A curated list of LLM powered AI Agents in Biomedical Research. Medical Image Analysis, Multi-omics Genomics Analysis, Biomedical Scientific Discoveries...
82
25 commits
updated Jul 28, 2026
README
Awesome AI Agents for Scientific Discovery 
A curated list of papers about AI agents for scientific discovery and research automation.
Maintained by Jieli Zhou
If you use this paper list for your research, please cite it using:
@misc{zhou2024awesome,
title={Awesome AI Agents for Scientific Discovery},
author={Zhou, Jieli},
year={2024},
publisher={GitHub},
journal={GitHub repository},
howpublished={\url{https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery}}
}
Introduction
The convergence of large language models (LLMs) and autonomous agents has ushered in a new era in scientific discovery, fundamentally transforming how research is conducted across disciplines. This emerging paradigm, articulated in Kitano's seminal "Nobel Turing Challenge" (2021), envisions AI systems capable of making scientific discoveries worthy of Nobel Prize recognition. Recent advances in LLM-based agents have brought us closer to this vision, enabling increasingly sophisticated automation of scientific workflows and decision-making processes.
Evolution and Current Landscape
The field has evolved rapidly since early visions of AI-driven scientific discovery. While traditional AI systems focused on narrow tasks, modern LLM-based agents demonstrate remarkable capabilities in complex scientific reasoning, experimental design, and hypothesis generation. The breakthrough capabilities of models like GPT-4 have catalyzed this transition, enabling agents to engage in sophisticated scientific discourse, interpret complex data, and even design novel experiments.
Key Research Directions
Several major research themes have emerged in this space:
-
Multi-Agent Architectures: Research has increasingly focused on collaborative multi-agent systems, where specialized agents work together to tackle complex scientific problems.
-
Domain-Specific Applications: The healthcare sector has seen particularly rapid adoption, with agents being developed for clinical decision support, medical diagnosis, and healthcare administration.
-
Scientific Process Automation: Agents are being developed to automate various aspects of the research pipeline, from literature review and hypothesis generation to experimental design and data analysis.
Impact and Future Directions
The emergence of AI agents in scientific discovery represents more than just technological advancement; it signals a fundamental shift in how science is conducted. These systems promise to:
- Accelerate the pace of scientific discovery
- Enable exploration of previously intractable research questions
- Democratize access to scientific expertise
- Foster more efficient use of research resources
Awesome LLM Agents for Scientific Discovery
A curated list of papers about AI agents for scientific discovery and research automation.
Maintained by Jieli Zhou
If you use this paper list for your research, please cite it using:
@misc{zhou2024awesome,
title={Awesome AI Agents for Scientific Discovery},
author={Zhou, Jieli},
year={2024},
publisher={GitHub},
journal={GitHub repository},
howpublished={\url{https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery}}
}
Introduction
The convergence of large language models (LLMs) and autonomous agents has ushered in a new era in scientific discovery, fundamentally transforming how research is conducted across disciplines. This emerging paradigm, articulated in Kitano's seminal "Nobel Turing Challenge" (2021), envisions AI systems capable of making scientific discoveries worthy of Nobel Prize recognition. Recent advances in LLM-based agents have brought us closer to this vision, enabling increasingly sophisticated automation of scientific workflows and decision-making processes.
Evolution and Current Landscape
The field has evolved rapidly since early visions of AI-driven scientific discovery. While traditional AI systems focused on narrow tasks, modern LLM-based agents demonstrate remarkable capabilities in complex scientific reasoning, experimental design, and hypothesis generation. The breakthrough capabilities of models like GPT-4 have catalyzed this transition, enabling agents to engage in sophisticated scientific discourse, interpret complex data, and even design novel experiments.
Key Research Directions
Several major research themes have emerged in this space:
-
Multi-Agent Architectures: Research has increasingly focused on collaborative multi-agent systems, where specialized agents work together to tackle complex scientific problems.
-
Domain-Specific Applications: The healthcare sector has seen particularly rapid adoption, with agents being developed for clinical decision support, medical diagnosis, and healthcare administration.
-
Scientific Process Automation: Agents are being developed to automate various aspects of the research pipeline, from literature review and hypothesis generation to experimental design and data analysis.
