Update README.md

README.md

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 [**📄 Paper (arXiv:2603.28533)**](https://arxiv.org/abs/2603.28533) | [**💻 GitHub**](https://github.com/XuShuwenn/GraphWalker) | [**🤗 Model**](https://huggingface.co/xushuwen23/GraphWalker-7B)
 
-**GraphWalker-7B** is a specialized large language model fine-tuned from [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for **Agentic Knowledge Graph Question Answering (KGQA)**. GraphWalker treats multi-hop KGQA as a sequential graph-walking process, learning to navigate knowledge graphs via synthetic trajectory curriculum — achieving strong generalization with a single, compact 7B model.
+**GraphWalker-7B** is a specialized large language model fine-tuned from [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for **Agentic Knowledge Graph Question Answering (KGQA)**. GraphWalker learns to navigate knowledge graphs via synthetic trajectory curriculum — achieving strong generalization with a single, compact 7B model.
 
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 ## 🌟 Overview
 
-Multi-hop KGQA requires reasoning over complex, interconnected entities across a knowledge graph. Existing approaches either rely on rigid retrieval pipelines or expensive multi-module LLM orchestration. **GraphWalker** addresses this with two core ideas:
-
-1. **Agentic Graph Walking:** The model acts as an agent that iteratively traverses a KG, selecting which edges to follow at each step based on the question and accumulated context — effectively decomposing multi-hop questions into a series of local, grounded decisions.
-
-2. **Synthetic Trajectory Curriculum (STC):** Instead of relying on expensive human-annotated reasoning chains, GraphWalker is trained on *synthetically generated* graph-walking trajectories. The curriculum is structured to progressively increase trajectory complexity, enabling the model to internalize robust multi-hop strategies.
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-Together, these designs allow GraphWalker-7B to outperform much larger models and complex multi-agent systems on standard KGQA benchmarks, while remaining efficient at inference time.
+**GraphWalker** is an agentic framework for multi-turn Knowledge Graph Question Answering (KGQA) over **Global Knowledge Graphs** (e.g., Freebase). It transforms LLMs into reasoning agents that autonomously navigate massive KGs through a "Think-Query-Observe" loop, optimized via a synthetic curriculum.
 
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-## 🔑 Key Features
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-- **Agentic KG Navigation:** Frames KGQA as an iterative, step-by-step graph traversal rather than a single-shot retrieval-and-generate task.
-- **Synthetic Trajectory Curriculum:** Trains on automatically constructed walking trajectories with progressively increasing difficulty, eliminating the need for costly human annotation.
-- **Lazy Greedy Search with Frontier Expansion:** An efficient beam-search-style graph traversal algorithm that maximizes information gain while keeping context size tractable.
-- **Single-Model Efficiency:** Achieves competitive performance with a single 7B model, without multi-agent overhead.
-- **Broad KG Compatibility:** Designed to generalize across standard KGQA benchmarks (e.g., WebQSP, CWQ, GrailQA).
-
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 ## 🛠️ Usage
 
@@ -67,7 +52,7 @@ vllm serve "./GraphWalker-7B" \
     --dtype auto \
     --chat-template "./GraphWalker-7B/chat_template.jinja"
 ```
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+For training and evaluation, see [**💻 GitHub**](https://github.com/XuShuwenn/GraphWalker) for details.
 
 
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