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D coordinates vi=(xi,yi)∈Viv^{i}=(x^{i},y^{i})\in V^{i}. All agents start from the same node and, following their policies πi\pi^{i}, select neighboring nodes as next waypoints and move along straight-line edges at constant speed. Because decisions are executed in parallel, agents may reach waypoints at different times. During execution, each agent ii builds a trajectory ψi={ψ1i,…,ψnii},ψji∈ℝ2,(ψji,ψj+1i)∈Ei\psi^{i}=\{\psi^{i}_{1},\ldots,\psi^{i}_{n_{i}}\},\psi^{i}_{j}\in\mathbb{R}^{2},\ (\psi^{i}_{j},\psi^{i}_{j+1})\in E^{i}, with path-length cost C(ψi)=∑j=1ni−
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Forget end-to-end training: this modular framework uses a symbolic KB, VLM, and LLM to solve Rubik's Cube with 79% success, outperforming traditional methods in both simulation and real-world settings.