Can AI Become a true Research Partner?

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Author: Denis Avetisyan

A new benchmark assesses whether large language models can move beyond data analysis and contribute to genuine scientific discovery in complex biological systems.

HeurekaBench dissects scientific discovery into a three-stage process-insight generation and validation from literature, reformulation as question-answer pairs, and autonomous, multi-step analysis to produce data-driven answers-effectively creating a system that reverse-engineers the path from observation to verifiable result.
HeurekaBench dissects scientific discovery into a three-stage process-insight generation and validation from literature, reformulation as question-answer pairs, and autonomous, multi-step analysis to produce data-driven answers-effectively creating a system that reverse-engineers the path from observation to verifiable result.

HeurekaBench introduces a framework for evaluating LLM-based agents performing multi-step reasoning and data-driven insights in single-cell biology.

Evaluating the potential of large language model-based agents as scientific collaborators remains challenging due to a lack of realistic, end-to-end benchmarks. To address this, we introduce HeurekaBench: A Benchmarking Framework for AI Co-scientist, a novel framework and benchmark designed to assess these agents’ abilities in open-ended scientific discovery, instantiated here with a single-cell biology dataset. Our approach leverages LLMs to construct exploratory research questions grounded in existing scientific studies, enabling quantitative analysis of agentic system design choices and revealing that incorporating a critic module can significantly improve performance. Will this framework pave the way for more rigorous evaluation and ultimately accelerate AI-driven scientific breakthroughs?

Deconstructing the Biological Black Box

Conventional bioinformatics pipelines, meticulously crafted for specific analytical tasks, often falter when confronted with genuinely open-ended biological questions. These pipelines typically demand precisely defined inputs and follow rigid, linear workflows, proving inadequate for investigations requiring iterative exploration and nuanced reasoning. The inherent complexity of biological systems necessitates a more flexible approach; researchers increasingly find themselves needing to formulate hypotheses during analysis, guided by emerging patterns in the data rather than pre-defined expectations. This shift challenges the traditional paradigm of ‘answer-seeking’ bioinformatics, instead demanding tools capable of ‘question-asking’ – systems that can autonomously generate and test potential explanations based on complex datasets, mirroring the exploratory process of scientific discovery itself.

The advent of single-cell technologies has unleashed datasets of unprecedented scale and dimensionality, fundamentally challenging conventional bioinformatics approaches. Analyzing these complex profiles requires more than simply applying pre-defined analytical pipelines; the sheer volume of variables and intricate cellular heterogeneity necessitate a paradigm shift towards autonomous hypothesis generation. Instead of researchers formulating specific questions and then seeking answers within the data, computational systems are now being developed to independently identify potentially significant patterns, correlations, and anomalies. This involves algorithms capable of iteratively exploring the data, proposing testable hypotheses, and refining those hypotheses based on subsequent analysis – essentially mimicking the exploratory process of scientific discovery. Such an approach promises to uncover previously hidden biological insights and accelerate the pace of research in areas like immunology, cancer biology, and developmental biology, where nuanced cellular differences often hold the key to understanding complex phenomena.

Evaluation across three large language models reveals consistent performance trends between the benchmarks, with the closed-source model consistently outperforming open-source alternatives, though scores for the cell-cell communication category are based on a unique dataset <span class="katex-eq" data-katex-display="false">Li2024UterineNK</span> not present in the smaller benchmark.
Evaluation across three large language models reveals consistent performance trends between the benchmarks, with the closed-source model consistently outperforming open-source alternatives, though scores for the cell-cell communication category are based on a unique dataset Li2024UterineNK not present in the smaller benchmark.

Automating Inquiry: The Rise of AI Agents

Large language model (LLM)-based agents represent a developing methodology for the automation of complex scientific workflows. These agents leverage the capacity of LLMs to process and interpret data, enabling them to perform tasks traditionally requiring human expertise, such as hypothesis generation, experimental design, and data analysis. Automation through these agents aims to significantly reduce the time and resources required for scientific discovery by enabling iterative, data-driven exploration and the rapid evaluation of potential insights. Early applications demonstrate the potential for accelerating research in fields generating large datasets, including genomics, materials science, and drug discovery, though current implementations require careful validation and oversight to ensure the reliability of results.

AI agents designed for scientific discovery function through a modular architecture comprising three core components. The Planner component is responsible for decomposing a research question into a series of executable steps, establishing the overall analytical strategy. The Retriever component facilitates access to external tools and data resources, including databases, APIs, and computational software, necessary for executing those steps. Finally, the Critic component evaluates the outputs of each step, identifying potential errors or inconsistencies and providing feedback to refine the analytical pipeline; this iterative refinement process enhances the reliability and accuracy of the generated insights.

