\vspace*{-5pt}
\section{Main Results}
\label{sec:result}
\vspace*{-3pt}
\input{tables/main_table}
\vspace*{-3pt}
\begin{figure}[htbp]
\centering
\vspace{0pt}
\includegraphics[width=\linewidth]{figs/dependency.pdf}
\captionof{figure}{Illustration of dependencies in the repository generated by Qwen3-Coder on \texttt{MLKit-Py}, showing (1) the repository skeleton at the folder/file level, (2) inter-module data flows, and (3) class and function dependencies.}
\label{fig:dependency}
\end{figure}

\paragraph{\graph{} enables richer functionality and larger repositories.}
\ours{} demonstrates that \graph{}–guided planning yields repositories of substantially greater scale, diversity, and novelty than existing approaches. On RepoCraft, it achieves up to 81.5\% coverage with \texttt{o3-mini}, representing a 27.3\% absolute improvement over the strongest baseline (Claude Code at 54.2\%). Beyond covering the required functionality, \ours{} also exhibits strong innovation, attaining novelty rates of 11–13\% with over 100 new functionalities, whereas most baselines contribute fewer than 10. In terms of repository size, \ours{} with \texttt{Qwen3-Coder} generates 36K LOC and 445K tokens, corresponding to 3.9× the code scale of Claude Code and about 68× that of other baselines. Among these approaches, \ours{} is the closest to human-developed Gold Projects, underscoring that \graph{} serves as the key structured representation for building repositories that are larger, more diverse, and closer to real-world software complexity.
\vspace*{-2pt}
\paragraph{\graph{} enhances reasoning consistency and structural fidelity.}
Beyond scale, \ours{} delivers substantially higher correctness and stability. To ensure reliability, we first validate the automatic localization and validation pipeline on human-developed Gold Projects, where it achieves 81.0\% pass rate and 92.0\% voting agreement, establishing the ceiling under our test harness. Under the same protocol, \ours{} attains a 69.7\% pass rate with \texttt{o3-mini}, an absolute improvement of 35.8\% compared to the strongest baseline (Claude Code at 33.9\%). These results indicate that \graph{} serves as a structured reasoning representation that enforces modular boundaries and functional contracts, thereby supporting coherent planning and yielding repositories that more faithfully realize intended specifications.
\vspace*{-2pt}
\paragraph{\graph{} induces complex data flows and dependencies.}
To illustrate the capacity of \graph{}–guided planning for generating complex repositories, we visualize \ours{} with \texttt{Qwen3-Coder} on the \texttt{MLKit-Py} task. At the file level, \graph{} organizes a coherent folder hierarchy; at the module level, inter-module flows define execution pipelines from \texttt{data\_lifecycle} through \texttt{clustering} and \texttt{models} to \texttt{evaluation}; and at the function level, inheritance and invocation edges capture intricate class interactions. These results show that \graph{} induces layered dependencies and coordinated execution, enabling repositories with both structural complexity and internal coherence.