Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
This work addresses the need for explainable and transferable link forecasting models in temporal graph reasoning, which is incremental as it builds on existing LLM-based graph reasoning methods.
The paper tackles the problem of forecasting future links on temporal graphs by introducing ReaL-TG, a reinforcement learning framework that fine-tunes LLMs to produce explainable predictions, and it shows that ReaL-TG-4B outperforms larger models like GPT-5 mini on ranking metrics while generating high-quality explanations.
Forecasting future links is a central task in temporal graph (TG) reasoning, requiring models to leverage historical interactions to predict upcoming ones. Traditional neural approaches, such as temporal graph neural networks, achieve strong performance but lack explainability and cannot be applied to unseen graphs without retraining. Recent studies have begun to explore using large language models (LLMs) for graph reasoning, but most of them are constrained to static graphs or small synthetic TGs and lack the evaluation of the quality of reasoning traces generated by LLMs. In this work, we present Reasoning-Enhanced Learning for Temporal Graphs (ReaL-TG), a reinforcement learning framework that fine-tunes LLMs to perform explainable link forecasting on real-world TGs. ReaL-TG uses outcome-based reward to encourage models to self-explore reasoning strategies from graph structure and to produce explanations that directly justify their predictions. To enable evaluation on LLM-generated reasoning traces, we propose a new evaluation protocol combining ranking metrics with an LLM-as-a-Judge system that assesses both the quality of reasoning and the impact of hallucinations. Experiments with ReaL-TG-4B, obtained by fine-tuning Qwen3-4B under our framework, show that it outperforms much larger frontier LLMs, including GPT-5 mini, on ranking metrics, while producing high-quality explanations confirmed by both the LLM judge and human evaluation.