Impact and Future Directions
The emergence of AI agents in scientific discovery represents more than just technological advancement; it signals a fundamental shift in how science is conducted. These systems promise to:
- Accelerate the pace of scientific discovery
- Enable exploration of previously intractable research questions
- Democratize access to scientific expertise
- Foster more efficient use of research resources
Table of Contents
- Foundations & Vision
- Core Technologies
- Scientific Process Automation
- Domain Applications
- Infrastructure & Tools
- AI Agent Frameworks & Tools
- Evaluation & Benchmarking
- Surveys & Reviews
Foundations & Vision
Vision Papers
-
Nobel Turing Challenge: Creating the Engine for Scientific Discovery
Hiroaki Kitano. NPJ Systems Biology and Applications 2021 -
Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery
Hiroaki Kitano. AI Magazine 2016 -
The AI Scientist: Towards Fully Automated Open-ended Scientific Discovery
Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha. arXiv 2024 -
Emergent autonomous scientific research capabilities of large language models
Daniil A Boiko, Robert MacKnight, Gabe Gomes. arXiv 2023 -
What is missing in autonomous discovery: open challenges for the community
Phillip M Maffettone, Pascal Friederich, Sterling G Baird, et al. Digital Discovery 2023 -
The future of fundamental science led by generative closed-loop artificial intelligence
Hector Zenil, Jesper Tegnér, Felipe S Abrahão, Alexander Lavin, et al. arXiv 2023
Core Technologies
Multi-Agent Systems & Architectures
-
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem. NeurIPS 2023 -
Dynamic LLM-Agent Network: An LLM-Agent Collaboration Framework with Agent Team Optimization
Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, Diyi Yang. arXiv 2023 -
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, et al. arXiv 2023
Reasoning & Knowledge Systems
-
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, et al. AAAI 2024 -
KnowAgent: Knowledge-augmented Planning for LLM-based Agents
Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, et al. arXiv 2024 -
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch. arXiv 2023
Scientific Process Automation
Research Planning & Literature Review
-
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, Sung Ju Hwang. arXiv 2024 -
SciMon: Scientific Inspiration Machines Optimized for Novelty
Qingyun Wang, Doug Downey, Heng Ji, Tom Hope. arXiv 2023 -
AutoSurvey: Large Language Models Can Automatically Write Surveys
Yidong Wang, Qi Guo, Wenjin Yao, Hongbo Zhang, et al. arXiv 2024
Experimental Design & Workflow
-
DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents
Peter Jansen, Marc-Alexandre Côté, Tushar Khot, Erin Bransom, et al. arXiv 2024 -
Genesis: Towards the Automation of Systems Biology Research Ievgeniia A Tiukova, Daniel Brunnsåker, Erik Y Bjurström, Alexander H Gower, et al. arXiv 2024
-
AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing (code) Jianda Du, Youran Sun, Haizhao Yang. arXiv 2026
Domain Applications
Healthcare & Medicine
Clinical Decision Support & Diagnosis
-
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, et al. NeurIPS 2024 -
Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis
Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, et al. arXiv 2024 -
MedAide: Towards an Omni Medical Aide via Specialized LLM-based Multi-Agent Collaboration
Jinjie Wei, Dingkang Yang, Yanshu Li, Qingyao Xu, et al. arXiv 2024 -
Large Language Models as Agents in the Clinic
Nikita Mehandru, Brenda Y. Miao, Eduardo Rodriguez Almaraz, et al. NPJ Digital Medicine 2024 -
MAGDA: Multi-Agent Guideline-Driven Diagnostic Assistance
David Bani-Harouni, Nassir Navab, Matthias Keicher. FMGMAI 2024
Healthcare Systems & Management
-
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration
Zixiang Wang, Yinghao Zhu, Huiya Zhao, Xiaochen Zheng, et al. arXiv 2024 -
Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
Junkai Li, Siyu Wang, Meng Zhang, Weitao Li, et al. arXiv 2024 -
ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World
Weixiang Yan, Haitian Liu, Tengxiao Wu, Qian Chen, et al. arXiv 2024 -
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow
Huizi Yu, Jiayan Zhou, Lingyao Li, Shan Chen, et al. arXiv 2024
Medical Education & Training
-
Medco: Medical education copilots based on a multi-agent framework
Hao Wei, Jianing Qiu, Haibao Yu, Wu Yuan. arXiv 2024 -
AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments
Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, et al. arXiv 2024
Medical Imaging & Pathology
-
CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting
