TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only

ArXi:2604.19070v1 Announce Type: cross Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure without task-specific supervision. While graph neural networks rely on fixed label spaces and supervised objectives, recent large language model (LLM)-based approaches often overlook graph context or depend on distillation from larger models, limiting generalisation. We propose TRN-R1-Zero, a post-