Instructions to use InternScience/Agents-A1-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InternScience/Agents-A1-Q4_K_M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="InternScience/Agents-A1-Q4_K_M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("InternScience/Agents-A1-Q4_K_M-GGUF", dtype="auto")

llama-cpp-python

How to use InternScience/Agents-A1-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="InternScience/Agents-A1-Q4_K_M-GGUF",
	filename="Agents-A1-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use InternScience/Agents-A1-Q4_K_M-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use InternScience/Agents-A1-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InternScience/Agents-A1-Q4_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InternScience/Agents-A1-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use InternScience/Agents-A1-Q4_K_M-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "InternScience/Agents-A1-Q4_K_M-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InternScience/Agents-A1-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "InternScience/Agents-A1-Q4_K_M-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InternScience/Agents-A1-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use InternScience/Agents-A1-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use InternScience/Agents-A1-Q4_K_M-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for InternScience/Agents-A1-Q4_K_M-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for InternScience/Agents-A1-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for InternScience/Agents-A1-Q4_K_M-GGUF to start chatting

How to use InternScience/Agents-A1-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use InternScience/Agents-A1-Q4_K_M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use InternScience/Agents-A1-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use InternScience/Agents-A1-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull InternScience/Agents-A1-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Agents-A1-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Lonepic commited on about 18 hours ago

Commit

a43bbec

verified ·

1 Parent(s): 976378a

Initial upload

Browse files

Files changed (9) hide show

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figures/github-logo.svg +3 -0
figures/hf-logo.svg +8 -0
figures/logo_nobg.png +3 -0
figures/modelscope-logo.svg +12 -0

