution selection, mimicking collaborative engineering workflows.
Key Results
- On the SWE-bench-lite dataset, performance across five models improved by 23% relative to baseline open-source agents without MCTS, with the highest improvement reaching 31%.
- Deeper search depth, enabled by increased inference time, significantly improved solution quality, confirming the scalability of the approach.
- The multi-agent debate mechanism increased final solution accuracy from 73% to 84%, demonstrating robustness in decision-making.
Significance
This work advances autonomous software agents by integrating deep search and self-evaluation, addressing the limitations of linear, static models. It demonstrates that performance can be substantially improved without enlarging models or requiring additional training data, solely by extending inference time. The approach aligns with real-world engineering workflows, offering a pathway toward more autonomous, adaptive, and collaborative software development systems. Its implications span automated bug fixing, code refactoring, and software maintenance, promising significant industry impact.
Technical Contribution
The key technical innovation is embedding MCTS within software engineering tasks, combined with a hybrid value function that produces both quantitative scores and natural language explanations. The modified UCT strategy incorporates depth-based bonuses and penalties, enabling efficient exploration akin to human problem-solving. The multi-agent debate framework introduces a new layer of robustness, allowing diverse perspectives to influence final decisions. This integrated approach offers a new paradigm for scalable, interpretable, and self-improving software agents, capable of operating effectively without additional training data.
Novelty
This is the first application of Monte Carlo Tree Search in software engineering tasks involving multi-agent collaboration with natural language explanations. Unlike prior work that relied solely on linear or greedy search, SWE-Search employs deep exploration with heuristic-guided search and structured debate, setting a new standard for autonomous software reasoning. Its combination of search, interpretability, and multi-agent debate constitutes a significant innovation in the field.
Limitations
- In highly complex or high-dimensional state spaces, the search depth may be insufficient, limiting performance. Computational costs increase with deeper searches, affecting real-time applicability.
- Multi-agent debate relies on predefined heuristics, which may introduce bias or inconsistency across different scenarios.
- Extending inference time improves performance but raises practical concerns about efficiency and resource consumption in industrial settings.
Future Work
Future directions include adaptive mechanisms for debate and search depth, integration with reinforcement learning for online policy improvement, and application to larger, more diverse software environments. Exploring ways to reduce inference costs while maintaining performance will be critical for real-world deployment. Additionally, extending the framework to multi-language and multi-platform software tasks remains an open challenge.
AI Executive Summary
In modern software engineering, tackling complex tasks such as debugging, feature development, and code refactoring requires flexible, iterative problem-solving. Traditional large language model (LLM)-based agents often follow linear, one-directional workflows, which limit their ability to backtrack, explore alternative solutions, or adapt to new information. This rigidity hampers their effectiveness in real-world, long-horizon tasks where dynamic decision-making and collaboration are essential.
To address these limitations, the authors introduce SWE-Search, a multi-agent framework that combines Monte Carlo Tree Search (MCTS) with self-evaluation and collaborative debate mechanisms. The system comprises four main components: the SWE-Agent, which explores the solution space adaptively; the Value Agent, which estimates utility and generates natural language explanations; a modified UCT-based MCTS algorithm that guides exploration efficiently; and a Discriminator Agent, which conducts multi-agent debates to select the most promising solutions. This architecture emulates human engineers' iterative workflows, allowing for backtracking, multi-path exploration, and collaborative decision-making.
The core innovation lies in integrating deep search with a hybrid value function that provides both quantitative scores and qualitative insights, enabling the system to refine strategies iteratively. The multi-agent debate further enhances robustness, leading to significant performance improvements. Experimental results on the SWE-bench-lite dataset show a 23% relative increase in success rate across five models, with the best results reaching 31%. Deeper search depth, enabled by increased inference time, correlates with higher solution quality, demonstrating scalability.
