PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorHongliang Li →
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
- Linked via arxiv authorYijin Liu →
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
- Linked via arxiv authorZhiwei Zhang →
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
- Linked via arxiv authorZihe Liu →
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
- Linked via arxiv authorXinyue Lou →
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
- Linked via arxiv authorJinan Xu →
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
- Linked via arxiv authorFandong Meng →
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
- Linked via arxiv authorKaiyu Huang →
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents