An Actionable Diagnosis of Multilingual, Multi-Agent Planning Failures

Abstract:Multilingual multi-agent systems exhibit substantial degradation beyond English, yet prior work rarely identifies how task-critical information is lost when user requests are converted into executable plans. We study the planner in a multi-agent system as the request-to-action interface and derive an actionable taxonomy of planning-grounding failures from failed real-world task executions. LLM-based analysis shows that these failures constitute an increasing share of unsuccessful executions as language-resource availability declines, with the strongest effects in low-resource languages. To test whether the taxonomy supports mitigation, we introduce TART, Taxonomy-Guided Actionable Representation, that makes the taxonomy's key aspects explicit to the planner and downstream sub-agents. Across multiple languages, three LLM backbones, two datasets, and two agentic configurations, TART consistently improves performance. On multilingual GAIA, it raises a state-of-the-art system's accuracy by 5.6 percentage points averaged across eleven languages spanning low- to high-resource settings.

Multiagent Systems (cs.MA); Computation and Language (cs.CL)

arXiv:2608.03735 [cs.MA]

(or arXiv:2608.03735v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2608.03735 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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