ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery

Authors: Ziru Chen, Shijie Chen, Yuting Ning, Qianheng Zhang, Boshi Wang, Botao Yu, Yifei Li, Zeyi Liao, Chen Wei, Zitong Lu, Vishal Dey, Mingyi Xue, Frazier N. Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, Huan Sun
Primary area: datasets and benchmarks
Keywords: Benchmark, Evaluation, Large Language Model, Language Agent, AI for Science, Code Generation, Task Automation

Abstract:
The advancements of language language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked both excitement and skepticism about the true capabilities of such agents. In this work, we argue that for an agent to fully automate scientific discovery, it must be able to complete all essential tasks in the workflow. Thus, we call for rigorous assessment of agents on individual tasks in a scientific workflow before making bold claims on end-to-end automation. To this end, we present ScienceAgentBench, a new benchmark for evaluating language agents for data-driven scientific discovery. To ensure the scientific authenticity and real-world relevance of our benchmark, we extract 102 tasks from 44 peer-reviewed publications in four disciplines and engage nine subject matter experts to validate them. We unify the target output for every task to a self-contained Python program file and employ an array of evaluation metrics to examine the generated programs, execution results, and costs. Each task goes through multiple rounds of manual validation by annotators and subject matter experts to ensure its annotation quality and scientific plausibility. We also propose two effective strategies to mitigate data contamination concerns. Using our benchmark, we evaluate five open-weight and proprietary LLMs, each with three frameworks: direct prompting, OpenHands, and self-debug. Given three attempts for each task, the best-performing agent can only solve 32.4% of the tasks independently and 34.3% with expert-provided knowledge. These results underscore the limited capacities of current language agents in generating code for data-driven discovery, let alone end-to-end automation for scientific research.

Decision: accept (poster)
Reviewer scores: 5, 8, 5

Reviews and discussion:
<thread>
creation date:
1730100085911

thread_type:
official_review

<thread_object>
creator: reviewer_ws3k

<content>
summary:
This paper introduces a novel benchmark, ScienceAgentBench, designed to assess language agents' performance in data-driven scientific exploration. It meticulously curates 102 diverse tasks sourced from 44 peer-reviewed publications spanning four disciplines (Bio, Chem, Information Sci, Psy & Cog Neuroscience), subsequently validated by nine subject matter experts. Employing a variety of evaluation metrics, the study examines the efficacy of generated programs, their execution outcomes, and associated costs. By evaluating five LLMs, including both open-weight and proprietary models, across three frameworks—direct prompting, OpenHands, and self-debug—the findings underscore the current limitations of language agents in generating code for data-driven discovery.

strengths:
(1) Writing: The clarity of this paper make it well-written and easy to comprehend.

(2) Benchmark: This paper introduces ScienceAgentBench, a framework tailored for assessing language agents in the realm of data-driven scientific exploration. It emphasizes scientific authenticity through collaboration with subject matter experts, establishes rigorous evaluation criteria, and maintains meticulous control over multi-stage quality assurance.

(3) Experiments: The paper evaluates three open-source models and two API-based models, conducting detailed assessments and in-depth analyses to provide comprehensive insights.

weaknesses:
(1) It appears that the emphasis of this paper leans more towards Data Science or data-driven discovery rather than scientific discovery.

(2) Task Annotation in Section 2.2 seems labor-intensive and time-consuming due to the involvement of identifying code, preprocessing data, implementing code, and writing dataset information. Are there any automated annotation or data collection methods available?

(3) How is the ground truth for each task defined and generated? Are there any automated validation methods that could streamline this process instead of relying solely on multiple rounds of manual validation by annotators?

(4) Could you elaborate on how the evaluation criteria outlined in Table 1 were established?

(5) Regarding the validation of generated Python programs during inference and the utilization of CodeBERTScore to assess token-level embeddings, have you considered employing a self-consistency strategy to validate multiple outputs over time?

