GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning

2026-08-08

AI Summary

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.

Abstract

Embodied agents using LLM-based planners often struggle with physical hallucinations, poor generalization to long-horizon tasks, and lack of environmental awareness. We propose GraphThink, a novel framework that integrates a task graph to provide structured knowledge for robust planning and a scene graph to maintain environmental memory for event-driven replanning. Specifically, the task graph guides LLM thinking through contextual prompting and iterative refinement, effectively mitigating planning hallucinations. Furthermore, within the GRPO framework, the task graph offers delicate reward design to train the LLM planner, enhancing long-horizon planning capabilities and improving generalization. Finally, an event-driven replanning module, powered by the scene graph, enables closed-loop environment awareness and error correction. GraphThink achieves state-of-the-art performance on the ALFRED benchmark. In particular, our high-level planner surpasses leading API-based LLMs on both the validation set and held-out long-horizon tasks, underscoring its robust zero-shot and few-shot capabilities. Additional evaluations further demonstrate strong out-of-distribution generalization to novel tasks and environments.

Problem

• physical hallucinations
• long-horizon tasks
• environmental awareness
• generalization
• embodied task planning

Innovation

• GraphThink
• task graph
• scene graph
• long-horizon planning
• embodied AI