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This paper introduces TopoPlanner, a novel framework that enhances LLM agent task planning by modeling tool dependencies as cellular workflow complexes rather than simple graphs. By leveraging topological structures to handle complex patterns like verification loops and branch merging, the approach demonstrates consistent performance gains across multiple benchmarks and local model backbones. It represents a significant methodological advance in orchestrating autonomous agents for real-world, non-linear workflows.
Topology-Consistent Task Planning over Cellular Workflow Complexes for LLM-based Agents
arXiv:2610.07004v1 Announce Type: new Abstract: Task planning for LLM agents requires workflows that satisfy both user intent and complex sub-task dependencies. While existing planners work well for sequential or directed acyclic graph (DAG)-like structures, they struggle with workflow patterns such as verification-correction loops, convergent branch merging, and reusable intermediate states that arise naturally in real-world tool orchestration. We present TopoPlanner, a topology-consistent planning framework that lifts tool dependency graphs into cellular workflow complexes and uses them as topologyaware context for LLM tool planning. TopoPlanner retrieves a request-relevant closed subcomplex through cosheaf-consistent cellular retrieval, performs multidimensional structural reasoning over the retrieved topology, and interfaces the resulting cellular representation with the planner LLM for tool-sequence generation. Experiments on four tool-planning benchmarks with topology-guided loop, merge, and loop-merge workflows show consistent improvements over prompt-based and graph-enhanced baselines across different local LLM backbones.
Abstract:Task planning for LLM agents requires workflows that satisfy both user intent and complex sub-task dependencies. While existing planners work well for sequential or directed acyclic graph (DAG)-like structures, they struggle with workflow patterns such as verification-correction loops, convergent branch merging, and reusable intermediate states that arise naturally in real-world tool orchestration. We present TopoPlanner, a topology-consistent planning framework that lifts tool dependency graphs into cellular workflow complexes and uses them as topologyaware context for LLM tool planning. TopoPlanner retrieves a request-relevant closed subcomplex through cosheaf-consistent cellular retrieval, performs multidimensional structural reasoning over the retrieved topology, and interfaces the resulting cellular representation with the planner LLM for tool-sequence generation. Experiments on four tool-planning benchmarks with topology-guided loop, merge, and loop-merge workflows show consistent improvements over prompt-based and graph-enhanced baselines across different local LLM backbones.
Artificial Intelligence (cs.AI)
arXiv:2610.07004 [cs.AI]
(or arXiv:2610.07004v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.07004 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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