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NSDL: Neuro-Symbolic Division of Labor for Multi-Robot Task Planning
arXiv:2611.16671, 2026 · Under review at ICRA 2027
A small vision-language model judges the scene and writes a plan sketch; a deterministic solver expands it into an executable joint plan for a heterogeneous robot team. The 7B model reaches 97.0% out-of-distribution accuracy on VIKI-L2.
Recommended citation: Xincheng Tan, Ke Wang, Yining Di, Minfeng Zhu†. NSDL: Neuro-Symbolic Division of Labor for Multi-Robot Task Planning. arXiv preprint arXiv:2611.16671, 2026.
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Refuse and Recompute: What a Classical Planner Fixes in LLM Agents
Manuscript, 2026
LLM agents that act over long horizons under partial observability lose mostly in two ways: an action after which the goal is unreachable, and acting without knowing what must still happen. A classical planner addresses both from the current state, with a guard that refuses such actions and by recomputing the remaining subgoals.
Recommended citation: Xincheng Tan, Ke Wang, Minfeng Zhu†. Refuse and Recompute: What a Classical Planner Fixes in LLM Agents. Manuscript, 2026.