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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ValueError
Message:      Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/meituan-longcat/VitaBench@b7b05b94d3f685a0fd33acb188a60668680d41af/cross_domain/tasks.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 186, in _generate_tables
                  raise ValueError(
              ValueError: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/meituan-longcat/VitaBench@b7b05b94d3f685a0fd33acb188a60668680d41af/cross_domain/tasks.json.

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🌱VitaBench: Benchmarking LLM Agents
with Versatile Interactive Tasks

πŸ“ƒ Paper β€’ 🌐 Website β€’ πŸ† Leaderboard β€’ πŸ› οΈ Code β€’ πŸ€— Dataset

πŸ”” News

πŸ“– Introduction

In this paper, we introduce VitaBench, a challenging benchmark that evaluates agents on versatile interactive tasks grounded in real-world settings. Drawing from daily applications in food delivery, in-store consumption, and online travel services, VitaBench presents agents with the most complex life-serving simulation environment to date, comprising 66 tools. Through a framework that eliminates domain-specific policies, we enable flexible composition of these scenarios and tools, yielding 100 cross-scenario tasks (main results) and 300 single-scenario tasks. Each task is derived from multiple real user requests and requires agents to reason across temporal and spatial dimensions, utilize complex tool sets, proactively clarify ambiguous instructions, and track shifting user intent throughout multi-turn conversations.

Moreover, we propose a rubric-based sliding window evaluator, enabling robust assessment of diverse solution pathways in complex environments and stochastic interactions. Our comprehensive evaluation reveals that even the most advanced models achieve only 30% success rate on cross-scenario tasks, and less than 50% success rate on others. Overall, we believe VitaBench will serve as a valuable resource for advancing the development of AI agents in practical real-world applications.

The name β€œVita” derives from the Latin word for β€œLife”, reflecting our focus on life-serving applications.

overall_performance

🌱 Benchmark Details

VitaBench provides an evaluation framework that supports model evaluations on both single-domain and cross-domain tasks through flexible configuration. For cross-domain evaluation, simply connect multiple domain names with commasβ€”this will automatically merge the environments of the specified domains into a unified environment.

Statistics of databases and environments:

Cross-Scenarios
(All domains)
Delivery In-store OTA
Databases
   Service Providers 1,324 410 611 1,437
   Products 6,946 788 3,277 9,693
   Transactions 447 48 28 154
API Tools
   Write 27 4 9 14
   Read 33 10 10 19
   General 6 6 5 5
Tasks 100 100 100 100

πŸ› οΈ Environment

VitaBench provides an evaluation framework that supports model evaluations on both single-domain and cross-domain tasks through flexible configuration. For cross-domain evaluation, simply connect multiple domain names with commasβ€”this will automatically merge the environments of the specified domains into a unified environment. Please visit our GitHub repository vitabench for more detailed instructions.

πŸ”Ž Citation

If you find our work helpful or relevant to your research, please kindly cite our paper:

@article{he2025vitabench,
      title={VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications}, 
      author={He, Wei and Sun, Yueqing and Hao, Hongyan and Hao, Xueyuan and Xia, Zhikang and Gu, Qi and Han, Chengcheng and Zhao, Dengchang and Su, Hui and Zhang, Kefeng and Gao, Man and Su, Xi and Cai, Xiaodong and Cai, Xunliang and Yang, Yu and Zhao, Yunke},
      journal={arXiv preprint arXiv:2509.26490},
      year={2025}
}

πŸ“œ License

This project is licensed under the MIT License - see the LICENSE file for details.

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