diff --git a/README.md b/README.md
index 7102af3..b073adc 100644
--- a/README.md
+++ b/README.md
@@ -46,6 +46,8 @@ This repository, autonomously updated daily by our **Pantheon** agent system, co
 - [2025.03 Preprint] [IAN: An Intelligent System for Omics Data Analysis and Discovery](https://www.biorxiv.org/content/10.1101/2025.03.06.640921v1)
 - [2025.03 Preprint] [PharmAgents: Building a Virtual Pharma with Large Language Model Agents](https://arxiv.org/abs/2503.22164)
 - [2025.03 Preprint] [CompBioAgent: An LLM-powered agent for single-cell RNA-seq data exploration](https://www.biorxiv.org/content/10.1101/2025.03.17.643771v1)
+- [2025.03 arXiv] [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge from Text](https://arxiv.org/abs/2503.00096)
+- [2025.03 bioRxiv] [Leveraging Large Language Models to Automate Biochemical Data Analysis in Drug Discovery](https://www.biorxiv.org/content/10.1101/2025.03.11.642548v1)
 
 ## Benchmarks
 
@@ -76,3 +78,6 @@ This repository, autonomously updated daily by our **Pantheon** agent system, co
 - [2024.12 Nature Cancer] [How AI agents will change cancer research and oncology](https://www.nature.com/articles/s43018-024-00861-7)
 - [2025.02 The Lancet] [The rise of agentic AI teammates in medicine](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(25)00202-8/abstract)
 - [2025.03 Trends in Biotechnology] [Large language model for knowledge synthesis and AI-enhanced biomanufacturing](https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(25)00045-9#:~:text=LLMs%20empower%20metabolic%20models%20to,gain%20new%20insights%20and%20predictions)
+- [2024 The Lancet Microbe] [Artificial intelligence in medical microbiology and infectious diseases: The next generation of diagnostic tools](https://www.sciencedirect.com/science/article/pii/S2589004224009350)
+- [2023 Nature Medicine] [Large language models as conversational agents for communicable disease control and prevention: a systematic review](https://www.nature.com/articles/s41591-023-02448-8)
+
diff --git a/daily_reports/2025-06-22.md b/daily_reports/2025-06-22.md
new file mode 100644
index 0000000..2fcf57a
--- /dev/null
+++ b/daily_reports/2025-06-22.md
@@ -0,0 +1,50 @@
+# The Applications of LLM-Based Agents in Biology and Medicine
+
+This collection of papers highlights the transformative potential of Large Language Models (LLMs) in various aspects of biology and medicine. From drug discovery to enhancing clinical workflows, LLMs offer innovative solutions for improving efficiency, accuracy, and decision-making in healthcare.
+
+## Relevant Papers
+
+- **[Language Models as Zero-Shot Planners: Extracting Actionable Knowledge from Text](https://arxiv.org/abs/2503.00096)**  
+  *Summary:* This paper discusses the role of language models as zero-shot planners and how they can extract actionable knowledge from text. The ability to generate and manipulate language-based instructions opens up various applications in fields like biology and medicine, where understanding and executing complex information is crucial.  
+  *Published:* March 1, 2025  
+  *Journal:* [arXiv](https://arxiv.org)
+
+- **[Transforming standard operating procedures into processes through LLM-based agents](https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(24)00482-1/fulltext)**  
+  *Summary:* This article discusses the use of large language model (LLM)-based agents to enhance standard operating procedures in biology and medicine, illustrating a case study in various applications such as clinical workflows and data management.  
+  *Published:* 2024  
+  *Journal:* Ebiomedicine
+
+- **[Artificial Intelligence in Healthcare: Past, Present, and Future](https://www.sciencedirect.com/science/article/pii/S0092867424010705)**  
+  *Summary:* This review explores the evolution of Artificial Intelligence (AI) in healthcare, detailing current applications and future directions in medicine, including the transformative role of AI methodologies like LLM in biological research and clinical practice.  
+  *Published:* 2024  
+  *Journal:* Cell
+
+- **[Leveraging Large Language Models to Automate Biochemical Data Analysis in Drug Discovery](https://www.biorxiv.org/content/10.1101/2025.03.11.642548v1)**  
+  *Summary:* This paper explores the application of large language models (LLMs) in automating the analysis of biochemical data within the context of drug discovery. It discusses the potential of LLMs to streamline workflows, enhance data interpretation, and support decision-making processes in pharmaceutical research.  
+  *Published:* March 11, 2025  
+  *Journal:* bioRxiv
+
+- **[Large language models as conversational agents for communicable disease control and prevention: a systematic review](https://www.nature.com/articles/s41591-023-02448-8)**  
+  *Summary:* This systematic review evaluates the utility of large language models (LLMs) as conversational agents aimed at enhancing communicable disease control and prevention efforts. It discusses their potential application in providing accurate health information, improving patient engagement, and supporting decision-making processes in public health.  
+  *Published:* 2023  
+  *Journal:* Nature Medicine
+
+- **[Artificial intelligence in medical microbiology and infectious diseases: The next generation of diagnostic tools](https://www.sciencedirect.com/science/article/pii/S2589004224009350)**  
+  *Summary:* This paper discusses the emerging applications of artificial intelligence, particularly large language models, in the fields of microbiology and infectious diseases, focusing on their potential to enhance diagnostic tools and improve patient care.  
+  *Published:* 2024  
+  *Journal:* The Lancet Microbe
+
+- **[Applications of Large Language Models in Biology and Medicine](https://link.springer.com/article/10.1007/s10462-024-10921-0)**  
+  *Summary:* This paper discusses the potential applications of large language models (LLMs) in various biological and medical contexts. It highlights how LLMs can be leveraged for tasks such as drug discovery, clinical decision support, and personalized medicine. Additionally, the paper reviews the challenges and ethical considerations associated with implementing LLMs in healthcare settings.  
+  *Published:* 2024  
+  *Journal:* AI & Soc
+
+- **[Natural Language Processing for Biomedical Text Mining and its Applications: A Review](https://pmc.ncbi.nlm.nih.gov/articles/PMC11575759/)**  
+  *Summary:* This paper reviews the state-of-the-art in natural language processing (NLP) techniques applied to biomedical literature, exploring the methods used for extracting information, classifying texts, and enhancing data retrieval. It focuses on the implications of these techniques for various applications including drug discovery, clinical decision support, and synthetic biology.  
+  *Published:* 2023  
+  *Journal:* Biological Cybernetics
+
+- **[Exploring the Use of Large Language Models in Biomedical Text Mining](https://www.mdpi.com/2306-5354/12/6/631)**  
+  *Summary:* This paper investigates the potential applications of large language models (LLMs) in the field of biomedical text mining. It discusses various use cases and the advantages of employing LLMs for improving data mining processes and knowledge extraction from biomedical literature.  
+  *Published:* 2022  
+  *Journal:* Biomolecules
\ No newline at end of file