Update public links for Stanford-CongLab

README.md

@@ -23,12 +23,12 @@ tags:
 
 <div align="center">
 
-[![Website](https://img.shields.io/badge/%F0%9F%9A%80%20Website-LabHorizon-00c2a8)](https://conglab-research.github.io/LabHorizon/)&nbsp;
+[![Website](https://img.shields.io/badge/%F0%9F%9A%80%20Website-LabHorizon-00c2a8)](https://stanford-conglab.github.io/LabHorizon/)&nbsp;
 ![arXiv](https://img.shields.io/badge/arXiv-coming%20soon-b31b1b?logo=arxiv&logoColor=white)&nbsp;
-[![Code](https://img.shields.io/badge/Code-LabHorizon-000000?logo=github&logoColor=white)](https://github.com/CongLab-Research/LabHorizon)&nbsp;
-[![Data L1 3D Asset](https://img.shields.io/badge/%F0%9F%A4%97%20Data-L1%203D%20Asset-blue)](https://huggingface.co/datasets/CongLab-Research/LabHorizon-3D-Asset-Perception)&nbsp;
-[![Data L2 Protocol](https://img.shields.io/badge/%F0%9F%A4%97%20Data-L2%20Protocol-purple)](https://huggingface.co/datasets/CongLab-Research/LabHorizon-Protocol-Conditioned-Planning)&nbsp;
-[![Model](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Qwen3.6-orange)](https://huggingface.co/CongLab-Research/LabHorizon-Model)
+[![Code](https://img.shields.io/badge/Code-LabHorizon-000000?logo=github&logoColor=white)](https://github.com/Stanford-CongLab/LabHorizon)&nbsp;
+[![Data L1 3D Asset](https://img.shields.io/badge/%F0%9F%A4%97%20Data-L1%203D%20Asset-blue)](https://huggingface.co/datasets/Stanford-CongLab/LabHorizon-3D-Asset-Perception)&nbsp;
+[![Data L2 Protocol](https://img.shields.io/badge/%F0%9F%A4%97%20Data-L2%20Protocol-purple)](https://huggingface.co/datasets/Stanford-CongLab/LabHorizon-Protocol-Conditioned-Planning)&nbsp;
+[![Model](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Qwen3.6-orange)](https://huggingface.co/Stanford-CongLab/LabHorizon-Model)
 
 **Qwen3.6-35B-A3B LoRA for protocol-conditioned laboratory action prediction**
 
@@ -73,8 +73,8 @@ The adapter is trained on the same public LabHorizon train split described by th
 
 | Level | Hugging Face Dataset | Input | Target | Metric |
 |:---|:---|:---|:---|:---|
-| **Level 1** | [LabHorizon-3D-Asset-Perception](https://huggingface.co/datasets/CongLab-Research/LabHorizon-3D-Asset-Perception) | Three asset views, historical actions, candidate next actions | Gold next action | Next-action accuracy |
-| **Level 2** | [LabHorizon-Protocol-Conditioned-Planning](https://huggingface.co/datasets/CongLab-Research/LabHorizon-Protocol-Conditioned-Planning) | Context, goal, constraints, available inputs, action pool | Gold experimental action sequence | L2 Action Sequence Similarity, L2 Parameter Accuracy |
+| **Level 1** | [LabHorizon-3D-Asset-Perception](https://huggingface.co/datasets/Stanford-CongLab/LabHorizon-3D-Asset-Perception) | Three asset views, historical actions, candidate next actions | Gold next action | Next-action accuracy |
+| **Level 2** | [LabHorizon-Protocol-Conditioned-Planning](https://huggingface.co/datasets/Stanford-CongLab/LabHorizon-Protocol-Conditioned-Planning) | Context, goal, constraints, available inputs, action pool | Gold experimental action sequence | L2 Action Sequence Similarity, L2 Parameter Accuracy |
 
 ## 📦 Model
 
@@ -118,7 +118,7 @@ For Level 1, the evaluator maps the final next action back to the candidate list
 
 ## 🏆 Leaderboard
 
-The tables below report direct-prompting baselines on the same test split used for the trained model comparison. The full code and evaluation scripts are maintained in the [LabHorizon GitHub repository](https://github.com/CongLab-Research/LabHorizon).
+The tables below report direct-prompting baselines on the same test split used for the trained model comparison. The full code and evaluation scripts are maintained in the [LabHorizon GitHub repository](https://github.com/Stanford-CongLab/LabHorizon).
 
 ### 🔬 Level 1: 3D Asset Perception
 
@@ -228,7 +228,7 @@ from transformers import AutoModelForCausalLM, AutoProcessor
 from peft import PeftModel
 
 base_id = "Qwen/Qwen3.6-35B-A3B"
-adapter_id = "CongLab-Research/LabHorizon-Model"
+adapter_id = "Stanford-CongLab/LabHorizon-Model"
 
 processor = AutoProcessor.from_pretrained(adapter_id, trust_remote_code=True)
 base = AutoModelForCausalLM.from_pretrained(
@@ -245,7 +245,7 @@ model = PeftModel.from_pretrained(base, adapter_id)
 Use the public code repository for evaluation and agent workflows:
 
 ```bash
-git clone https://github.com/CongLab-Research/LabHorizon
+git clone https://github.com/Stanford-CongLab/LabHorizon
 cd LabHorizon
 ```