Large Language Models (LLMs) have demonstrated impressive capabilities as intelligent agents capable of solving complex problems. However, effective planning in scenarios involving dependencies between API or tool calls-particularly in multi-turn conversations-remains a significant challenge. To address this, we introduce T1, a tool-augmented, multi-domain, multi-turn conversational dataset specifically designed to capture and manage inter-tool dependencies across diverse domains. T1 enables rigorous evaluation of agents' ability to coordinate tool use across nine distinct domains (4 single domain and 5 multi-domain) with the help of an integrated caching mechanism for both short- and long-term memory, while supporting dynamic replanning-such as deciding whether to recompute or reuse cached results. Beyond facilitating research on tool use and planning, T1 also serves as a benchmark for evaluating the performance of open-weight and proprietary large language models. We present results powered by T1-Agent, highlighting their ability to plan and reason in complex, tool-dependent scenarios.
@article{arxiv.2505.16986,
title = {T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning},
author = {Amartya Chakraborty and Paresh Dashore and Nadia Bathaee and Anmol Jain and Anirban Das and Shi-Xiong Zhang and Sambit Sahu and Milind Naphade and Genta Indra Winata},
journal= {arXiv preprint arXiv:2505.16986},
year = {2025}
}
Comments
Accepted by NeurIPS 2025 Datasets and Benchmarks Track