Update README.md

README.md

@@ -11,6 +11,9 @@ tags:
 - Agent
 - arxiv:2508.02258
 ---
+# Patho-AgenticRAG: Towards Multimodal Agentic Retrieval-Augmented Generation for Pathology VLMs via Reinforcement Learning
+\[[Arxiv](https://arxiv.org/abs/2508.02258)\] | \[[Github Repo](https://github.com/Wenchuan-Zhang/Patho-AgenticRAG)] | \[[Cite](#citation)\]
+
 ## Introduction📝
 **Vision Language Models** have demonstrated significant potential in medical imaging tasks, but pathology presents unique challenges due to its ultra-high resolution, complex tissue structures, and nuanced clinical semantics. These challenges often lead to **hallucinations** in VLMs, where the outputs are inconsistent with the visual evidence, undermining clinical trust. Current **Retrieval-Augmented Generation (RAG)** approaches predominantly rely on text-based knowledge bases, limiting their ability to effectively incorporate critical visual information from pathology images.
 
@@ -18,19 +21,19 @@ To address these challenges, we introduce **Patho-AgenticRAG**, a **multimodal R
 
 Our experiments demonstrate that Patho-AgenticRAG significantly outperforms existing multimodal models in many tasks such as multiple-choice diagnosis and visual question answering.
 ![Patho-AgenticRAG Overview](https://github.com/Wenchuan-Zhang/Patho-AgenticRAG/raw/main/docs/casestudy.png)
-### Quickstart🏃
+## Quickstart🏃
 This document outlines the workflow for setting up and running the **Patho-AgenticRAG** framework. The process involves the ingestion of pathology pdf images, model downloads, and serving the models for inference via API servers. Below are the steps to follow:
-## 1. Milvus Ingestion
+### 1. Milvus Ingestion
 To ingest pathology images into Milvus for searching:
 ```bash
 python milvus_ingestion.py
 ```
-## 2. Milvus Search Engine API
+### 2. Milvus Search Engine API
 Next, run the Milvus search engine API to handle the retrieval process:
 ```bash
 python milvus_search_engine_api.py
 ```
-## 3. Model Download
+### 3. Model Download
 Download the necessary models from Hugging Face. These models are critical for the workflow and should be stored locally.
 - Agentic-Router:
 ```bash
@@ -44,7 +47,7 @@ hf download autumncc/Qwen2.5-VL-7B-VRAG --local-dir ./models/Qwen2.5-VL-7B-VRAG
 ```bash
 hf download WenchuanZhang/Patho-R1-7B --local-dir ./models/Patho-R1-7B --token <your-token>
 ```
-## 4. Serving the Models
+### 4. Serving the Models
 You can now serve the models for inference using the following commands:
 - Agentic Router (on CUDA device 1):
 ```bash
@@ -58,7 +61,7 @@ CUDA_VISIBLE_DEVICES=2,3 vllm serve ./models/Qwen2.5-VL-7B-VRAG --port 8003 --ho
 ```bash
 CUDA_VISIBLE_DEVICES=4,5 python3 -m vllm.entrypoints.openai.api_server --model ./models/Patho-R1-7B --tokenizer ./models/Patho-R1-7B --port 8004 --host 0.0.0.0 --served-model-name Patho-R1 --tensor-parallel-size 2
 ```
-## 5. Running the Demo
+### 5. Running the Demo
 Finally, run the Patho-AgenticRAG script for a demo:
 ```bash
 python patho_agenticrag.py