Large language models (LLMs) have shown strong capabilities in software engineering tasks, yet most existing LLM-based SWE-Agents mainly tackle well-defined problems using conventional methods, often overlooking alternative or innovative solutions beyond their predefined frameworks. This limitation is evident in open-world software environments, where emerging challenges transcend established paradigms. We propose U2F (Unknown Unknowns to Functional solutions), a cognitive-inspired, uncertainty-embracing multi-agent framework that systematically surfaces "Unknown Unknowns" - novel solution pathways absent from initial formulations but holding innovative potential. U2F consists of two key components: (1) a Discovery-Exploration-Integration agent system for uncovering and synthesizing potential solutions, and (2) cognitive enhancement mechanisms across three dimensions: cross-domain analogical reasoning, reverse thinking, and external validation, which strategically reframe and extend conventional solution boundaries. Applied to 218 real-world software enabler stories curated from authentic engineering tasks, U2F achieved notable improvements: human experts reported a 14 percent increase in overall novelty, 51 percent improvement in semantic novelty, and stable feasibility (4.02/5.0), corroborated by an LLM-based evaluator. These results highlight the potential of embracing uncertainty as a catalyst for innovation in software engineering.
翻译：大语言模型（LLMs）在软件工程任务中展现出强大能力，然而现有基于LLM的SWE-Agent主要采用常规方法处理明确定义的问题，往往忽视其预设框架之外的替代性或创新性解决方案。这一局限在开放世界软件环境中尤为明显，新兴挑战常超越既定范式。我们提出U2F（从未知未知到功能解决方案），这是一个受认知启发的、拥抱不确定性的多智能体框架，能系统性地揭示“未知未知”——即初始方案中不存在但具有创新潜力的新颖解决路径。U2F包含两个核心组件：（1）用于发掘与整合潜在解决方案的“发现-探索-集成”智能体系统；（2）涵盖三个维度的认知增强机制：跨领域类比推理、逆向思维与外部验证，这些机制通过策略性重构与拓展传统解决方案边界来提升创新能力。在从真实工程任务中筛选的218个现实软件赋能案例中应用U2F后，取得显著改进：人类专家报告整体新颖性提升14%、语义新颖性提高51%，同时保持稳定的可行性评分（4.02/5.0），该结果得到基于LLM的评估器验证。这些发现凸显了将不确定性作为软件工程创新催化剂的重要潜力。
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