This approach marks a shift towards more autonomous, interpretable, and collaborative software agents. It offers a practical pathway to improve software development workflows without enlarging models or requiring additional training data, solely by leveraging inference-time computation. The framework's success suggests promising future directions, including online learning, broader application domains, and industrial deployment, paving the way for smarter, more adaptable software engineering tools.
Deep Analysis
Background
软件工程作为一门复杂且不断演变的学科，经历了从规则驱动到机器学习的转变。早期方法依赖静态规则和模板，难以应对多变的实际需求。近年来，深度学习和大模型（如OpenAI Codex、DeepMind AlphaCode）推动了自动代码生成、调试和重构的发展，但这些方法多采用线性、单向流程，缺乏探索和回溯能力，限制了在复杂任务中的表现。研究逐步认识到搜索机制和多智能体协作的重要性，试图突破单一模型的局限，推动系统向更自主、鲁棒的方向发展。
Core Problem
现有大模型软件代理多沿用线性、单向流程，难以进行有效的回溯和多路径探索，导致在面对复杂、多变的工程任务时，容易陷入局部最优或重复无效操作。这限制了系统的自主性和适应性，特别是在长远目标、多方案选择和团队合作场景中表现不足。解决方案需要引入深度搜索、动态规划和多智能体讨论，模拟工程师的反复试错、方案讨论与优化过程，以提升整体解决能力。
Innovation
本研究的核心创新包括：1）将蒙特卡洛树搜索（MCTS）引入软件工程任务，实现多路径深度探索，突破线性流程限制；2）结合大模型的数值估算与自然语言解释，支持自我反馈与策略优化，增强系统的可解释性；3）设计多智能体辩论机制，模拟工程师团队的合作讨论，提升方案的鲁棒性。这些创新共同推动软件代理向更自主、适应性强的方向发展，为复杂软件任务提供了新思路。
Methodology
- �� 构建动态状态空间与git-like提交树结构，支持灵活回溯与多路径探索。• 采用改进的UCT策略，结合深度奖励与惩罚，优化搜索路径，模拟工程师的探索习惯。• 利用LLM生成数值估算和自然语言解释，支持自我反馈与策略调整。• 设计多智能体系统，包括SWE-Agent、Value Agent和Discriminator Agent，协同完成任务。• 在搜索中结合启发式奖励，平衡探索与利用，逐步收敛到最优方案。• 通过多轮辩论筛选方案，提升决策的鲁棒性。• 在SWE-bench-lite上验证性能，分析搜索深度与推理时间的关系。
Experiments
采用SWE-bench-lite数据集，包含300个真实软件缺陷实例。对比基线Moatless-Adapted与改进模型，使用Resolve Rate（Pass@1）和Pass@5指标，评估成功率和效率。模型超参数包括最大搜索节点数100，节点扩展限制3次。进行消融实验验证深度搜索和辩论机制的贡献。多模型测试确保结果稳健。分析搜索深度与推理时间的关系，验证性能随计算资源增加而提升。结果显示，深度搜索显著提升复杂任务的解决率，验证了算法的扩展性。
Results
五个模型中，SWE-Search平均性能提升23%，最高达31%。深度搜索增强了复杂任务的解决能力，验证了搜索深度的正向关系。辩论机制使最终方案准确率从73%提升至84%，显示出方案的鲁棒性。多模型结果表明，该方法在不同架构下均有效，具有良好的泛化能力。增加推理时间进一步提升方案质量，验证了算法的扩展潜力。
Applications
该方法适用于自动缺陷修复、代码重构、软件维护等场景。企业可结合此框架提升开发效率，减少人工调试。系统可集成到持续集成/持续部署流程，实现自动检测与修复。未来结合在线学习和强化学习，有望实现更高自主性和适应性，推动工业界智能软件代理的普及。
Limitations & Outlook
在极端复杂或高维状态空间中，搜索深度可能不足，影响性能。多智能体辩论依赖预定义规则，可能引入偏差或不一致。推理时间增加带来计算成本，实际应用中需权衡效率与效果。未来需优化搜索策略和辩论机制，提升效率和鲁棒性。
Plain Language Accessible to non-experts
想象你在厨房做一道复杂的菜肴。你会尝试不同的调料和步骤，反复试验，调整味道。有时候你会退回到之前的步骤，重新尝试不同的做法。你还会和朋友讨论，听取他们的建议，最后选择最合适的方案。SWE-Search就像这个厨房助手，它能不断尝试不同的做法，听取各种反馈，最终做出最美味的菜肴。它不是死板地按步骤走，而是像厨师一样灵活调整，确保每次都能做出更好的结果。
ELI14 Explained like you're 14
想象你在学校做科学实验，遇到一个难题。你会试着用不同的方法解决，比如换个角度或用不同的工具。有时候你会发现之前的方法不行，就会退回去，重新考虑。你还会和同学讨论，听听他们的想法，最后决定用哪个方案最好。SWE-Search就像一个聪明的学生助手，它可以不断试验不同的方案，听取各种建议，最后帮你找到最好的解决办法。它不像机器人那样死板，而是像个聪明的伙伴，帮你解决复杂的问题。