(6) How is the validity of outputs generated by GPT-4o for the four heterogeneous datasets depicted in Figure 1 verified?

(7) Given the focus on code generation for data science, have you considered evaluating or providing the performance of code generation models like Codellama and DeepSeek-Coder?

(8) There appears to be inconsistency in the citation format, as observed in instances such as line 249 and line 251. Would it be possible to ensure uniformity in citation formatting throughout the paper?

questions:
Please see the Weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730607891446

thread_type:
official_review

<thread_object>
creator: reviewer_fdqa

<content>
summary:
The ScienceAgentBench framework is introduced in this paper to assess the data-driven scientific discovery capabilities of LLM models. The framework offers both end-to-end and fine-grained metrics in evaluations. Significant room for improvement in scientific tasks was confirmed by implementing the benchmark on various sota models. The benchmark has the potential to serve as a long-term progress indicator for LLM models on scientific reasoning capabilities.

strengths:
The study involved extensive data curation and human annotation, demonstrating the authors' dedication and thoroughness. The inclusion of both end-to-end and fine-grained metrics allows for a comprehensive evaluation of models, particularly when the models can only partially solve a problem. Additionally, the exploration and discussion of various interaction methods with the local environment provides valuable insights.

weaknesses:
Coding generation-related tasks may not be representative of some other scientific domains. While recent research has focused on such tasks, the authors could briefly acknowledge this limitations, especially since the benchmark's name suggests a more comprehensive evaluation of broader scientific capabilities.

questions:
Why was VER chosen over CBS when ranking models? High VER but low CBS could still indicate good context understanding, though poor execution. Was it considered to use heuristics / weighted sum to combine all metrics in the final evaluation?

Will setting CBS to 1.0 when SR is 1 introduce bias into the metric? Some argue that this specific treatment can skew the metric's results. While CBS may not be ideal when the model employs a different approach than annotation but still arrives at the correct answer, setting it to 1.0 could lead to inconsistent score interpretations. Additionally, if the ranking is order-based, this specific treatment might not have a significant impact.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730673141444

thread_type:
official_review

<thread_object>
creator: reviewer_c5sh

<content>
summary:
This paper introduces ScienceAgentBench, a new benchmark designed to test how well language agents can handle tasks in data-driven scientific discovery. The authors collected 102 tasks from 44 peer-reviewed papers across four scientific fields: Bioinformatics, Computational Chemistry, Geographical Information Science, and Psychology & Cognitive Neuroscience. Each task asks the agents to write self-contained Python programs to perform specific scientific activities like data processing, model development, analysis, and visualization.

To ensure the tasks are authentic and to prevent issues with data contamination, the authors involved experts from the respective fields and modified datasets so agents couldn't rely on memorized code. They evaluated five large language models using three different frameworks: direct prompting, OpenHands, and self-debug. The results showed that the best-performing agent could complete only about one-third of the tasks. This highlights the current limitations of language agents in fully automating data-driven scientific discovery and suggests that more advancements are needed.

strengths:
The paper introduces ScienceAgentBench, a novel benchmark for evaluating language agents in data-driven scientific discovery tasks. By incorporating tasks from four diverse scientific disciplines—Bioinformatics, Computational Chemistry, Geographical Information Science, and Psychology & Cognitive Neuroscience—it creatively applies language agents to new domains, filling a gap where existing benchmarks fall short.

The benchmark is rigorously developed with input from nine subject matter experts, ensuring tasks are authentic and challenging. The authors proactively mitigate data contamination by modifying datasets, enhancing the reliability of their evaluation. They use comprehensive evaluation metrics—including Valid Execution Rate (VER), Success Rate (SR), CodeBERTScore (CBS), and computational costs—to provide a holistic assessment of agent performance.

