Huozhi Zhou

GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL

GRAIN models reasoning as a semantic parsing and tool-execution pipeline, guided by a Structure Invariance Reward, which forces the LLM to learn robust text-to-structure mappings rather than memorizing linguistic artifacts.