gentaiscool/test2

T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains

T1-Bench is a high-fidelity benchmark for evaluating task-completion and role-playing agents across 25 domains, including 11 single-domain and 14 multi-domain settings. It provides 76 tools and extensive human annotations, enabling systematic evaluation of agents in realistic, policy-grounded multi-domain interactions with natural user–assistant role-playing.

T1-Bench is a fully automated benchmark for evaluating the tool-calling capabilities of conversational AI agents across diverse service domains in task-oriented settings. The framework simulates end-to-end user–agent interactions without requiring human intervention at inference time, where a User Agent generates realistic customer utterances conditioned on predefined task goals and a tool-augmented Assistant Agent responds by invoking domain-specific tools/APIs and producing outputs grounded in tool results, prior conversational context, and domain-specific policies. Designed to capture the sequential and interactive nature of real-world service workflows across multi-domain scenarios, T1-Bench requires agents to maintain conversational state, reason over prior tool outputs, and execute multi-step operations such as search, filtering, booking, modification, and cancellation. Because all interactions are grounded in deterministic datasets and executable tools, the benchmark enables reproducible and fine-grained evaluation of agent behavior, tool-use decisions, and task completion performance.

The paper is accepted at EMNLP Findings 2026. Check our ArXiv.

Results

Code

Please refer to the official implementation repository:
https://github.com/CapitalOne-Research/t1-bench

Contact

E-mail: Genta Indra Winata or Amartya Chakraborty.

License

The dataset is licensed under CC-BY-SA 4.0.

Citation

If you find this dataset useful, please cite the following work

@article{winata2026t1,
  title={T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains},
  author={Winata, Genta Indra and Chakraborty, Amartya and Lin, Yuzhen and Rao, Swasthi P and Siingh, Shikhhar and Lu, Houhan and Bathaee, Nadia and Hatwar, Sriharsha and Dashore, Paresh and Jain, Anmol and others},
  journal={arXiv preprint arXiv:2606.11070},
  year={2026}
}