The paper is well-organized and written, utilizing figures and tables to enhance understanding. The authors provide insightful analyses of experimental results, highlighting why current language agents struggle with these tasks. By releasing all code and data, they promote open science and collaboration, significantly contributing to the advancement of AI in scientific research.

weaknesses:
1. The paper evaluates agents using three frameworks but doesn't justify these choices or explore advanced architectures like ReAct or Toolformer. Without including state-of-the-art frameworks that offer advanced reasoning and tool-use capabilities, the study may not fully assess the agents' potential to handle complex scientific tasks. Incorporating such frameworks could provide deeper insights into their capabilities and limitations.
2. Human evaluators who also participated in data collection may introduce bias due to familiarity with the tasks, affecting the objectivity of the assessments. Additionally, the error analysis lacks depth, as specific failure modes are not thoroughly examined. Involving independent evaluators and conducting a detailed error analysis would improve objectivity and help identify areas where agents struggle.
3. The paper doesn't compare the agents' performance with traditional methods or domain-specific tools, making it difficult to assess their practical utility relative to existing solutions. Including such comparisons would provide valuable context to evaluate the agents' real-world usefulness and guide future improvements.
4. Providing expert domain knowledge doesn't consistently improve agent performance and sometimes even decreases it, suggesting agents struggle to integrate this information effectively. Exploring why agents fail to benefit from expert knowledge could lead to better integration strategies and enhance their overall performance.

questions:
1. Have you considered evaluating state-of-the-art frameworks like ReAct or Toolformer incorporating advanced reasoning and tool-use capabilities? Including these could offer deeper insights into the agents' performance on complex tasks.
2. Since evaluators were also involved in data collection, how did you mitigate potential assessment bias? Would involving independent evaluators improve objectivity?
3. Could you provide a more detailed analysis of the standard failure modes encountered by the agents? Understanding specific errors might help identify areas for improvement.
4. Have you compared the agents' performance with traditional methods or domain-specific tools? Including such comparisons could help assess their practical utility relative to existing solutions.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732161870652

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Manuscript Updated By Authors

comment:
We would like to thank the reviewers again for their constructive feedback. We have revised our paper to reflect some of the suggestions made by the reviewers:

- In the main text, we have added more references to content in Appendix A, C and D. 
- We have moved the discussion of our future work from Section 6 to Appendix A. We have also added more discussion on agent safety, especially our rationales why this work introduces limited or no risk in inadvertently synthesizing toxic or dangerous chemicals.
- We have added Appendix C for more details about how we defined the annotated programs and established success criteria in our benchmark construction process.
- We have also included our detailed error analysis of agent trajectories as Appendix D.2.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732679471718

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Summary of Author Responses and Gentle Reminder of Discussion

comment:
Dear Reviewers,

Since the author-reviewer discussion period is extended for one week, we would like to gently remind you again of our responses and appreciate any acknowledgements or follow-up discussions. Here, we summarize the contributions of our work and our responses to each reviewer:

### Contributions
***

1. We present ScienceAgentBench, a rigorously developed benchmark for evaluating language agents on data-driven scientific discovery tasks. We involve extensive human annotation efforts and include nine subject-matter experts to ensure data quality and scientific authenticity, as well as proactively mitigating data contamination risks.
2. We comprehensively evaluate existing state-of-the-art LLMs and agents with different metrics, provide insightful analysis of our experimental results, and point out potential future directions in developing and evaluating agents for scientific discovery.

### Response to Reviewer c5sH
***

We have reached an agreement with the reviewer that OpenHands CodeAct evaluated in this work is one of the state-of-the-art frameworks, which incorporates both ReAct-style reasoning and Toolformer-like tool-use capabilities. Additionally, we provide a detailed analysis of agent trajectories and clarify that better expert knowledge integration is out of the scope of this benchmark paper. Since we have addressed most of the reviewer's concerns, we appreciate it if the reviewer could adjust their assessment accordingly.

We encourage the reviewer to follow up on our discussion and name one of the "traditional methods or domain-specific tools" they have in mind. To our understanding, methods or tools for reliable code generation simply do not exist other than LLMs. 

