arXiv Computation and Language September 4, 2026 By Yuanhao Shen, Daniel Xavier de Sousa, Ricardo Mar\c{c}al, Hongyu Guo, Xiaodan Zhu

IDRBench: Understanding the Capability of Large Language Models on Interdisciplinary Research

Read the original on arXiv Computation and Language →

The paper introduces IDRBench, a framework designed to evaluate how well large language models (LLMs) can integrate knowledge across disciplines for interdisciplinary research. It comprises datasets and tasks—IDR Paper Identification, IDR Idea Integration, and IDR Idea Recommendation—to benchmark LLM performance. The authors analyze ten mainstream LLMs, offering a comprehensive assessment and establishing baselines for future studies.

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