arXiv:2606.26130v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to guide research methodology, yet their default methodological tendencies under minimal prompting remain unclear. Here, we prompt GPT-5.1, Gemini 3 Pro, and DeepSeek-V3.2 with an LLM-extracted research question from each of 1,000 recent arXiv computer-science papers and compare the resulting methodology suggestions against a paper-derived experimental inventory. Since we provide only the research question, the differences we measure reflect initial suggestions and not how optimal those suggest
The increasing integration of LLMs into research workflows makes understanding their default methodological suggestions critical for academic integrity and scientific progress.
This study offers insights into the inherent biases and default operating modes of leading LLMs when generating research methodologies, which is crucial for their responsible deployment in scientific discovery.
We gain a clearer understanding of how current frontier LLMs approach scientific methodology when unguided, revealing their strengths and potential blind spots.
- · AI ethicists and safety researchers
- · Academics and research institutions
- · LLM developers improving model robustness
- · Researchers over-relying on unguided LLM outputs
- · Sub-optimal research methodologies
Researchers will become more aware of the limitations of current LLMs in designing robust methodologies.
LLM developers will likely integrate more explicit methodological constraints or guidance into their next-generation models.
The development of specialized AI agents for scientific method generation could accelerate, leading to novel research paradigms.
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Read at arXiv cs.AI