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AAAR-1.0: Assessing AI's Potential to Assist Research

Renze Lou, Hanzi Xu, Sijia Wang, Jiangshu Du, Ryo Kamoi, Xiaoxin Lu, Jian Xie, Yuxuan Sun, Yusen Zhang, Jihyun Janice Ahn, Hongchao Fang, Zhuoyang Zou, Wenchao Ma, Xi Li, Kai Zhang, Congying Xia, Lifu Huang, Wenpeng Yin

TL;DR

AAAR-1.0 introduces four expert-level AI research tasks—EqInfer, ExpDesign, PaperWeakness, and ReviewCritique—to rigorously benchmark LLMs on domain knowledge and reasoning. It uses expert-annotated data pipelines and task-specific metrics to reveal strengths and gaps across open- and closed-source models, showing notable challenges in precision, feasibility, and meta-review analysis. The framework emphasizes transparent, stand-alone task evaluation rather than end-to-end automation, aiming to guide responsible integration of LLMs into research workflows. Overall, the work provides a principled benchmark and insights into how AI might assist researchers without replacing them, and outlines directions for larger-scale future versions.

Abstract

Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation. However, researchers face unique challenges and opportunities in leveraging LLMs for their own work, such as brainstorming research ideas, designing experiments, and writing or reviewing papers. In this study, we introduce AAAR-1.0, a benchmark dataset designed to evaluate LLM performance in three fundamental, expertise-intensive research tasks: (i) EquationInference, assessing the correctness of equations based on the contextual information in paper submissions; (ii) ExperimentDesign, designing experiments to validate research ideas and solutions; (iii) PaperWeakness, identifying weaknesses in paper submissions; and (iv) REVIEWCRITIQUE, identifying each segment in human reviews is deficient or not. AAAR-1.0 differs from prior benchmarks in two key ways: first, it is explicitly research-oriented, with tasks requiring deep domain expertise; second, it is researcher-oriented, mirroring the primary activities that researchers engage in on a daily basis. An evaluation of both open-source and proprietary LLMs reveals their potential as well as limitations in conducting sophisticated research tasks. We will keep iterating AAAR-1.0 to new versions.

AAAR-1.0: Assessing AI's Potential to Assist Research

TL;DR

AAAR-1.0 introduces four expert-level AI research tasks—EqInfer, ExpDesign, PaperWeakness, and ReviewCritique—to rigorously benchmark LLMs on domain knowledge and reasoning. It uses expert-annotated data pipelines and task-specific metrics to reveal strengths and gaps across open- and closed-source models, showing notable challenges in precision, feasibility, and meta-review analysis. The framework emphasizes transparent, stand-alone task evaluation rather than end-to-end automation, aiming to guide responsible integration of LLMs into research workflows. Overall, the work provides a principled benchmark and insights into how AI might assist researchers without replacing them, and outlines directions for larger-scale future versions.

Abstract

Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation. However, researchers face unique challenges and opportunities in leveraging LLMs for their own work, such as brainstorming research ideas, designing experiments, and writing or reviewing papers. In this study, we introduce AAAR-1.0, a benchmark dataset designed to evaluate LLM performance in three fundamental, expertise-intensive research tasks: (i) EquationInference, assessing the correctness of equations based on the contextual information in paper submissions; (ii) ExperimentDesign, designing experiments to validate research ideas and solutions; (iii) PaperWeakness, identifying weaknesses in paper submissions; and (iv) REVIEWCRITIQUE, identifying each segment in human reviews is deficient or not. AAAR-1.0 differs from prior benchmarks in two key ways: first, it is explicitly research-oriented, with tasks requiring deep domain expertise; second, it is researcher-oriented, mirroring the primary activities that researchers engage in on a daily basis. An evaluation of both open-source and proprietary LLMs reveals their potential as well as limitations in conducting sophisticated research tasks. We will keep iterating AAAR-1.0 to new versions.

Paper Structure

This paper contains 57 sections, 4 equations, 13 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLMs for AI Research.
  4. Benchmarks for AI Research Tasks.
  5. AAAR-1.0
  6. EquationInference
  7. ① Data crawling and cleaning.
  8. ② LLM-based equation synthesis.
  9. ③ LLM-based filtering.
  10. ④ Expert-based examination.
  11. Final data.
  12. ExperimentDesign
  13. ① Data crawling.
  14. ② Domain-expert annotation.
  15. ③ Multi-round peer discussion.
  16. ...and 42 more sections

Figures (13)

  • Figure 1: The input-output illustration of four tasks in the proposed AAAR-1.0 benchmark.
  • Figure 2: Data construction workflows of the three tasks in AAAR-1.0.
  • Figure 3: The data diversity illustration of Weakness, including the score distribution and track distribution of the papers used in our dataset.
  • Figure 4: The input context length scaling trend on the EqInfer task.
  • Figure 5: The input context length scaling trend of different LLMs on the ExpDesign task.
  • ...and 8 more figures