Code Graph Models (CGMs) innovatively integrate both semantic and structural information from code repositories into LLMs, enabling effective repository-level coding tasks without relying on agents or closed-source models.

We propose a co-evolving reinforcement learning method that jointly optimizes the coder and unit tester without relying on ground-truth code supervision.