Naman Sharma. arXiv 2024 -
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
Yuxuan Sun, Yunlong Zhang, Yixuan Si, Chenglu Zhu, et al. arXiv 2024
Medical Research
- OpenLens AI: Fully Autonomous Research Agent for Health Infomatics
Yuxiao Cheng, Jinli Suo arXiv 2025, GitHub Repo
Biology & Life Sciences
Genomics & Molecular Biology
-
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Yusuf Roohani, Andrew Lee, Qian Huang, Jian Vora, et al. arXiv 2024 -
GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases
Zhizheng Wang, Qiao Jin, Chih-Hsuan Wei, Shubo Tian, et al. arXiv 2024 -
Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation
Biqing Qi, Kaiyan Zhang, Kai Tian, Haoxiang Li, et al. arXiv 2024
Bioinformatics Tools & Platforms
-
BIA: BioInformatics Agent - Unleashing the Power of Large Language Models to Reshape Bioinformatics Workflow
Qi Xin, Quyu Kong, Hongyi Ji, Yue Shen, et al. bioRxiv 2024 -
CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis
Yihang Xiao, Jinyi Liu, Yan Zheng, Xiaohan Xie, et al. bioRxiv 2024 -
SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing Devam Mondal, Atharva Inamdar. arXiv 2024
-
ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics Chen Yang, Xianyang Zhang, Jun Chen. bioRxiv 2026 An MCP server that enables spatial transcriptomics analysis via natural language, integrating 60+ methods across Python and R into a single conversational workflow. Code
Chemistry & Materials Science
Drug Discovery & Development
-
DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning
Yoshitaka Inoue, Tianci Song, Tianfan Fu. arXiv 2024 -
Malade: Orchestration of LLM-powered agents with retrieval augmented generation for pharmacovigilance
Jihye Choi, Nils Palumbo, Prasad Chalasani, Matthew M Engelhard, et al. arXiv 2024
Molecular Modeling & Computation
-
ChatMol Copilot: An Agent for Molecular Modeling and Computation Powered by LLMs
Jinyuan Sun, Auston Li, Yifan Deng, Jiabo Li. L+M Workshop 2024 -
A review of large language models and autonomous agents in chemistry
Mayk Caldas Ramos, Christopher J Collison, Andrew D White. arXiv 2024
Earth & Environmental Sciences
- An LLM Agent for Automatic Geospatial Data Analysis
Yuxing Chen, Weijie Wang, Sylvain Lobry, Camille Kurtz. arXiv 2024
Evaluation & Benchmarking
General Benchmarks
-
ClawBench: A Comprehensive Benchmark for Evaluating AI Web Agents
Reacher et al. arXiv 2026. An open benchmark for browser agents on everyday tasks across live websites, with 153 V1 and 130 V2 tasks and reproducible execution traces (code, project). -
AgentBench: Evaluating LLMs as Agents
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, et al. arXiv 2023 -
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, et al. arXiv 2023 -
Benchmarking large language models as ai research agents
Qian Huang, Jian Vora, Percy Liang, Jure Leskovec. NeurIPS 2023 Workshop
Domain-Specific Benchmarks
-
BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science
Xinna Lin, Siqi Ma, Junjie Shan, Xiaojing Zhang, et al. arXiv 2024 -
GenoTEX: A Benchmark for Evaluating LLM-Based Exploration of Gene Expression Data
Haoyang Liu, Haohan Wang. arXiv 2024 -
IdeaBench: Benchmarking Large Language Models for Research Idea Generation
Sikun Guo, Amir Hassan Shariatmadari, Guangzhi Xiong, Albert Huang, et al. arXiv 2024
Surveys & Reviews
Comprehensive Surveys
-
Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, et al. Nature 2023 -
The rise and potential of large language model based agents: A survey
Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, et al. arXiv 2023 -
Large language model based multi-agents: A survey of progress and challenges
Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, et al. arXiv 2024 -
A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges
Xinyi Li, Sai Wang, Siqi Zeng, Yu Wu, Yi Yang. Vicinagearth 2024
Domain-Specific Reviews
-
AI for Biomedicine in the Era of Large Language Models
Zhenyu Bi, Sajib Acharjee Dip, Daniel Hajialigol, Sindhura Kommu, et al. arXiv 2024 -
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
Lei Liu, Xiaoyan Yang, Junchi Lei, Xiaoyang Liu, et al. arXiv 2024 -
From LLMs to LLM-based Agents for Software Engineering: A Survey
Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, et al. arXiv 2024
AI Agent Frameworks & Tools
- Bride — Cognitive AI agent with Active Inference, HDC, and anomaly detection for hypothesis generation. [Rust, MIT]
2026
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A curated list of LLM powered AI Agents in Biomedical Research. Medical Image Analysis, Multi-omics Genomics Analysis, Biomedical Scientific Discoveries...