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+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+---
+# Agents-A1: Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
+<div style="display: flex; flex-direction: column; align-items: center; line-height: 1.2;">
+  <div style="display: flex; justify-content: center; align-items: center; gap: 10px; height: 30px;">
+    <span style="font-size: 16px;" role="img" aria-label="Homepage">🏠</span>
+    <a href="https://internscience.github.io/Agents-A1/"><b>Homepage</b></a>
+    <span style="color: #ccc;">|</span>
+    <img src="./figures/24px.svg" width="16" height="16" alt="Technical Report" style="filter: invert(0.5);">
+    <a href="https://arxiv.org/abs/2606.30616"><b>Technical Report</b></a>
+  </div>
+  <div style="display: flex; justify-content: center; align-items: center; gap: 10px; height: 30px; margin-top: 2px;">
+    <img src="./figures/hf-logo.svg" width="16" height="16" alt="Hugging Face">
+    <a href="https://huggingface.co/InternScience/Agents-A1"><b>Hugging Face</b></a>
+    <span style="color: #ccc;">|</span>
+    <img src="./figures/github-logo.svg" width="16" height="16" alt="GitHub">
+    <a href="https://github.com/InternScience/Agents-A1"><b>Github</b></a>
+    <span style="color: #ccc;">|</span>
+    <img src="./figures/modelscope-logo.svg" width="16" height="16" alt="Model Scope">
+    <a href="https://modelscope.cn/models/InternScience/Agents-A1"><b>ModelScope</b></a>
+  </div>
+</div>
+> [!Note]
+> This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format.
+>
+> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc.
+**Agents‑A1** is a 35B Mixture‑of‑Experts agentic model from [InternScience](https://huggingface.co/InternScience), built to scale heterogeneous agentic abilities across multiple domains including **Long‑horizon Search, Engineering, Scientific Research, Instruction Following, and Tool-calling**. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities.
+From the scaling of long-horizon trajectories, **Agents‑A1** is trained with the assistance of a domain-grounded knowledge-action infrastructure that jointly constructs actions, observations, and verifier outcomes, turning the agent's process into a trainable target. From the scaling of heterogeneous agent abilities, **Agents‑A1** presents a three-stage training paradigm for building scalable general-purpose agentic model. First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors. Second, we train domain-level teacher models to capture specialized expertise in each domain. Third, we propose multi-teacher multi-domain on-policy distillation with heterogeneity-aware optimization to improve knowledge transfer efficiency across different domains.
+![Agents-A1 Benchmark Overview](./figures/a1_benchmarks_altair_grid.svg)
+## Highlights
+- **Agentic Reasoning**: Agents-A1 excels at decomposing complex tasks into executable sub-steps, planning ahead, and adapting its strategy based on intermediate results.
+- **Tool Use**: Natively supports function calling and tool integration, enabling seamless interaction with APIs, code interpreters, search engines, and other external tools.
+- **Scientific and Professional Reasoning**: Handles tool-integrated scientific reasoning and professional knowledge question answering.
+- **Instruction Following**: Precisely follows detailed, multi-constraint instructions across diverse domains.
+We welcome developers and enterprises to integrate and try Agents-A1 and share their feedback.
+## Performance
+We evaluate Agents-A1 in real-world agentic and research-oriented workflows across six directions — long-horizon search, engineering tasks, scientific research, instruction following, general agentic tasks, and scientific agentic tasks. Despite operating in the ~35B model class, Agents-A1 delivers highly competitive performance against frontier-scale systems such as GPT-5.5, DeepSeek-V4-pro, and Kimi-K2.6. It achieves overall SOTA results on several challenging benchmarks, including Seal-0 (56.4), HiPhO (46.4), FrontierScience-Olympiad (79.0), FrontierScience-Research (40.00), IFBench (80.6), and IFEval (94.8), while also ranking as the best among comparable models on a broad range of tasks such as BrowseComp (75.5), XBench-DS-2510 (86.0), GAIA (96.0), SciCode (44.3), HLE with tools (47.6), and MolBench-bind (56.8). These results show that Agents-A1 combines strong long-horizon search ability, robust scientific reasoning, and reliable instruction following, establishing it as a highly capable and efficient agentic model that narrows the gap with much larger frontier models.
+<p>
+🥇 Overall SOTA &nbsp;&nbsp;
+🟢 Best Among Comparable Models (~35B)
+</p>
+<table>
+<thead>
+<tr>
+<th rowspan="2" align="left">Benchmark</th>
+<th colspan="3" align="center" style="text-align:center;">
+    📏 Comparable Models (~35B)
+</th>
+<th colspan="4" align="center" style="text-align:center;">
+    🚀 Larger-scale Models
+</th>