Workflow generation is a core capability of AI agents designed for scientific discovery, allowing for the automated construction of analytical pipelines. These pipelines are not pre-defined but are dynamically assembled based on the specific research question posed to the agent. The process involves identifying relevant data sources, selecting appropriate analytical tools – which may include statistical packages, simulation software, or database queries – and sequencing these tools into a coherent workflow. This dynamic construction contrasts with traditional scripting, where pipelines are manually coded and lack adaptability. The agent iteratively refines the workflow based on intermediate results and feedback, allowing for exploration of multiple analytical paths and ultimately increasing the efficiency of data analysis and hypothesis testing.

Independent evaluations by three large language model judges consistently identified Claude-4-Sonnet as the top-performing planner within the Biomni agent.
Independent evaluations by three large language model judges consistently identified Claude-4-Sonnet as the top-performing planner within the Biomni agent.

Benchmarking the Autonomous Explorer: sc-HeurekaBench

sc-HeurekaBench is a benchmark dataset designed to evaluate agent performance in the context of single-cell biology by adapting the existing HeurekaBench framework. The dataset comprises a total of 10,050 questions, specifically 5,050 open-ended questions (OEQs) and 5,050 multiple-choice questions (MCQs). These questions are formulated as complex research problems requiring agents to demonstrate analytical reasoning and knowledge application within the domain of single-cell data analysis, thereby providing a standardized and scalable method for evaluating and comparing agent capabilities in this field.

Agent performance within sc-HeurekaBench is assessed through a three-stage process mirroring the scientific method. Initially, agents must generate testable hypotheses based on provided open-ended questions (OEQs). Subsequently, agents are required to identify and apply relevant analytical tools – encompassing statistical tests, data visualization techniques, and bioinformatics pipelines – to the single-cell data. Finally, agents are evaluated on their capacity to interpret the results of these analyses and extract biologically meaningful insights, demonstrating an ability to connect data-driven findings back to the initial hypothesis and formulate conclusions.

The sc-HeurekaBench utilizes an LLM-as-a-Judge system for automated evaluation of agent responses, offering a scalable alternative to manual expert assessment. This system demonstrates substantial correlation with human expert evaluations, achieving a Spearman’s Rank Correlation coefficient of 0.93. Furthermore, inter-rater reliability is high, as indicated by a Cohen’s Kappa value of 0.85, signifying strong agreement between the LLM-based judgements and those of human experts in assessing the quality and validity of agent-generated responses.

Question distributions across the six task categories are consistent between benchmark versions, with the exception of cell-cell communication analysis questions sourced solely from the Li2024UterineNK dataset and not included in sc-HeurekaBench-Lite.
Question distributions across the six task categories are consistent between benchmark versions, with the exception of cell-cell communication analysis questions sourced solely from the Li2024UterineNK dataset and not included in sc-HeurekaBench-Lite.

Biomni: An Autonomous System for Single-Cell Inquiry

Biomni is an autonomous agent specifically engineered for data analysis tasks within the field of single-cell biology. Its functionality is driven by large language model (LLM) capabilities, enabling it to interpret biological questions and formulate analytical workflows without direct human intervention. This agent-based approach allows for automated processing of single-cell datasets, aiming to streamline research and accelerate discovery by automating traditionally manual analytical pipelines. The system is designed to accept a biological question as input and then independently execute the necessary computational steps to arrive at an answer, leveraging the power of LLMs for reasoning and task orchestration.

Biomni incorporates established bioinformatics tools directly into its automated workflow creation. Specifically, it leverages SCENIC for regulatory network inference, CellChat and CellPhoneDB for cell-cell communication analysis, and integrates these methods without requiring manual scripting or external tool chaining. This integration enables Biomni to perform complex single-cell analyses, such as identifying key transcription factors, predicting ligand-receptor interactions, and characterizing intercellular communication patterns, all within a unified, autonomous framework. The seamless incorporation of these methods streamlines the analytical process and facilitates reproducible research.

Evaluations using the sc-HeurekaBench-Lite benchmark demonstrate Biomni’s analytical capabilities, achieving an accuracy score of 2.49 when employing an end-critic module; this performance is comparable to that of established, closed-source models. Notably, the inclusion of the end-critic also resulted in a 0.6 increase in correctness scores specifically on questions where initial performance was low, indicating improved reasoning on challenging queries. Performance was significantly reduced when the retriever module was disabled, highlighting its critical role in Biomni’s data analysis workflow and ability to accurately interpret single-cell data.

