MSCoRe: A Benchmark for Multi-Stage Collaborative Reasoning in LLM Agents

• URL: http://arxiv.org/abs/2509.17628v1
• Date: Mon, 22 Sep 2025 11:36:16 GMT
• Title: MSCoRe: A Benchmark for Multi-Stage Collaborative Reasoning in LLM Agents
• Authors: Yuzhen Lei, Hongbin Xie, Jiaxing Zhao, Shuangxue Liu, Xuan Song,
• Abstract summary: MSCoRe is a novel benchmark comprising 126696 domain-specific QA instances spanning scenarios in automotive, pharmaceutical, electronics, and energy sectors.<n>The commercial models performed best across all tasks and scenarios, but a notable gap in ROUGE scores remains between simple and complex tasks.<n>MSCoRe provides a valuable new resource for the community to evaluate and improve multi-stage reasoning in LLM agents.
• Score: 7.339769470891067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have excelled in question-answering (QA) tasks within single domains. However, their reasoning and coordination capabilities in complex, multi-stage scenarios remain underexplored. Existing benchmarks typically focus on isolated tasks or narrow domains, overlooking models' abilities for multi-stage collaboration and optimization without explicit external guidance. To bridge this gap, we propose \textbf{MSCoRe}, a novel benchmark comprising 126696 domain-specific QA instances spanning scenarios in automotive, pharmaceutical, electronics, and energy sectors. The dataset is created using a structured three-phase pipeline: dynamic sampling, iterative question-answer generation, and a multi-level quality assessment to ensure data quality. Tasks are further categorized into three difficulty levels according to stage coverage and complexity. With MSCoRe, we have conducted a comprehensive evaluation of various state-of-the-art LLM agents. The commercial models performed best across all tasks and scenarios, but a notable gap in ROUGE scores remains between simple and complex tasks. We also tested the models' robustness and found that their performance is negatively affected by noisy data. MSCoRe provides a valuable new resource for the community to evaluate and improve multi-stage reasoning in LLM agents. The code and data are available at https://github.com/D3E0-source/MSCoRE.

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