Impressive paper showing the impact of a good harness. Improves Qwen3-8B from 41.2% to 91.8% on long-horizon robot tasks without any change …

Impressive paper showing the impact of a good harness. Improves Qwen3-8B from 41.2% to 91.8% on long-horizon robot tasks without any change to the model. The gain comes from the harness. GAVEL keeps an explicit graph world model holding object relations, action preconditions and effects, and probabilistic beliefs about where unobserved objects are. Before the robot executes an LLM-generated action, the graph predicts what that action would do. Violations get caught, and the ones whose fix follows directly from the world model get repaired without calling the model again. Only errors that need semantic reasoning go back to the LLM. On BEHAVIOR-1K across 500 multi-task instructions, success rises from 19.9% to 92.6%. Reasoning over the distribution of possible object locations also reorders the remaining subtasks and cuts travel distance about 5.4%. Why does it matter? Many long-horizon agent failures are state-tracking failures rather than reasoning failures, and a symbolic model sitting outside the LLM catches them cheaply. Paper: https://academy.dair.ai/papers/gavel-graph-world-models-for-verified-and-efficient-long-horizon-llm-task-planni-2609.19315

Source: DAIR.AI (X) | 2026-09-20

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