Topology-Consistent Task Planning over Cellular Workflow Complexes for LLM-based Agents

Task planning for LLM agents requires workflows that satisfy both user intent and complex sub-task dependencies. While existing planners excel at sequential or directed acyclic graph (DAG)-like structures, they struggle with high-dimensional dependencies—such as verification-correction loops and convergent branch merging—inherent in real-world tool orchestration. We present TopoPlanner, a framework that lifts tool dependency graphs into cellular workflow complexes and uses them as topology-aware context for LLM tool planning. Specifically, TopoPlanner attaches 2-cells to fundamental cycles, retrieves a request-relevant closed subcomplex with cosheaf-consistency regularization, performs multi-dimensional structural reasoning over 0-, 1-, and 2-cells, and interfaces the resulting cellular representation with the planner LLM through a topology-guided conditioning layer. Evaluated on four tool-planning benchmarks, including the three TaskBench domains and ToolBench, all extended with curated cyclic-dependency cases, TopoPlanner consistently outperforms prior baselines, showing significant gains on non-linear workflows. Code and datasets are available at: https://anonymous.4open.science/r/TopoPlaner-AE05.