| --- |
| license: cc-by-sa-4.0 |
|
|
| language: |
| - eng |
| language_details: >- |
| en |
| tags: |
| - task-completion |
| - agents |
| - llm-agents |
| - dialogue-system |
| - task-oriented-dialogue-system |
| - user-persona |
| - user-simulator |
| - role-playing |
| task_categories: |
| - text-generation |
| configs: |
| - config_name: all |
| data_files: |
| - split: test |
| path: output_jsonl/*.jsonl |
| - config_name: multi_8domain |
| data_files: |
| - split: test |
| path: output_jsonl/multi_8domain.jsonl |
| - config_name: multi_11domain |
| data_files: |
| - split: test |
| path: output_jsonl/multi_11domain.jsonl |
| - config_name: attraction |
| data_files: |
| - split: test |
| path: output_jsonl/attraction.jsonl |
| - config_name: bar |
| data_files: |
| - split: test |
| path: output_jsonl/bar.jsonl |
| - config_name: cafe |
| data_files: |
| - split: test |
| path: output_jsonl/cafe.jsonl |
| - config_name: cruise |
| data_files: |
| - split: test |
| path: output_jsonl/cruise.jsonl |
| - config_name: dessert |
| data_files: |
| - split: test |
| path: output_jsonl/dessert.jsonl |
| - config_name: flight |
| data_files: |
| - split: test |
| path: output_jsonl/flight.jsonl |
| - config_name: food&dining |
| data_files: |
| - split: test |
| path: output_jsonl/food&dining.jsonl |
| - config_name: hotel |
| data_files: |
| - split: test |
| path: output_jsonl/hotel.jsonl |
| - config_name: kayak |
| data_files: |
| - split: test |
| path: output_jsonl/kayak.jsonl |
| - config_name: liveshow |
| data_files: |
| - split: test |
| path: output_jsonl/liveshow.jsonl |
| - config_name: vehicle |
| data_files: |
| - split: test |
| path: output_jsonl/vehicle.jsonl |
| - config_name: multi_2domain_attraction_cafe |
| data_files: |
| - split: test |
| path: output_jsonl/multi_2domain_attraction_cafe.jsonl |
| - config_name: multi_2domain_attraction_dessert |
| data_files: |
| - split: test |
| path: output_jsonl/multi_2domain_attraction_dessert.jsonl |
| - config_name: multi_2domain_food&dining_hotel |
| data_files: |
| - split: test |
| path: output_jsonl/multi_2domain_food&dining_hotel.jsonl |
| - config_name: multi_2domain_hotel_vehicle |
| data_files: |
| - split: test |
| path: output_jsonl/multi_2domain_hotel_vehicle.jsonl |
| - config_name: multi_2domain_liveshow_food&dining |
| data_files: |
| - split: test |
| path: output_jsonl/multi_2domain_liveshow_food&dining.jsonl |
| - config_name: multi_2domain_vehicle_attraction |
| data_files: |
| - split: test |
| path: output_jsonl/multi_2domain_vehicle_attraction.jsonl |
| - config_name: multi_3domain_attraction_food&dining_cafe |
| data_files: |
| - split: test |
| path: output_jsonl/multi_3domain_attraction_food&dining_cafe.jsonl |
| - config_name: multi_3domain_flight_hotel_bar |
| data_files: |
| - split: test |
| path: output_jsonl/multi_3domain_flight_hotel_bar.jsonl |
| - config_name: multi_3domain_hotel_attraction_vehicle |
| data_files: |
| - split: test |
| path: output_jsonl/multi_3domain_hotel_attraction_vehicle.jsonl |
| - config_name: multi_3domain_vehicle_food&dining_hotel |
| data_files: |
| - split: test |
| path: output_jsonl/multi_3domain_vehicle_food&dining_hotel.jsonl |
| - config_name: multi_4domain_flight_hotel_attraction_vehicle |
| data_files: |
| - split: test |
| path: output_jsonl/multi_4domain_flight_hotel_attraction_vehicle.jsonl |
| - config_name: multi_4domain_hotel_vehicle_food&dining_bar |
| data_files: |
| - split: test |
| path: output_jsonl/multi_4domain_hotel_vehicle_food&dining_bar.jsonl |
| --- |
| |
| # 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](https://arxiv.org/abs/2606.11070). |
|
|
| ## Dataset structure |
|
|
| Each row has the following columns: |
|
|
| - `domain_id`: the domain name concatenated with a row index (e.g. `attraction-0`). |
| - `domain`: the domain name, matching the source folder name. |
| - `user_template`: the persona, user profile, and goal used to drive the user simulator. |
| - `benchmark_success_criteria`: a JSON-stringified object describing the exact-match tool calls and LLM-as-judge metrics used to score the conversation. |
|
|
| ## Domains |
|
|
| 11 single-domain configs: `attraction`, `bar`, `cafe`, `cruise`, `dessert`, `flight`, `food&dining`, `hotel`, `kayak`, `liveshow`, `vehicle`. |
|
|
| 14 multi-domain configs, prefixed with `multi_Xdomain_` where `X` is the number of entities combined: `multi_2domain_attraction_cafe`, `multi_2domain_attraction_dessert`, `multi_2domain_food&dining_hotel`, `multi_2domain_hotel_vehicle`, `multi_2domain_liveshow_food&dining`, `multi_2domain_vehicle_attraction`, `multi_3domain_attraction_food&dining_cafe`, `multi_3domain_flight_hotel_bar`, `multi_3domain_hotel_attraction_vehicle`, `multi_3domain_vehicle_food&dining_hotel`, `multi_4domain_flight_hotel_attraction_vehicle`, `multi_4domain_hotel_vehicle_food&dining_bar`, `multi_8domain`, `multi_11domain`. |
|
|
| ## Results |
|
|
|  |
|
|
|  |
|
|
| ## Code |
| Please refer to the official implementation repository: |
| [https://github.com/CapitalOne-Research/t1-bench](https://github.com/CapitalOne-Research/t1-bench) |
|
|
| ## Contact |
|
|
| E-mail: [Genta Indra Winata](mailto:genta.winata@capitalone.com) or [Amartya Chakraborty](mailto:amartya.chakraborty@capitalone.com). |
|
|
| ## License |
| The dataset is licensed under CC-BY-SA 4.0. |
|
|
| ## Citation |
|
|
| If you find this dataset useful, please cite the following work |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
