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arXiv · 2510.10549

ELAIPBench: A Benchmark for Expert-Level Artificial Intelligence Paper Understanding

Xinbang Dai·Huikang Hu·Yongrui Chen·Jiaqi Li·Rihui Jin·Yuyang Zhang·Xiaoguang Li·Lifeng Shang·Guilin Qi
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Abstract

While large language models (LLMs) excel at many domain-specific tasks, their ability to deeply comprehend and reason about full-length academic papers remains underexplored. Existing benchmarks often fall short of capturing such depth, either due to surface-level question design or unreliable evaluation metrics. To address this gap, we introduce ELAIPBench, a benchmark curated by domain experts to evaluate LLMs' comprehension of artificial intelligence (AI) research papers. Developed through an incentive-driven, adversarial annotation process, ELAIPBench features 403 multiple-choice questions from 137 papers. It spans three difficulty levels and emphasizes non-trivial reasoning rather than shallow retrieval. Our experiments show that the best-performing LLM achieves an accuracy of only 39.95%, far below human performance. Moreover, we observe that frontier LLMs equipped with a thinking mode or a retrieval-augmented generation (RAG) system fail to improve final results-even harming accuracy due to overthinking or noisy retrieval. These findings underscore the significant gap between current LLM capabilities and genuine comprehension of academic papers.

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Xinbang Dai, Huikang Hu, Yongrui Chen, Jiaqi Li, Rihui Jin, Yuyang Zhang, Xiaoguang Li, Lifeng Shang, Guilin Qi. 2026-01-07. ELAIPBench: A Benchmark for Expert-Level Artificial Intelligence Paper Understanding. https://arxiv.org/abs/2510.10549

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