PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents

arXiv cs.AI Papers
multilingual benchmark llm-agents long-horizon evaluation workflow

Summary

Introduces PolyWorkBench, a benchmark for evaluating LLM agents on multilingual long-horizon workplace workflows across five domains, demonstrating significant performance degradation compared to monolingual settings.

arXiv:2607.06008v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong performance in long-horizon tasks that require planning, tool use, and interaction with external environments. However, most existing benchmarks implicitly assume a monolingual setting, where the entire execution process, including reasoning, tool invocation, and output generation, is conducted within a single language. In contrast, real-world applications often involve multilingual inputs and outputs within a unified workflow, yet the interaction between multilinguality and agentic execution remains underexplored. In this work, we introduce PolyWorkBench, a benchmark for evaluating LLM agents on multilingual long-horizon workplace workflows. PolyWorkBench consists of 67 tasks across five domains, including commerce, knowledge work, legal analysis, localization, and manufacturing, where agents must process heterogeneous multilingual inputs, perform iterative reasoning, invoke external tools, and produce structured outputs. To enable comprehensive evaluation, we propose a hybrid framework that combines structural grading, executable verification, and LLM-based semantic assessment. This design allows us to capture both functional correctness and linguistic consistency across complex workflows. Empirical results show that state-of-the-art LLM agents suffer significant performance degradation in multilingual workflow settings compared to monolingual counterparts. Our analysis suggests that multilinguality introduces compounding effects across reasoning and execution steps, highlighting the importance of jointly modeling language variation and procedural decision-making in agent evaluation.
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# PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
Source: [https://arxiv.org/abs/2607.06008](https://arxiv.org/abs/2607.06008)
[View PDF](https://arxiv.org/pdf/2607.06008)

> Abstract:Large language model \(LLM\) agents have shown strong performance in long\-horizon tasks that require planning, tool use, and interaction with external environments\. However, most existing benchmarks implicitly assume a monolingual setting, where the entire execution process, including reasoning, tool invocation, and output generation, is conducted within a single language\. In contrast, real\-world applications often involve multilingual inputs and outputs within a unified workflow, yet the interaction between multilinguality and agentic execution remains underexplored\. In this work, we introduce PolyWorkBench, a benchmark for evaluating LLM agents on multilingual long\-horizon workplace workflows\. PolyWorkBench consists of 67 tasks across five domains, including commerce, knowledge work, legal analysis, localization, and manufacturing, where agents must process heterogeneous multilingual inputs, perform iterative reasoning, invoke external tools, and produce structured outputs\. To enable comprehensive evaluation, we propose a hybrid framework that combines structural grading, executable verification, and LLM\-based semantic assessment\. This design allows us to capture both functional correctness and linguistic consistency across complex workflows\. Empirical results show that state\-of\-the\-art LLM agents suffer significant performance degradation in multilingual workflow settings compared to monolingual counterparts\. Our analysis suggests that multilinguality introduces compounding effects across reasoning and execution steps, highlighting the importance of jointly modeling language variation and procedural decision\-making in agent evaluation\.

## Submission history

From: Hongliang Li \[[view email](https://arxiv.org/show-email/19d056db/2607.06008)\] **\[v1\]**Tue, 7 Jul 2026 08:50:09 UTC \(2,256 KB\)

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