E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios
Large Language Models (LLMs) are increasingly deployed as agents that interact with stateful environments over multiple steps: gathering hidden information, composing tool calls, and committing state changes. We refer to this capability as multi-step tool use. Existing benchmarks have advanced tool-use agent evaluation, but often focus on isolated API calls, short trajectories, or settings that are difficult to scale or control. We introduce E-Bench, a fully synthetic benchmark with 323 state-changing tasks across three product domains: Honor of Kings, QQ Music, and Tencent Meeting. E-Bench decouples environment synthesis from task synthesis: graph-guided database filling builds reusable,...
| Authors | Weihuang Zheng, Tianyuan Zou, Eileen Ye et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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