Large language model (LLM) agents have recently shown strong performance on repository-level issue resolution, but existing systems are almost exclusively designed for Python and rely heavily on lexical retrieval and shallow code navigation. These approaches transfer poorly to C++ projects, where overloaded identifiers, nested namespaces, template instantiations, and deep control-flow structures make context retrieval and fault localization substantially more difficult. As a result, state-of-the-art Python-oriented agents show a drastic performance drop on the C++ subset of MultiSWE-bench. We introduce INFCODE-C++, the first C++-aware autonomous system for end-to-end issue resolution. The system combines two complementary retrieval mechanisms -- semantic code-intent retrieval and deterministic AST-structured querying -- to construct accurate, language-aware context for repair.These components enable precise localization and robust patch synthesis in large, statically typed C++ repositories. Evaluated on the \texttt{MultiSWE-bench-CPP} benchmark, INFCODE-C++ achieves a resolution rate of 25.58\%, outperforming the strongest prior agent by 10.85 percentage points and more than doubling the performance of MSWE-agent. Ablation and behavioral studies further demonstrate the critical role of semantic retrieval, structural analysis, and accurate reproduction in C++ issue resolution. INFCODE-C++ highlights the need for language-aware reasoning in multi-language software agents and establishes a foundation for future research on scalable, LLM-driven repair for complex, statically typed ecosystems.

翻译：大语言模型（LLM）智能体近期在仓库级问题解决任务中展现出强大性能，但现有系统几乎专为Python设计，且严重依赖词法检索与浅层代码导航。这些方法在迁移至C++项目时表现不佳，因为重载标识符、嵌套命名空间、模板实例化以及深层控制流结构使得上下文检索与故障定位难度显著增加。因此，面向Python的最先进智能体在MultiSWE-bench的C++子集上出现性能急剧下降。本文提出INFCODE-C++，首个具备C++感知能力的端到端问题解决自主系统。该系统结合两种互补的检索机制——语义代码意图检索与确定性AST结构化查询——以构建精确、语言感知的修复上下文。这些组件能够在大型静态类型C++仓库中实现精准定位与稳健补丁合成。在\\texttt{MultiSWE-bench-CPP}基准测试中，INFCODE-C++达到25.58%的解决率，较先前最强智能体提升10.85个百分点，且性能超过MSWE-agent两倍以上。消融实验与行为研究进一步验证了语义检索、结构分析与精确复现在C++问题解决中的关键作用。INFCODE-C++凸显了多语言软件智能体中语言感知推理的必要性，并为未来面向复杂静态类型生态系统的可扩展LLM驱动修复研究奠定了基础。