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-license: cc-by-sa-4.0
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: source
-    dtype: string
-  - name: task
-    dtype: string
-  - name: type
-    dtype: string
-  - name: instruction
-    dtype: string
-  - name: question
-    dtype: string
-  - name: options
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: context
-    dtype: string
-  - name: evidence
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 1966003072
-    num_examples: 1934
-  download_size: 660289122
-  dataset_size: 1966003072
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: data/test-*
----
+---
+license: cc-by-sa-4.0
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: source
+      dtype: string
+    - name: task
+      dtype: string
+    - name: type
+      dtype: string
+    - name: instruction
+      dtype: string
+    - name: question
+      dtype: string
+    - name: options
+      dtype: string
+    - name: answer
+      dtype: string
+    - name: context
+      dtype: string
+    - name: evidence
+      dtype: string
+  splits:
+    - name: test
+      num_bytes: 1966003072
+      num_examples: 1934
+  download_size: 660289122
+  dataset_size: 1966003072
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: data/test-*
+---
+
+<div align="center">
+
+<h1><b>LooGLE v2</b></h1>
+
+**The official repository of "LooGLE v2: Are LLMs Ready for Real World Long Dependency Challenges?"**
+
+**NeurIPS DB Track 2025**
+
+<div>
+  <a href="https://huggingface.co/datasets/MulabPKU/LooGLE-v2">
+    <img src="https://img.shields.io/badge/🤗-Dataset-blue" alt="Dataset">
+  </a>
+  <a href="https://mulabpku.github.io/LooGLE-v2/">
+    <img src="https://img.shields.io/badge/🌐-Website-green" alt="Website">
+  </a>
+  <a href="https://arxiv.org/abs/2510.22548">
+    <img src="https://img.shields.io/badge/📄-Paper-red" alt="Paper">
+  </a>
+  <a href="https://opensource.org/licenses/MIT">
+    <img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License">
+  </a>
+</div>
+
+
+
+</div>
+
+---
+
+## 📋 Overview
+
+LooGLE v2 is a comprehensive benchmark designed to evaluate large language models on their ability to understand and process long-context documents with complex dependencies. The benchmark covers diverse domains including **Finance**, **Law**, **Code**, and **Game**.
+
+---
+
+## 🚀 Quick Start
+
+### 📦 Installation
+
+```bash
+# Create environment with Python 3.10
+conda create -n loogle-v2 python=3.10
+conda activate loogle-v2
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Install Flash Attention
+pip install flash-attn==2.6.3 --no-build-isolation
+
+# Or you can download flash_attn-2.6.3-cp310-cp310-linux_x86_64.whl
+pip install flash_attn-2.6.3-cp310-cp310-linux_x86_64.whl
+```
+
+---
+
+## 📊 Dataset
+
+Download the LooGLE v2 dataset from Hugging Face:
+
+```bash
+git clone https://huggingface.co/datasets/MuLabPKU/LooGLE-v2 ./datasets/LooGLE-v2
+# Or use the Hugging Face CLI to download:
+hf download MuLabPKU/LooGLE-v2   --repo-type dataset  --local-dir ./datasets/LooGLE-v2
+```
+
+
+---
+
+## 🛠️ Usage
+
+### ⚙️ Configuration
+
+**vLLM server (for `predict.py`):**
+```bash
+python -m vllm.entrypoints.openai.api_server \
+  --model path/to/your/model \
+  --port 8000 \
+  --max-model-len 131072
+```
+
+**Model entry (`config/models.jsonl`, shared by both scripts):**
+```json
+{
+  "name": "your-model-name",
+  "model": "path/to/model",
+  "max_len": 131072,
+  "base_url": "http://localhost:8000/v1",
+  "api_key": "your-api-key"
+}
+```
+
+Transformers mode (`predict_transformers.py`) does not need a server; it still reuses `name/model/max_len` from this config. Ensure `base_url` matches your vLLM port when using the server route.
