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[2601.01678] HeurekaBench: A Benchmarking Framework for AI Co-scientist
Summary
HeurekaBench introduces a benchmarking framework for AI co-scientists, enabling rigorous evaluation of LLM-based systems through realistic scientific scenarios and datasets.
Why It Matters
As AI systems increasingly assist in scientific research, establishing effective benchmarks is crucial for evaluating their performance and reliability. HeurekaBench addresses the challenge of creating realistic evaluation scenarios, thus contributing to the advancement of AI in scientific domains.
Key Takeaways
- HeurekaBench provides a framework for benchmarking AI co-scientists in scientific research.
- The framework uses semi-automated pipelines to generate exploratory research questions.
- It demonstrates improved performance of open-source LLM agents with the addition of a critic module.
- The benchmarks are grounded in real scientific workflows, enhancing evaluation relevance.
- HeurekaBench aims to standardize the assessment of agentic systems in scientific contexts.
Computer Science > Machine Learning arXiv:2601.01678 (cs) [Submitted on 4 Jan 2026 (v1), last revised 22 Feb 2026 (this version, v2)] Title:HeurekaBench: A Benchmarking Framework for AI Co-scientist Authors:Siba Smarak Panigrahi, Jovana Videnović, Maria Brbić View a PDF of the paper titled HeurekaBench: A Benchmarking Framework for AI Co-scientist, by Siba Smarak Panigrahi and 2 other authors View PDF HTML (experimental) Abstract:LLM-based reasoning models have enabled the development of agentic systems that act as co-scientists, assisting in multi-step scientific analysis. However, evaluating these systems is challenging, as it requires realistic, end-to-end research scenarios that integrate data analysis, interpretation, and the generation of new insights from the experimental data. To address this limitation, we introduce HeurekaBench, a framework to create benchmarks with exploratory, open-ended research questions for experimental datasets. Each such question is grounded in a scientific study and its corresponding code repository, and is created using a semi-automated pipeline that leverages multiple LLMs to extract insights and generate candidate workflows, which are then verified against reported findings. We instantiate the framework in single-cell biology to obtain sc-HeurekaBench benchmark and use it to compare state-of-the-art single-cell agents. We further showcase the benefits of our benchmark for quantitatively analyzing current design choices in agentic syste...