82
25 commits
updated Jul 28, 2026
README
Awesome AI Agents for Scientific Discovery
A curated list of papers about AI agents for scientific discovery and research automation.
Maintained by Jieli Zhou
If you use this paper list for your research, please cite it using:
@misc{zhou2024awesome,
title={Awesome AI Agents for Scientific Discovery},
author={Zhou, Jieli},
year={2024},
publisher={GitHub},
journal={GitHub repository},
howpublished={\url{https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery}}
}
Introduction
The convergence of large language models (LLMs) and autonomous agents has ushered in a new era in scientific discovery, fundamentally transforming how research is conducted across disciplines. This emerging paradigm, articulated in Kitano's seminal "Nobel Turing Challenge" (2021), envisions AI systems capable of making scientific discoveries worthy of Nobel Prize recognition. Recent advances in LLM-based agents have brought us closer to this vision, enabling increasingly sophisticated automation of scientific workflows and decision-making processes.
Evolution and Current Landscape
The field has evolved rapidly since early visions of AI-driven scientific discovery. While traditional AI systems focused on narrow tasks, modern LLM-based agents demonstrate remarkable capabilities in complex scientific reasoning, experimental design, and hypothesis generation. The breakthrough capabilities of models like GPT-4 have catalyzed this transition, enabling agents to engage in sophisticated scientific discourse, interpret complex data, and even design novel experiments.
Key Research Directions
Several major research themes have emerged in this space:
-
Multi-Agent Architectures: Research has increasingly focused on collaborative multi-agent systems, where specialized agents work together to tackle complex scientific problems.
-
Domain-Specific Applications: The healthcare sector has seen particularly rapid adoption, with agents being developed for clinical decision support, medical diagnosis, and healthcare administration.
-
Scientific Process Automation: Agents are being developed to automate various aspects of the research pipeline, from literature review and hypothesis generation to experimental design and data analysis.
Impact and Future Directions
The emergence of AI agents in scientific discovery represents more than just technological advancement; it signals a fundamental shift in how science is conducted. These systems promise to:
- Accelerate the pace of scientific discovery
- Enable exploration of previously intractable research questions
- Democratize access to scientific expertise
- Foster more efficient use of research resources
Awesome LLM Agents for Scientific Discovery
A curated list of papers about AI agents for scientific discovery and research automation.
Maintained by Jieli Zhou
If you use this paper list for your research, please cite it using:
@misc{zhou2024awesome,
title={Awesome AI Agents for Scientific Discovery},
author={Zhou, Jieli},
year={2024},
publisher={GitHub},
journal={GitHub repository},
howpublished={\url{https://github.com/zhoujieli/Awesome-LLM-Agents-Scientific-Discovery}}
}
Introduction
The convergence of large language models (LLMs) and autonomous agents has ushered in a new era in scientific discovery, fundamentally transforming how research is conducted across disciplines. This emerging paradigm, articulated in Kitano's seminal "Nobel Turing Challenge" (2021), envisions AI systems capable of making scientific discoveries worthy of Nobel Prize recognition. Recent advances in LLM-based agents have brought us closer to this vision, enabling increasingly sophisticated automation of scientific workflows and decision-making processes.
Evolution and Current Landscape
The field has evolved rapidly since early visions of AI-driven scientific discovery. While traditional AI systems focused on narrow tasks, modern LLM-based agents demonstrate remarkable capabilities in complex scientific reasoning, experimental design, and hypothesis generation. The breakthrough capabilities of models like GPT-4 have catalyzed this transition, enabling agents to engage in sophisticated scientific discourse, interpret complex data, and even design novel experiments.
Key Research Directions
Several major research themes have emerged in this space:
-
Multi-Agent Architectures: Research has increasingly focused on collaborative multi-agent systems, where specialized agents work together to tackle complex scientific problems.
-
Domain-Specific Applications: The healthcare sector has seen particularly rapid adoption, with agents being developed for clinical decision support, medical diagnosis, and healthcare administration.
-
Scientific Process Automation: Agents are being developed to automate various aspects of the research pipeline, from literature review and hypothesis generation to experimental design and data analysis.
