@inproceedings{winata-etal-2026-t1,
title = "{T}1-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
Tayal, Kshitij and
Lin, Xiuzhu and
Das, Anirban and
Sahu, Sambit and
Zhang, Shi-Xiong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1929/",
pages = "41097--41145",
ISBN = "979-8-89176-462-0",
abstract = "Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However, existing benchmarks remain limited in task complexity, realism, and domain diversity, and often fail to capture interactions that span multiple domains, limiting their ability to evaluate agents in realistic multi-step settings that require sustained reasoning and coordination. To address these limitations, we introduce T1-Bench, a high-fidelity, comprehensive benchmark for evaluating agentic systems in realistic customer-facing, multi-domain environments, featuring interleaved scenarios that require structured reasoning across multi-turn user-assistant interactions and substantially increasing both compositional complexity and evaluative rigor across 25 domains of varying difficulty. We evaluate T1-Bench using 12 proprietary and open-weight models, providing a reproducible and standardized framework for assessing agent behavior, tool utilization, and conversational quality in complex, multi-step environments. We further complement automatic evaluation with human judgments to strengthen the assessment of qualitative performance. Overall, T1-Bench substantially advances prior benchmarks by increasing task complexity, interaction depth, and domain coverage in simulated multi-domain environments. To facilitate future research on agentic systems, we will publicly release data and evaluation code as open source."
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<abstract>Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However, existing benchmarks remain limited in task complexity, realism, and domain diversity, and often fail to capture interactions that span multiple domains, limiting their ability to evaluate agents in realistic multi-step settings that require sustained reasoning and coordination. To address these limitations, we introduce T1-Bench, a high-fidelity, comprehensive benchmark for evaluating agentic systems in realistic customer-facing, multi-domain environments, featuring interleaved scenarios that require structured reasoning across multi-turn user-assistant interactions and substantially increasing both compositional complexity and evaluative rigor across 25 domains of varying difficulty. We evaluate T1-Bench using 12 proprietary and open-weight models, providing a reproducible and standardized framework for assessing agent behavior, tool utilization, and conversational quality in complex, multi-step environments. We further complement automatic evaluation with human judgments to strengthen the assessment of qualitative performance. Overall, T1-Bench substantially advances prior benchmarks by increasing task complexity, interaction depth, and domain coverage in simulated multi-domain environments. To facilitate future research on agentic systems, we will publicly release data and evaluation code as open source.</abstract>
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%0 Conference Proceedings
%T T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains
%A Winata, Genta Indra
%A Chakraborty, Amartya
%A Lin, Yuzhen
%A Rao, Swasthi P.
%A Siingh, Shikhhar
%A Lu, Houhan
%A Bathaee, Nadia
%A Hatwar, Sriharsha
%A Dashore, Paresh
%A Jain, Anmol
%A Tayal, Kshitij
%A Lin, Xiuzhu
%A Das, Anirban
%A Sahu, Sambit
%A Zhang, Shi-Xiong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F winata-etal-2026-t1
%X Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However, existing benchmarks remain limited in task complexity, realism, and domain diversity, and often fail to capture interactions that span multiple domains, limiting their ability to evaluate agents in realistic multi-step settings that require sustained reasoning and coordination. To address these limitations, we introduce T1-Bench, a high-fidelity, comprehensive benchmark for evaluating agentic systems in realistic customer-facing, multi-domain environments, featuring interleaved scenarios that require structured reasoning across multi-turn user-assistant interactions and substantially increasing both compositional complexity and evaluative rigor across 25 domains of varying difficulty. We evaluate T1-Bench using 12 proprietary and open-weight models, providing a reproducible and standardized framework for assessing agent behavior, tool utilization, and conversational quality in complex, multi-step environments. We further complement automatic evaluation with human judgments to strengthen the assessment of qualitative performance. Overall, T1-Bench substantially advances prior benchmarks by increasing task complexity, interaction depth, and domain coverage in simulated multi-domain environments. To facilitate future research on agentic systems, we will publicly release data and evaluation code as open source.
%U https://aclanthology.org/2026.findings-emnlp.1929/
%P 41097-41145
Markdown (Informal)
[T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains](https://aclanthology.org/2026.findings-emnlp.1929/) (Winata et al., Findings 2026)
ACL
- Genta Indra Winata, Amartya Chakraborty, Yuzhen Lin, Swasthi P Rao, Shikhhar Siingh, Houhan Lu, Nadia Bathaee, Sriharsha Hatwar, Paresh Dashore, Anmol Jain, Kshitij Tayal, Xiuzhu Lin, Anirban Das, Sambit Sahu, and Shi-Xiong Zhang. 2026. T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 41097–41145, Budapest, Hungary. Association for Computational Linguistics.