Beyond Automation: The Future of AI-Driven Biology

Current large language models demonstrate impressive abilities in biological text processing, but often lack the capacity for complex, iterative reasoning necessary to formulate and test hypotheses. Integrating reinforcement learning offers a pathway to overcome this limitation. By training these agents with reward signals based on experimental outcomes – whether simulated or real – their reasoning processes can be refined beyond simple pattern recognition. This allows the agent to proactively design experiments, interpret results, and adjust its approach-essentially learning to ‘think’ like a scientist. Such a system could, for example, navigate the vast landscape of genomic data to identify promising drug targets or optimize gene editing strategies, moving beyond merely summarizing existing knowledge to generating novel insights and accelerating biological discovery.

The capacity of large language model-based agents to analyze and interpret complex biological data isn’t limited to the intricacies of single-cell systems; rather, it represents a broadly applicable methodology poised to reshape scientific investigation across numerous disciplines. These agents, initially demonstrating proficiency in deciphering gene expression patterns and cellular functions, are increasingly being adapted to tackle challenges in fields like materials science, drug discovery, and even climate modeling. By autonomously formulating hypotheses, designing experiments – both in silico and potentially in vitro – and interpreting results, these systems promise to accelerate the pace of discovery beyond the constraints of traditional, manual approaches. The ability to synthesize information from vast, heterogeneous datasets and identify non-obvious correlations suggests a future where AI-driven agents serve as indispensable partners in tackling some of the most pressing scientific questions, extending the reach of automated reasoning far beyond its initial biological focus.

The burgeoning field of artificial intelligence in biological research demands more than just sophisticated algorithms; it requires rigorous, standardized evaluation to ensure progress is genuine and reproducible. Currently, assessing the performance of AI agents designed for scientific discovery lacks universally accepted benchmarks, hindering comparisons between different approaches and obscuring true advancements. Developing robust metrics that go beyond simple accuracy – encompassing factors like experimental design quality, hypothesis novelty, and the efficiency of knowledge acquisition – is paramount. Such standardized evaluations will not only accelerate innovation by pinpointing areas for improvement, but also foster trust in AI-driven insights, ultimately enabling these tools to become indispensable partners in scientific exploration and validation.

HeurekaBench, as a framework for evaluating LLM-based agents in scientific discovery, inherently necessitates a willingness to challenge established norms. The pursuit of data-driven insights within single-cell biology, as the article details, isn’t merely about confirming existing hypotheses, but about venturing into the unknown. This resonates deeply with Donald Knuth’s observation: “Premature optimization is the root of all evil.” The framework doesn’t prematurely constrain the LLM-based agents; instead, it allows them to explore a problem space, even if that exploration initially appears chaotic, ultimately revealing unseen connections and fostering genuine scientific advancement. The benchmark’s focus on multi-step reasoning embodies the principle of letting the system reveal its underlying architecture through iterative testing.

Beyond the Horizon

HeurekaBench represents more than simply a benchmark; it’s a controlled demolition of the ‘black box’ surrounding LLM-based scientific inquiry. The framework deliberately forces these agents to articulate a reasoning process, exposing the fault lines in their ‘comprehension’-or, more accurately, their statistical mimicry of it. Current iterations succeed in navigating pre-defined datasets, but the true exploit of comprehension will lie in an agent’s ability to formulate novel, testable hypotheses without explicit prompting, venturing into the unmapped territory of biological space.

The limitations are, predictably, not computational. It is the scarcity of truly ‘ground truth’ in single-cell biology – the inherent messiness of life – that presents the ultimate challenge. Evaluating an agent’s insight isn’t about matching a known answer, but assessing the elegance of its questioning. Future iterations must therefore incorporate mechanisms for evaluating not just the output, but the method of discovery – the very heuristics guiding the search for knowledge.

Ultimately, HeurekaBench, and frameworks like it, aren’t building AI scientists. They’re building better tools for reverse-engineering reality. The true measure of success won’t be automation, but augmentation – the degree to which these agents can amplify human intuition, revealing previously inaccessible patterns in the noise. The benchmark isn’t the destination; it’s the starting point for a more rigorous interrogation of intelligence itself.

Original article: https://arxiv.org/pdf/2601.01678.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-06 17:01