+<th colspan="2" align="center" style="text-align:center;">
+    ⭐ Ours
+</th>
+</tr>
+<tr>
+<th align="center">Qwen3.5-35B-A3B</th>
+<th align="center">Qwen3.6-35B-A3B</th>
+<th align="center">Nex-N2-mini</th>
+<th align="center">Step-3.5-Flash</th>
+<th align="center">Kimi-K2.6</th>
+<th align="center">DeepSeek-V4-pro(Max)</th>
+<th align="center">GPT-5.5(xhigh)</th>
+<th align="center">Agents-A1</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+    <td colspan="9" align="left"><b>🔍 Long-horizon Search</b></td>
+</tr>
+<tr>
+<td align="left">BrowseComp</td>
+<td align="center">61.0</td>
+<td align="center">67.93</td>
+<td align="center">74.1</td>
+<td align="center">69.0</td>
+<td align="center">83.2</td>
+<td align="center">83.4</td>
+<td align="center">🥇 84.4</td>
+<td align="center">🟢 75.51</td>
+</tr>
+<tr>
+<td align="left">XBench-DS-2510</td>
+<td align="center">77.0</td>
+<td align="center">71.0</td>
+<td align="center">82.0</td>
+<td align="center">56.3</td>
+<td align="center">🥇 90.0</td>
+<td align="center">🥇 90.0</td>
+<td align="center">84.0</td>
+<td align="center">🟢 86.0</td>
+</tr>
+<tr>
+<td align="left">Seal0</td>
+<td align="center">41.4</td>
+<td align="center">38.74</td>
+<td align="center">49.55</td>
+<td align="center">36.94</td>
+<td align="center">50.45</td>
+<td align="center">54.95</td>
+<td align="center">42.34</td>
+<td align="center">🥇 56.36</td>
+</tr>
+<tr>
+<td align="left">GAIA</td>
+<td align="center">59.8</td>
+<td align="center">78.64</td>
+<td align="center">82.52</td>
+<td align="center">84.5</td>
+<td align="center">80.58</td>
+<td align="center">🥇 98.06</td>
+<td align="center">87.38</td>
+<td align="center">🟢 96.04</td>
+</tr>
+<tr>
+    <td colspan="9" align="left"><b>⚙️ Engineering Tasks</b></td>
+</tr>
+<tr>
+<td align="left">SciCode</td>
+<td align="center">37.7</td>
+<td align="center">35.8</td>
+<td align="center">29.9</td>
+<td align="center">40.4</td>
+<td align="center">53.5</td>
+<td align="center">50.0</td>
+<td align="center">🥇 56.1</td>
+<td align="center">🟢 44.33</td>
+</tr>
+<tr>
+<td align="left">MLE-Lite</td>
+<td align="center">24.24</td>
+<td align="center">34.85</td>
+<td align="center">34.85</td>
+<td align="center">54.55</td>
+<td align="center">62.12</td>
+<td align="center">63.64</td>
+<td align="center">🥇 72.73</td>
+<td align="center">🟢 43.94</td>
+</tr>
+<tr>
+    <td colspan="9" align="left"><b>🧪 Scientific Research</b></td>
+</tr>
+<tr>
+<td align="left">HLE w/ tools</td>
+<td align="center">47.4</td>
+<td align="center">36.2</td>
+<td align="center">32.0</td>
+<td align="center">23.1</td>
+<td align="center">🥇 54.0</td>
+<td align="center">48.2</td>
+<td align="center">52.2</td>
+<td align="center">🟢 47.6</td>
+</tr>
+<tr>
+<td align="left">HiPhO</td>
+<td align="center">37.0</td>
+<td align="center">37.7</td>
+<td align="center">38.5</td>
+<td align="center">38.3</td>
+<td align="center">41.1</td>
+<td align="center">38.7</td>
+<td align="center">43.3</td>
+<td align="center">🥇 46.4</td>
+</tr>
+<tr>
+<td align="left">FrontierScience-Olympiad</td>
+<td align="center">64.5</td>
+<td align="center">60.3</td>
+<td align="center">52.0</td>
+<td align="center">61.0</td>
+<td align="center">73.0</td>
+<td align="center">76.0</td>
+<td align="center">78.0</td>
+<td align="center">🥇 79.0</td>
+</tr>
+<tr>
+<td align="left">FrontierScience-Research</td>
+<td align="center">2.5</td>
+<td align="center">2.9</td>
+<td align="center">5.0</td>
+<td align="center">6.7</td>
+<td align="center">17.9</td>
+<td align="center">13.3</td>
+<td align="center">26.7</td>
+<td align="center">🥇 40.0</td>
+</tr>
+<tr>
+    <td colspan="9" align="left"><b>📋 Instruction Following</b></td>
+</tr>
+<tr>
+<td align="left">IFBench</td>
+<td align="center">70.2</td>
+<td align="center">64.4</td>
+<td align="center">54.08</td>
+<td align="center">64.6</td>
+<td align="center">71.77</td>
+<td align="center">73.47</td>
+<td align="center">75.9</td>
+<td align="center">🥇 80.61</td>
+</tr>
+<tr>
+<td align="left">LongBench-v2</td>
+<td align="center">59.0</td>
+<td align="center">57.7</td>
+<td align="center">59.6</td>
+<td align="center">57.5</td>
+<td align="center">62.0</td>
+<td align="center">🥇 64.3</td>
+<td align="center">-</td>
+<td align="center">🟢 60.2</td>
+</tr>
+<tr>
+<td align="left">IFEval</td>
+<td align="center">91.9</td>
+<td align="center">91.3</td>
+<td align="center">88.4</td>
+<td align="center">93.53</td>
+<td align="center">94.45</td>
+<td align="center">93.35</td>
+<td align="center">93.35</td>
+<td align="center">🥇 94.82</td>
+</tr>
+<tr>
+    <td colspan="9" align="left"><b>🤖 General Agentic Tasks</b></td>
+</tr>
+<tr>
+<td align="left">τ<sup>2</sup>-Bench</td>
+<td align="center">🟢 81.2</td>
+<td align="center">79.0</td>
+<td align="center">74.53</td>
+<td align="center">75.77</td>
+<td align="center">81.93</td>
+<td align="center">🥇 82.2</td>
+<td align="center">81.63</td>
+<td align="center">79.81</td>
+</tr>
+<tr>
