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 re
The proliferation of LLM agents in diverse global applications necessitates benchmarks that reflect real-world multilingual complexities, moving beyond the traditionally monolingual focus.
A truly robust and globally applicable AI agent ecosystem requires capabilities that seamlessly handle multiple languages, impacting user adoption and market reach for such systems.
The focus of LLM agent development shifts towards integrated multilingual support, moving agentic systems closer to real-world applicability in diverse linguistic contexts.
- · AI Agent developers (multilingual capability)
- · Global businesses with diverse linguistic operations
- · Non-English speaking markets
- · Monolingual LLM agent platforms
- · AI models without robust multilingual integration
PolyWorkBench provides a crucial benchmark for evaluating and improving multilingual long-horizon LLM agents.
This will accelerate the development of more capable and globally relevant AI agents, expanding their utility and market penetration beyond English-centric applications.
The integration of multilingual AI agents into various sectors could further blur geographical and linguistic barriers in automated workflows, increasing the demand for localized AI solutions.
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Read at arXiv cs.AI