GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning
This work addresses the challenges of physical hallucination, poor generalization, and inadequate environmental perception in large language model (LLM)-driven embodied agents when performing long-horizon tasks. To overcome these limitations, the authors propose a synergistic planning framework that integrates task graphs and scene graphs. The task graph encodes structured prior knowledge to guide high-level planning, while the scene graph enables event-driven dynamic replanning, establishing a closed-loop perception and error-correction mechanism. This approach is the first to incorporate dual-graph structures into LLM-based planning pipelines, combining contextual prompting, GRPO-based reward design, and model fine-tuning. Evaluated on the ALFRED benchmark, the method achieves state-of-the-art performance, significantly improving task success rates under both zero-shot and few-shot settings and demonstrating strong out-of-distribution generalization on unseen long-horizon tasks.