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KGMACG is evaluated on three industrial-scale case studies against six state-of-the-art multi-agent baselines: MetaGPT, AutoGen, CAMEL, CrewAI, ChatDev and CodeAgent and indicates that KGMACG advances the automation of application-level software development.
DeepDiscovery is presented, a task-level repository-understanding method for large industrial codebases that uses a two-stage \textit{Location--Inference} framework to localize high-confidence task anchors and recover broader task-relevant context over multi-relational repository structure under budget constraints.
SemRef, a framework that combines LLMs with dependency analysis to automatically refine architectures recovered by existing architecture recovery tools, is introduced, which improves accuracy across various metrics and enhances both the accuracy and the comprehension of recovered architectures.
Findings indicate that cross-model collaboration offers a practical and parameter-efficient alternative to scaling up monolithic models for code generation and maintains competitive accuracy when only 20% of test cases are available for diagnostic feedback.
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