F rontier S cience Bench: Evaluating AI Research Capabilities in LLM s (2025)

Abstract: Large language models (LLMs) have shown remarkable capabilities across various tasks, yet their potential to reason about and construct scientific methodologies remains under explored. This work introduces a novel benchmark evaluating LLMs’ capacity to predict methodological details in AI research papers. We construct a dataset of 88 papers with redacted methodology sections and zero-shot prompt several state-of-the-art LLMs to generate methodology predictions. Our evaluation framework then employs a LLM-as-judge system with multiple LLM judges, majority voting, and self-omission techniques to minimize biases. We validate our LLM judge scores against human judgments. We then briefly analyze the judging results of our zero-shot prediction pipeline, suggesting that even state-of-the-art LLMs struggle with the task of methodology generation without more advanced techniques. This benchmark lays the groundwork for future research into evaluating LLMs’ potential for aiding in AI research.

## introduction large language models (llms) have demonstrated remarkable capabilities in tasks such as summarization, translation, and text generation @xcite @xcite . while llms excel at identifying patterns from their vast training data, their ability to engage in the structured reasoning required for scientific discovery remains underexplored. this is crucial because in many scientific fields, progress relies not just on understanding existing knowledge but on devising novel methodologies to transform abstract goals into rigorous experimental designs. to test llms' capacity for scientific reasoning, we introduce a benchmark that evaluates their ability to infer plausible methodology sections from redacted ai research papers, where explicit methodological details have been removed. our study provides a high-quality dataset of 88 manually redacted papers, a zero-shot prediction pipeline, and a rigorous llm-as-a-judge evaluation framework to assess the quality of predictions. ## related works the application of llms as assistants in the scientific method is a promising research area. existing benchmarks for llm evaluation, such as factual knowledge retrieval, summarization, or question answering, focus mainly on surface-level tasks @xcite @xcite . these benchmarks test recall and synthesis but rarely assess whether llms can reason creatively and simulate problem-solving processes essential for scientific discovery. @xcite demonstrated that llms have surpassed experts in predicting neuroscience experiment outcomes through their benchmark, brain-bench. while outcome prediction is valuable, it reveals little about an llm's ability to plan and reason through methodological steps. this abstract reasoning process is crucial for aiding researchers in accelerating scientific discovery. @xcite developed the scieval benchmark to examine llm reasoning in biology, chemistry, and physics. however, its multiple-choice format primarily assesses textbook knowledge and calculation skills rather than extensive reasoning. while useful for evaluating general understanding, these questions do not push llms to plan and organize methodological steps-a critical skill when considering the background and context of a research study. @xcite systematically examines the challenges of using llms for evaluation tasks, emphasizing reliability, bias mitigation, and resistance to manipulation. their survey addresses critiques of llm-as-a-judge systems, including biases like length bias and self-enhancement bias, and discusses mitigation approaches such as multiple llm judges, structured outputs, voting-based consensus mechanisms, and fine-tuning. while their study covers broad applications, our work focuses specifically on methodology generation and assessing the accuracy of these methodologies. weng et al. @xcite uses llm agents to simulate the full research-review-refinement workflow, while @xcite evaluates ai agents' ability to replicate 20 icml 2024 papers via hierarchical rubrics and llm-based judging-finding only about 21 % average replication. in contrast, our benchmark isolates zero-shot methodology prediction for ai/ml papers and employs a multijudge llm framework with majority voting and self-omission to reduce bias. this narrow focus complements broader end-to-end and replication studies by targeting the core methodological reasoning essential for novel ai research. ## methods in this section we explain the details behind the construction of our benchmark. as a reminder, our goal is to evaluate the extent to which llms can reconstruct novel research methodologies based on redacted ai research papers. the benchmark consists of three stages: dataset curation, prediction, and evaluation. we first created a dataset of 88 research papers, redacted to remove their methodology sections, results, and any references to methodology found in other sections. we then used this new dataset and zero-shot prompted multiple state-of-the-art llm's including gpt-4o, o3-mini, claude 3.5 sonnet, and gemini 1.5 pro to generate methodologies. finally, we used a llm-as-a-judge framework to evaluate how close the predicted methodologies were to the original ones. detailed prompts, examples, and additional figures are provided in the appendices (d, e, f). ## experiments and results our framework uses gpt-4o, o3-mini, claude 3.5 sonnet, and gemini 1.5 pro as predictors, while judges include gpt-4o, o3-mini, claude 3.5 sonnet, and grok 2. models were chosen for their strong instruction-following capabilities. grok 2 was excluded from prediction due to inconsistent outputs and uncertain cutoff; gemini 1.5 pro from judging due to bias. deepseek r1 and llama 3.1 405b instruct failed initial trials due to json formatting issues. we provide examples and extra figures in appendix e and appendix f. ## conclusion in this work, we introduced a novel benchmark to evaluate the ability of large language models to generate detailed scientific methodologies from redacted research papers. our dataset of 88 redacted papers, combined with a zero-shot prediction pipeline and a robust llm-as-a-judge evaluation framework, demonstrates that while stateof-the-art llms can capture broad methodological outlines, they consistently struggle to deliver the domain-specific precision required for high-quality scientific methods. the close alignment with human evaluations (mean absolute error of 0.725) reinforce the reliability of our evaluation process. future work should expand the dataset to more scientific disciplines and better handle mathematical notation. automating paper redaction will help publish updated versions of the dataset for newer llms. ai agents with code execution tools will allow models to validate predicted methodologies before scoring. finally, including more llm judges with expanded human validation will increase reliability in scoring. we imposed this page limit due to context window limits and to constrain the manual redaction process. this may exclude more rigorous method-ologies in longer research papers that are more challenging to predict. additionally, papers with extensive mathematical notation were excluded due to pymupdf parsing failures. this limits our benchmark's applicability to theoretical or math-heavy research and may inflate model performance. finally, while all 88 papers were published after gpt-4o's october 2023 cutoff to minimize training data overlap, we later included models with later cutoffs (e.g., claude 3.5 sonnet, gemini 1.5 pro). this introduces a risk of training data contamination in those models.