\section{Related Work}
\label{sec:related}

\textbf{Repository Generation.} Research has transitioned from localized file completion~\citep{wang2025epicoder, li2023starcoder} to systemic workflows that emphasize architectural coherence. Multi-agent frameworks like MetaGPT~\citep{hong2024metagpt} and paper-to-code systems~\citep{seo2025paper2code, lin2025autop2c} utilize role-based abstraction to manage complexity. More recently, Commit0~\citep{zhao2024commit0} introduces a library-level reconstruction paradigm starting from near-zero implementations, while terminal-based agents such as Claude Code~\citep{anthropic2025claude_code} and Gemini CLI~\citep{google2025gemini_cli} facilitate iterative "build-test-fix" cycles in real-world environments. RPG~\citep{luo2025rpg_zerorepo} advances this by utilizing structured planning graphs to ground generative intent in execution dependencies, ensuring that synthesized repositories remain topologically valid.

\textbf{Repository Understanding.} Current paradigms shift from passive retrieval to active, structure-aware exploration. Early iterative methods~\citep{zhang2023repocoder, xia2024agentless} have been augmented by graph-guided navigation frameworks~\citep{ouyang2024repograph, liu2025codexgraph}. To address high-density codebases, LocAgent~\citep{chen2025locagent} and KGCompass~\citep{yang2025kgcompass} leverage explicit dependency schemas and knowledge graphs to prune the search space. Furthermore, RepoHyper~\citep{phan2025repohyper} explores long-context summarization for global semantic grasp, while agents like OrcaLoca~\citep{yu2025orcaloca} and CoSIL~\citep{jiang2025cosil} integrate dynamic execution signals for precise fault localization. These systems increasingly rely on "browse-edit-run" loops~\citep{yang2024swe} and are rigorously benchmarked on real-world issue-solving datasets like SWE-bench Verified~\citep{jimenez2023swe}. Despite these advancements, existing methods often suffer from fragmented representations: dependency graphs lack semantic depth, while semantic retrievers lack topological precision. \ours{} bridges this gap by coupling dense semantic features with structural constraints, enabling fine-grained localization that is both intent-driven and execution-grounded.