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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.07886 |
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| _version_ | 1866911498528358400 |
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| author | Xue, Xiaona Huang, Yiqiao Li, Jiacheng Zheng, Yuanhang Miao, Huiqi Ma, Yunfei Liu, Rui Sun, Xinbao Liu, Minglu Meng, Fanyu Deng, Chao Feng, Junlan |
| author_facet | Xue, Xiaona Huang, Yiqiao Li, Jiacheng Zheng, Yuanhang Miao, Huiqi Ma, Yunfei Liu, Rui Sun, Xinbao Liu, Minglu Meng, Fanyu Deng, Chao Feng, Junlan |
| contents | Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation methods often oversimplify instruction complexity as a mere additive combination of atomic constraints, failing to adequately capture the high-dimensional complexity arising from the intricate interplay of content and format, logical workflow control, and real-world applications. This leads to a significant gap between current evaluation practices and practical demands. To bridge this gap, we introduce CCR-Bench, a novel benchmark designed to assess LLMs' adherence to complex instructions. CCR-Bench is characterized by: (1) deep entanglement of content and formatting requirements in task specifications; (2) instructions that involve intricate task decomposition, conditional reasoning, and procedural planning; and (3) evaluation samples derived entirely from real-world industrial scenarios. Extensive experiments on CCR-Bench demonstrate that even state-of-the-art models exhibit substantial performance deficiencies, clearly quantifying the gap between current LLM capabilities and the demands of realworld instruction understanding. We believe that CCR-Bench offers a more rigorous and realistic evaluation framework, advancing the development of LLMs toward the next generation of models capable of understanding and executing complex tasks in industrial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07886 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CCR-Bench: A Comprehensive Benchmark for Evaluating LLMs on Complex Constraints, Control Flows, and Real-World Cases Xue, Xiaona Huang, Yiqiao Li, Jiacheng Zheng, Yuanhang Miao, Huiqi Ma, Yunfei Liu, Rui Sun, Xinbao Liu, Minglu Meng, Fanyu Deng, Chao Feng, Junlan Computation and Language Artificial Intelligence Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation methods often oversimplify instruction complexity as a mere additive combination of atomic constraints, failing to adequately capture the high-dimensional complexity arising from the intricate interplay of content and format, logical workflow control, and real-world applications. This leads to a significant gap between current evaluation practices and practical demands. To bridge this gap, we introduce CCR-Bench, a novel benchmark designed to assess LLMs' adherence to complex instructions. CCR-Bench is characterized by: (1) deep entanglement of content and formatting requirements in task specifications; (2) instructions that involve intricate task decomposition, conditional reasoning, and procedural planning; and (3) evaluation samples derived entirely from real-world industrial scenarios. Extensive experiments on CCR-Bench demonstrate that even state-of-the-art models exhibit substantial performance deficiencies, clearly quantifying the gap between current LLM capabilities and the demands of realworld instruction understanding. We believe that CCR-Bench offers a more rigorous and realistic evaluation framework, advancing the development of LLMs toward the next generation of models capable of understanding and executing complex tasks in industrial applications. |
| title | CCR-Bench: A Comprehensive Benchmark for Evaluating LLMs on Complex Constraints, Control Flows, and Real-World Cases |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.07886 |