### Response to Reviewer fdqA
***

We clarify our consensus with Reviewer fdqA that while code generation is necessary for data-driven scientific discovery, it may not be representative of some other scientific domains. We kindly refer the reviewer to Appendix A for relevant discussions on this limitation and why we focus on code generation for data-driven discovery. Besides, we provide detailed responses to Reviewer fdqA's other questions.

### Response to Reviewer Ws3K
***

We kindly point out that the extensive time, labor, and multi-round validation efforts in this work are important contributions rather than weaknesses. To our knowledge, methods to automate the annotation or validation process do not exist (not to mention replacing humans, especially subject-matter experts). We also clarify that our work contributes an evaluation benchmark to assess existing language agents rigorously, which has minimal or no risk in "inadvertently synthesizing toxic or dangerous chemicals."

We have provided detailed responses to other concerns and questions of this reviewer, including the relationship between data-driven discovery and scientific discovery, annotation and evaluation details, and citation formats. We would love to hear back from the reviewer and discuss any remaining concerns.

Sincerely,

Authors of Submission12844
</content>
</thread_object>

</thread>


Metareview:
metareview:
We recommend the paper to be accepted for Poster.

The contribution seems timely and addresses some concerns in the literature. 

Below more details about this contribution. 

The paper introduces ScienceAgentBench, a new benchmark for evaluating language agents for data-driven scientific discovery. To ensure the scientific authenticity and real-world relevance of our benchmark, we extract 102 tasks from 44 peer-reviewed publications in four disciplines and engage nine subject matter experts to validate them. 

The key strengths (S#) of the paper are as follow: 

- (S1)	The paper creatively applies language agents to new domains, filling a gap where existing benchmarks fall short. 
- (S2)	The benchmark is rigorously developed with input from nine subject matter experts, ensuring tasks are authentic and challenging. 
- (S3)	The authors proactively mitigate data contamination by modifying datasets, enhancing the reliability of their evaluation. They use comprehensive evaluation metrics—including Valid Execution Rate (VER), Success Rate (SR), CodeBERTScore (CBS), and computational costs—to provide a holistic assessment of agent performance.
- (S4)	The study involved extensive data curation and human annotation, demonstrating the authors' dedication and thoroughness. The inclusion of both end-to-end and fine-grained metrics allows for a comprehensive evaluation of models, particularly when the models can only partially solve a problem. Additionally, the exploration and discussion of various interaction methods with the local environment provides valuable insights.

The key weaknesses (W#) of the paper are as follows: 

- (W1)	The paper evaluates agents using three frameworks but doesn't justify these choices or explore advanced architectures like ReAct or Toolformer. Without including state-of-the-art frameworks that offer advanced reasoning and tool-use capabilities, the study may not fully assess the agents' potential to handle complex scientific tasks. Incorporating such frameworks could provide deeper insights into their capabilities and limitations.
- (W2)	Human evaluators who also participated in data collection may introduce bias due to familiarity with the tasks, affecting the objectivity of the assessments. Additionally, the error analysis lacks depth, as specific failure modes are not thoroughly examined. Involving independent evaluators and conducting a detailed error analysis would improve objectivity and help identify areas where agents struggle.
- (W3)	The paper doesn't compare the agents' performance with traditional methods or domain-specific tools, making it difficult to assess their practical utility relative to existing solutions. Including such comparisons would provide valuable context to evaluate the agents' real-world usefulness and guide future improvements.
- (W4)	Providing expert domain knowledge doesn't consistently improve agent performance and sometimes even decreases it, suggesting agents struggle to integrate this information effectively. Exploring why agents fail to benefit from expert knowledge could lead to better integration strategies and enhance their overall performance.
- (W5)	Coding generation-related tasks may not be representative of some other scientific domains. While recent research has focused on such tasks, the authors could briefly acknowledge this limitations, especially since the benchmark's name suggests a more comprehensive evaluation of broader scientific capabilities.

We note that the authors addressed many of the reviewers concerns.