Problem
Research questions and friction points this paper is trying to address.
multilingual agents
state preservation
cross-language performance
culturally grounded scenarios
WorldBench: Culturally Grounded Benchmark for Multilingual Agents
🤖 AI Summary
为解决多语言环境下状态保持和实际应用问题，提出WorldBench基准测试，通过1600个跨语言任务评估模型性能。
📝 Abstract
Despite the growing use of LLM-powered agents to solve multi-step tasks in complex environments, existing benchmarks rarely test state preservation, performance across languages, and application to realistic, grounded scenarios. To address these concerns, we present WorldBench: a comprehensive, multilingual benchmark of genuine, persona-grounded everyday workflows, where agents can act in a sandbox via structured actions. WorldBench comprises 1,600 tasks across seven languages and eight cultures, filtered and refined through feedback from human annotators with language- and culture-specific expertise. For evaluation, we extend metrics from previous works and introduce Constrained Task Success (CTS), which combines natural language instructions and testbeds to score task completion, minimal modification, and other complementary metrics through deterministic and LLM-as-a-Judge evaluations. Our experiments show that frontier models reach only 49.2% CTS, with all models demonstrating large gaps between correctness and environment preservation. We thereby show that current agents remain brittle in multilingual, agentic scenarios, especially for long-horizon tasks and under state-preservation constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
multilingual benchmark
state preservation
Constrained Task Success (CTS)
cultural grounding
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