TBNAI
Topology-Consistent Task Planning over Cellular Workflow Complexes for LLM-based Agents
Publication: cs.AI updates on arXiv.org
Author: Sen Zhao, Jia Tang, Ruiqi Kong, Zuyu Zhang, Lifeng Shen, Ding Zou, Xinyu He, Xu Zhang, Junwei Han
Source published: 07/10/2026, 04:00:00 UTC
Briefing created: 26/10/2026, 04:03:55 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 02:18:24 UTC.
Briefings
This article introduces TopoPlanner, a framework for LLM-based agents that uses cellular workflow complexes to handle complex tool dependencies. Experiments on four benchmarks show consistent improvements over prompt-based and graph-enhanced baselines across different local LLM backbones.
Key points
- TopoPlanner lifts tool dependency graphs into cellular workflow complexes to serve as topology-aware context for LLM tool planning.
- The framework retrieves a request-relevant closed subcomplex via cosheaf-consistent cellular retrieval and performs multidimensional structural reasoning over the retrieved topology.
Context
Existing planners struggle with workflow patterns like verification-correction loops and convergent branch merging. TopoPlanner addresses this by using cellular workflow complexes as topology-aware context for LLM tool planning.
Limitations
Results are limited to four tool-planning benchmarks; improvements are shown across different local LLM backbones but specific performance metrics per backbone are not detailed.
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