Impact and Future Directions
The emergence of AI agents in scientific discovery represents more than just technological advancement; it signals a fundamental shift in how science is conducted. These systems promise to:
- Accelerate the pace of scientific discovery
- Enable exploration of previously intractable research questions
- Democratize access to scientific expertise
- Foster more efficient use of research resources
Table of Contents
- Foundations & Vision
- Core Technologies
- Scientific Process Automation
- Domain Applications
- Infrastructure & Tools
- AI Agent Frameworks & Tools
- Evaluation & Benchmarking
- Surveys & Reviews
Foundations & Vision
Vision Papers
-
Nobel Turing Challenge: Creating the Engine for Scientific Discovery
Hiroaki Kitano. NPJ Systems Biology and Applications 2021 -
Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery
Hiroaki Kitano. AI Magazine 2016 -
The AI Scientist: Towards Fully Automated Open-ended Scientific Discovery
Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha. arXiv 2024 -
Emergent autonomous scientific research capabilities of large language models
Daniil A Boiko, Robert MacKnight, Gabe Gomes. arXiv 2023 -
What is missing in autonomous discovery: open challenges for the community
Phillip M Maffettone, Pascal Friederich, Sterling G Baird, et al. Digital Discovery 2023 -
The future of fundamental science led by generative closed-loop artificial intelligence
Hector Zenil, Jesper Tegnér, Felipe S Abrahão, Alexander Lavin, et al. arXiv 2023
Core Technologies
Multi-Agent Systems & Architectures
-
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem. NeurIPS 2023 -
Dynamic LLM-Agent Network: An LLM-Agent Collaboration Framework with Agent Team Optimization
Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, Diyi Yang. arXiv 2023 -
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, et al. arXiv 2023
Reasoning & Knowledge Systems
-
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, et al. AAAI 2024 -
KnowAgent: Knowledge-augmented Planning for LLM-based Agents
Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, et al. arXiv 2024 -
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch. arXiv 2023
Scientific Process Automation
Research Planning & Literature Review
-
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, Sung Ju Hwang. arXiv 2024 -
SciMon: Scientific Inspiration Machines Optimized for Novelty
Qingyun Wang, Doug Downey, Heng Ji, Tom Hope. arXiv 2023 -
AutoSurvey: Large Language Models Can Automatically Write Surveys
Yidong Wang, Qi Guo, Wenjin Yao, Hongbo Zhang, et al. arXiv 2024
Experimental Design & Workflow
-
DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents
Peter Jansen, Marc-Alexandre Côté, Tushar Khot, Erin Bransom, et al. arXiv 2024 -
Genesis: Towards the Automation of Systems Biology Research Ievgeniia A Tiukova, Daniel Brunnsåker, Erik Y Bjurström, Alexander H Gower, et al. arXiv 2024
-
AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing (code) Jianda Du, Youran Sun, Haizhao Yang. arXiv 2026
Domain Applications
Healthcare & Medicine
Clinical Decision Support & Diagnosis
-
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, et al. NeurIPS 2024 -
Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis
Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, et al. arXiv 2024 -
MedAide: Towards an Omni Medical Aide via Specialized LLM-based Multi-Agent Collaboration
Jinjie Wei, Dingkang Yang, Yanshu Li, Qingyao Xu, et al. arXiv 2024 -
Large Language Models as Agents in the Clinic
Nikita Mehandru, Brenda Y. Miao, Eduardo Rodriguez Almaraz, et al. NPJ Digital Medicine 2024 -
MAGDA: Multi-Agent Guideline-Driven Diagnostic Assistance
David Bani-Harouni, Nassir Navab, Matthias Keicher. FMGMAI 2024
Healthcare Systems & Management
-
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration
Zixiang Wang, Yinghao Zhu, Huiya Zhao, Xiaochen Zheng, et al. arXiv 2024 -
Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
Junkai Li, Siyu Wang, Meng Zhang, Weitao Li, et al. arXiv 2024 -
ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World
Weixiang Yan, Haitian Liu, Tengxiao Wu, Qian Chen, et al. arXiv 2024 -
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow
Huizi Yu, Jiayan Zhou, Lingyao Li, Shan Chen, et al. arXiv 2024
Medical Education & Training
-
Medco: Medical education copilots based on a multi-agent framework
Hao Wei, Jianing Qiu, Haibao Yu, Wu Yuan. arXiv 2024 -
AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments
Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, et al. arXiv 2024