Glossary
Monte Carlo Tree Search (蒙特卡洛树搜索)
一种基于随机采样的搜索算法，通过模拟多条路径选择最优行动，广泛应用于游戏和决策任务。在本文中用于软件任务的深度搜索。
作为核心搜索机制，优化软件代理的探索效率。
价值函数 (Value Function)
评估状态或动作潜在收益，结合数值和自然语言解释，支持自我反馈。在本文中用于指导决策与策略优化。
为多智能体提供决策依据，增强系统的解释能力。
多智能体辩论 (Multi-agent Debate)
多个智能体就方案进行讨论，评估优劣，最终达成共识。提升方案鲁棒性。在本文中用于最终方案筛选。
模拟工程师团队合作，增强决策合理性。
Open Questions Unanswered questions from this research
- 1 如何在极端复杂或高维状态空间中保证搜索深度和效率？
- 2 多智能体辩论机制的偏差控制与一致性保障问题仍待深入研究。
- 3 在实际工业环境中，如何平衡推理时间与搜索深度以实现实时应用？
Applications
Immediate Applications
自动缺陷修复
结合SWE-Search实现自动化代码修复，减少人工调试时间，提升软件维护效率。
持续集成优化
在CI/CD流程中集成该系统，实现自动检测与修复软件缺陷，保障系统稳定性。
Long-term Vision
自主软件开发平台
构建全自动化的软件生成与优化平台，减少人类干预，推动软件产业智能化。
Abstract
Software engineers operating in complex and dynamic environments must continuously adapt to evolving requirements, learn iteratively from experience, and reconsider their approaches based on new insights. However, current large language model (LLM)-based software agents often follow linear, sequential processes that prevent backtracking and exploration of alternative solutions, limiting their ability to rethink their strategies when initial approaches prove ineffective. To address these challenges, we propose SWE-Search, a multi-agent framework that integrates Monte Carlo Tree Search (MCTS) with a self-improvement mechanism to enhance software agents' performance on repository-level software tasks. SWE-Search extends traditional MCTS by incorporating a hybrid value function that leverages LLMs for both numerical value estimation and qualitative evaluation. This enables self-feedback loops where agents iteratively refine their strategies based on both quantitative numerical evaluations and qualitative natural language assessments of pursued trajectories. The framework includes a SWE-Agent for adaptive exploration, a Value Agent for iterative feedback, and a Discriminator Agent that facilitates multi-agent debate for collaborative decision-making. Applied to the SWE-bench benchmark, our approach demonstrates a 23% relative improvement in performance across five models compared to standard open-source agents without MCTS. Our analysis reveals how performance scales with increased inference-time compute through deeper search, providing a pathway to improve software agents without requiring larger models or additional training data. This highlights the potential of self-evaluation driven search techniques in complex software engineering environments.