Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
With Qwen3-8B on BEHAVIOR-1K across 100 long-horizon tasks; multi-task success rises from 19.9 to 92.6 percent across 500 multi-task instructions.02The graph predicts consequences before execution. Violations are detected and repaired directly when the correction follows from the world model, and LLM replanning is reserved for errors that need semantic reasoning.03Belief reasoning reorders subtasks. Reasoning over distributions of possible object locations reduces expected search cost and cuts travel distance about 5.4 percent against a static variant.04The gain holds for a compact model.
- ▪With Qwen3-8B on BEHAVIOR-1K across 100 long-horizon tasks; multi-task success rises from 19.9 to 92.6 percent across 500 multi-task instructions.02The graph predicts consequences before execution.
- ▪Violations are detected and repaired directly when the correction follows from the world model, and LLM replanning is reserved for errors that need semantic reasoning.03Belief reasoning reorders subtasks.
- ▪Reasoning over distributions of possible object locations reduces expected search cost and cuts travel distance about 5.4 percent against a static variant.04The gain holds for a compact model.
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| Original publisher | Dair |
| Canonical URL | https://academy.dair.ai/papers/gavel-graph-world-models-for-verified-and-efficient-long-horizon-llm-task-planni-2609.19315 |
| Publication time | Sun, 20 Sep 2026 18:04:13 +0000 |
| Retrieval time | 2026-09-20T18:13:46.875Z |
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Agents · RoboticsGAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task PlanningRuiyang Wang, Hao-Lun Hsu, Swarajh Mehta, Jiwoo Kim, Zhihao Dou, Miroslav PajicChat with PaperFirst pageThe curator’s takeRuiyang Wang and colleagues present GAVEL, which verifies and repairs long-horizon LLM robot plans against an explicit graph world model holding object relations, action preconditions and effects, and probabilistic beliefs over unobserved locations.Ask this paperQuestion about this paperAsk in Paper ChatKey points01Single-task success goes from 41.2 to 91.8 percent. With Qwen3-8B on BEHAVIOR-1K across 100 long-horizon tasks; multi-task success rises from 19.9 to 92.6 percent across 500 multi-task instructions.02The graph predicts consequences before execution.
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