+
+### 🔎 Pre-compute RAG Contexts (optional)
+
+If you plan to run `--use_rag`, first generate `context_rag` with the preprocessor:
+
+```bash
+python rag_preprocess.py \
+  --input_path ./datasets/LooGLE-v2 \
+  --split test \
+  --output_path ./datasets/LooGLE-v2/test_rag.jsonl \
+  --embedding_model THUDM/LongCite-glm4-9b \
+  --devices 0,1
+```
+
+For multi-turn refinement (using a generator model to iteratively improve retrieval queries):
+
+```bash
+python rag_preprocess.py \
+  --input_path ./datasets/LooGLE-v2 \
+  --split test \
+  --output_path ./datasets/LooGLE-v2/test_rag_multi.jsonl \
+  --embedding_model THUDM/LongCite-glm4-9b \
+  --generator_model meta-llama/Llama-3.1-8B \
+  --multi_turn --devices 0,1
+```
+
+### 🎯 Running Predictions
+
+#### Option A: vLLM server (`predict.py`)
+
+```bash
+python predict.py \
+  --model your-model-name \
+  --data_dir ./datasets/LooGLE-v2 \
+  --save_dir ./results \
+  --max_new_tokens 512
+```
+
+#### Option B: Transformers local (`predict_transformers.py`)
+
+```bash
+python predict_transformers.py \
+  --model your-model-name \
+  --data_dir ./datasets/LooGLE-v2 \
+  --save_dir ./results \
+  --max_new_tokens 512
+```
+
+Optional prompting flags (both scripts):
+- `--use_cot` for Chain-of-Thought
+- `--use_rag --rag_topk <k> --rag_context <path>` to inject precomputed `context_rag` (default file: `./datasets/LooGLE-v2/test_rag.jsonl`)
+
+<details>
+<summary><b>📝 Core parameters (both options)</b></summary>
+
+| Flag | Purpose |
+|------|---------|
+| `--model` | Must match `config/models.jsonl` name |
+| `--data_dir` | Dataset path (jsonl or HF) |
+| `--save_dir` | Output directory |
+| `--with_context` | 1/0 to include original context |
+| `--n_proc` | Parallel processes |
+| `--max_new_tokens` | Generation length |
+| `--use_cot` | Enable Chain-of-Thought |
+| `--use_rag` | Use retrieved context |
+| `--rag_topk` | How many retrieved chunks to keep |
+| `--rag_context` | Path to `id + context_rag` jsonl |
+
+</details>
+
+<details>
+<summary><b>🖥️ Transformers-only flags</b></summary>
+
+| Flag | Purpose |
+|------|---------|
+| `--device` | Target device (cuda/cpu, auto by default) |
+| `--load_in_8bit` | 8-bit quantization (needs bitsandbytes) |
+| `--load_in_4bit` | 4-bit quantization (needs bitsandbytes) |
+| `--torch_dtype` | Weight dtype: float16/bfloat16/float32 |
+
+> 💡 Install `bitsandbytes` to enable quantization: `pip install bitsandbytes`
+
+</details>
+
+### 📈 Evaluation
+
+After prediction, evaluate the results:
+
+```bash
+python evaluate.py --input_path ./results/your-model-name.jsonl
+```
+
+This outputs per-task accuracy for each domain and overall accuracy.
+
+For batch evaluation (e.g., multiple runs with CoT/RAG or no-context variants):
+
+```bash
+python evaluate.py --input_path ./results --batch --output_json ./results/summary.json
+```
+
+This scans a folder for `.jsonl` files, reports each file’s accuracy, and optionally saves a summary.
+
+---
+
+## 📁 Project Structure
+
+```
+LooGLE-v2/
+├── src/
+│   ├── answer_extractor.py    # Answer extraction logic
+│   ├── evaluator.py           # Evaluation metrics
+│   ├── llm_client.py          # LLM client implementations
+│   ├── data_loader.py         # Data loading utilities
+│   └── utils.py               # Common utilities
+├── config/
+│   └── models.jsonl           # Model configurations
+├── predict.py                  # Prediction script (vLLM server)
+├── predict_transformers.py     # Prediction script (direct transformers)
+├── rag_preprocess.py           # RAG context preprocessing
+├── evaluate.py                 # Evaluation script
+└── requirements.txt            # Dependencies
+```
+
+---
+
+
+## 📄 Results Format
+
+Prediction outputs are saved in JSONL format:
+
+```json
+{
+  "id": "sample_id",
+  "source": "Finance",
+  "task": "Metric Calculation",
+  "type": "question_type",
+  "correct_answer": "123.45",
+  "pred_answer": "123.40",
+  "response": "The correct answer is 123.40",
+  "judge": true
+}
+```
+
+---
+
+## 📖 Citation
+
+If you use LooGLE v2 in your research, please cite:
+
+```bibtex
+@article{he2025loogle,
+  title={LooGLE v2: Are LLMs Ready for Real World Long Dependency Challenges?},
+  author={He, Ziyuan and Wang, Yuxuan and Li, Jiaqi and Liang, Kexin and Zhang, Muhan},
+  journal={arXiv preprint arXiv:2510.22548},
+  year={2025}
+}
+```
+
+---
+
+## 📜 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+---