Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. However, current evaluations predominantly focus on single-turn reasoning scenarios, leaving interactive tasks largely unexplored. We attribute it to the absence of comprehensive datasets and scalable automatic evaluation protocols. To fill these gaps, we present MTR-Bench for LLMs' Multi-Turn Reasoning evaluation. Comprising 4 classes, 40 tasks, and 3600 instances, MTR-Bench covers diverse reasoning capabilities, fine-grained difficulty granularity, and necessitates multi-turn interactions with the environments. Moreover, MTR-Bench features fully-automated framework spanning both dataset constructions and model evaluations, which enables scalable assessment without human interventions. Extensive experiments reveal that even the cutting-edge reasoning models fall short of multi-turn, interactive reasoning tasks. And the further analysis upon these results brings valuable insights for future research in interactive AI systems.
@article{arxiv.2505.17123,
title = {MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation},
author = {Xiaoyuan Li and Keqin Bao and Yubo Ma and Moxin Li and Wenjie Wang and Rui Men and Yichang Zhang and Fuli Feng and Dayiheng Liu},
journal= {arXiv preprint arXiv:2505.17123},
year = {2026}
}