+<td align="left">VitaBench</td>
+<td align="center">31.9</td>
+<td align="center">35.6</td>
+<td align="center">23.0</td>
+<td align="center">30.0</td>
+<td align="center">35.63</td>
+<td align="center">🥇 49.04</td>
+<td align="center">45.0</td>
+<td align="center">🟢 38.75</td>
+</tr>
+<tr>
+    <td colspan="9" align="left"><b>🔬 Scientific Agentic Tasks</b></td>
+</tr>
+<tr>
+<td align="left">MatTools</td>
+<td align="center">21.0</td>
+<td align="center">15.9</td>
+<td align="center">34.1</td>
+<td align="center">44.93</td>
+<td align="center">63.8</td>
+<td align="center">47.1</td>
+<td align="center">🥇 68.8</td>
+<td align="center">🟢 47.1</td>
+</tr>
+<tr>
+<td align="left">MolBench-bind</td>
+<td align="center">46.0</td>
+<td align="center">48.7</td>
+<td align="center">51.4</td>
+<td align="center">45.95</td>
+<td align="center">21.6</td>
+<td align="center">37.8</td>
+<td align="center">🥇 62.2</td>
+<td align="center">🟢 56.8</td>
+</tr>
+</tbody>
+</table>
+## Usage
+### SGLang
+[SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models.
+Install SGLang with uv:
+```shell
+uv venv --python 3.12 --seed --managed-python
+source .venv/bin/activate
+uv pip install sglang
+```
+See [its documentation](https://docs.sglang.ai/get_started/install.html) for more details.
+The following commands create API endpoints at `http://localhost:8000/v1`:
+- **Standard Version** (1 GPUs, 262K context):
+  ```shell
+  python -m sglang.launch_server \
+    --model-path InternScience/Agents-A1 \
+    --port 8000 \
+    --tp-size 1 \
+    --mem-fraction-static 0.8 \
+    --context-length 262144 \
+    --reasoning-parser qwen3
+  ```
+- **Tool Use**:
+  ```shell
+  python -m sglang.launch_server \
+    --model-path InternScience/Agents-A1 \
+    --port 8000 \
+    --tp-size 1 \
+    --mem-fraction-static 0.8 \
+    --context-length 262144 \
+    --reasoning-parser qwen3 \
+    --tool-call-parser qwen3_coder
+  ```
+### vLLM
+[vLLM](https://github.com/vllm-project/vllm) is a high-throughput and memory-efficient inference and serving engine for LLMs.
+Install vLLM from the main branch via uv:
+```shell
+uv venv --python 3.12 --seed --managed-python
+source .venv/bin/activate
+uv pip install vllm --torch-backend=auto
+```
+See [its documentation](https://docs.vllm.ai/en/stable/getting_started/installation/index.html) for more details.
+The following commands create API endpoints at `http://localhost:8000/v1`:
+- **Standard Version** (1 GPUs, 262K context):
+  ```shell
+  vllm serve InternScience/Agents-A1 \
+    --port 8000 \
+    --tensor-parallel-size 1 \
+    --max-model-len 262144 \
+    --reasoning-parser qwen3
+  ```
+- **Tool Call**:
+  ```shell
+  vllm serve InternScience/Agents-A1 \
+    --port 8000 \
+    --tensor-parallel-size 1 \
+    --max-model-len 262144 \
+    --reasoning-parser qwen3 \
+    --enable-auto-tool-choice \
+    --tool-call-parser qwen3_coder
+  ```
+- **Text-Only** (skips vision encoder to free KV cache memory):
+  ```shell
+  vllm serve InternScience/Agents-A1 \
+    --port 8000 \
+    --tensor-parallel-size 1 \
+    --max-model-len 262144 \
+    --reasoning-parser qwen3 \
+    --language-model-only
+  ```
+### Recommended Sampling Parameters
+For the best generation quality, we recommend the following sampling parameters:
+- `temperature`: 0.85
+- `top_p`: 0.95
+- `top_k`: 20
+- `min_p`: 0.0
+- `presence_penalty`: 1.1
+- `repetition_penalty`: 1.0
+## Agent Capability Evaluation
+To provide the community with a unified agent evaluation codebase for fair comparison, we have also open-sourced an evaluation framework for assessing agentic models across core capabilities, including tool use and multi-step reasoning. The evaluation code is included in the [Agents-A1/evaluation](https://github.com/InternScience/Agents-A1/tree/main/evaluation) of this repository.
+We use this framework to evaluate the released model under a standardized and reproducible setting.
+Specifically, the model is tested on a set of agent-oriented tasks that require it to understand user goals, decompose complex instructions, interact with tools or environments when necessary, and produce final results. The evaluation results reported in [Model Card](https://huggingface.co/InternScience/Agents-A1) are generated using the open-source framework above, so that users can reproduce the experiments, compare other models under the same protocol, and further extend the benchmark for new agent scenarios. (**Note that:** To ensure a fair comparison, we report the benchmark results from their original technical reports. If a model does not report the corresponding benchmark results, we evaluate it using the same evaluation protocol as our model.)
+For detailed evaluation scripts, task definitions, metrics, and reproduction instructions, please refer to the evaluation codebase.
+## Citation
+If you find our work helpful, feel free to give us a cite.
+```
+```

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