Medical Imaging & Pathology
-
CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting
Naman Sharma. arXiv 2024 -
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
Yuxuan Sun, Yunlong Zhang, Yixuan Si, Chenglu Zhu, et al. arXiv 2024
Medical Research
- OpenLens AI: Fully Autonomous Research Agent for Health Infomatics
Yuxiao Cheng, Jinli Suo arXiv 2025, GitHub Repo
Biology & Life Sciences
Genomics & Molecular Biology
-
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Yusuf Roohani, Andrew Lee, Qian Huang, Jian Vora, et al. arXiv 2024 -
GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases
Zhizheng Wang, Qiao Jin, Chih-Hsuan Wei, Shubo Tian, et al. arXiv 2024 -
Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation
Biqing Qi, Kaiyan Zhang, Kai Tian, Haoxiang Li, et al. arXiv 2024
Bioinformatics Tools & Platforms
-
BIA: BioInformatics Agent - Unleashing the Power of Large Language Models to Reshape Bioinformatics Workflow
Qi Xin, Quyu Kong, Hongyi Ji, Yue Shen, et al. bioRxiv 2024 -
CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis
Yihang Xiao, Jinyi Liu, Yan Zheng, Xiaohan Xie, et al. bioRxiv 2024 -
SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing Devam Mondal, Atharva Inamdar. arXiv 2024
-
ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics Chen Yang, Xianyang Zhang, Jun Chen. bioRxiv 2026 An MCP server that enables spatial transcriptomics analysis via natural language, integrating 60+ methods across Python and R into a single conversational workflow. Code
Chemistry & Materials Science
Drug Discovery & Development
-
DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning
Yoshitaka Inoue, Tianci Song, Tianfan Fu. arXiv 2024 -
Malade: Orchestration of LLM-powered agents with retrieval augmented generation for pharmacovigilance
Jihye Choi, Nils Palumbo, Prasad Chalasani, Matthew M Engelhard, et al. arXiv 2024
Molecular Modeling & Computation
-
ChatMol Copilot: An Agent for Molecular Modeling and Computation Powered by LLMs
Jinyuan Sun, Auston Li, Yifan Deng, Jiabo Li. L+M Workshop 2024 -
A review of large language models and autonomous agents in chemistry
Mayk Caldas Ramos, Christopher J Collison, Andrew D White. arXiv 2024
Earth & Environmental Sciences
- An LLM Agent for Automatic Geospatial Data Analysis
Yuxing Chen, Weijie Wang, Sylvain Lobry, Camille Kurtz. arXiv 2024
Evaluation & Benchmarking
General Benchmarks
-
ClawBench: A Comprehensive Benchmark for Evaluating AI Web Agents
Reacher et al. arXiv 2026. An open benchmark for browser agents on everyday tasks across live websites, with 153 V1 and 130 V2 tasks and reproducible execution traces (code, project). -
AgentBench: Evaluating LLMs as Agents
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, et al. arXiv 2023 -
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, et al. arXiv 2023 -
Benchmarking large language models as ai research agents
Qian Huang, Jian Vora, Percy Liang, Jure Leskovec. NeurIPS 2023 Workshop
Domain-Specific Benchmarks
-
BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science
Xinna Lin, Siqi Ma, Junjie Shan, Xiaojing Zhang, et al. arXiv 2024 -
GenoTEX: A Benchmark for Evaluating LLM-Based Exploration of Gene Expression Data
Haoyang Liu, Haohan Wang. arXiv 2024 -
IdeaBench: Benchmarking Large Language Models for Research Idea Generation
Sikun Guo, Amir Hassan Shariatmadari, Guangzhi Xiong, Albert Huang, et al. arXiv 2024
Surveys & Reviews
Comprehensive Surveys
-
Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, et al. Nature 2023 -
The rise and potential of large language model based agents: A survey
Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, et al. arXiv 2023 -
Large language model based multi-agents: A survey of progress and challenges
Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, et al. arXiv 2024 -
A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges
Xinyi Li, Sai Wang, Siqi Zeng, Yu Wu, Yi Yang. Vicinagearth 2024
Domain-Specific Reviews
-
AI for Biomedicine in the Era of Large Language Models
Zhenyu Bi, Sajib Acharjee Dip, Daniel Hajialigol, Sindhura Kommu, et al. arXiv 2024 -
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
Lei Liu, Xiaoyan Yang, Junchi Lei, Xiaoyang Liu, et al. arXiv 2024 -
From LLMs to LLM-based Agents for Software Engineering: A Survey
Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, et al. arXiv 2024
AI Agent Frameworks & Tools
- Bride — Cognitive AI agent with Active Inference, HDC, and anomaly detection for hypothesis generation. [Rust, MIT]
2026
Contributing
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