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 principled taxonomy, evaluation procedure, and unified algorithm space for offline RL.

COGNAC is a benchmark suite for evaluating cooperative multi-agent reinforcement learning on graph-structured control tasks, highlighting the strengths of decentralized approaches.