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

Source: arXiv - 2606.11070v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.CL
• cs.AI

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.CL.

Authors

• 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
• Shi-Xiong Zhang

Paper Information

• arXiv ID: 2606.11070v1
• Categories: cs.CL, cs.AI
• Published: June 9, 2026
• PDF: Download PDF