Code, environment and data for "ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows"
- 2025-06-08: ScienceBoard will be presented at WCUA@ICML 2025 as an oral paper. π
- 2025-06-04: We release the virtual machine snapshot of ScienceBoard.
- 2025-05-27: Initial release of our paper, environment, benchmark, and project page. Check it out! π
The infrastructure of the framework is based on OSWorld together with VMware Workstation Pro (which is free for personal use since May, 2024) in Ubuntu or Windows. Please make sure that your device meets the minimal requirements of these preliminaries.
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Download VMware Workstation Pro 17 and our pre-made images from Hugging Face;
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Clone this repository and install packages needed:
git clone https://github.com/OS-Copilot/ScienceBoard cd ScienceBoard conda create -n sci python=3.11 conda activate sci pip install -r requirements.txt
-
We recommend you to change evaluating process in
main.py
directly with some sensitive information hidden in environment variables, especially when it comes to complicate configs concerningcommunity
.
Note
Community
specifies the form of cooperation in which one or more models completes the tasks. You can customize your own multi-agents system by creating a new class inheriting Community
together with the method of __call__()
.
-
Used in our template of
main.py
:VM_PATH
: path to vmware .vmx file; will be automatically extracted (repeatedly) if set to path ofVM.zip
HTTPX_PROXY
: proxy URL if needed; avoid clashes withHTTP_PROXY
andHTTPS_PROXY
on Linux;OPENAI_API_KEY
: API key for OpenAI GPT;GOOGLE_API_KEY
: API key for Google Gemini;ANTHROPIC_API_KEY
: API key for Anthropic Claude;
and variables for open-source models:
Model Base URL Name QwenVL QWEN_VL_URL
QWEN_VL_NAME
InternVL INTERN_VL_URL
INTERN_VL_NAME
QVQ QVQ_VL_URL
QVQ_VL_NAME
OS-Atlas OS_ACT_URL
OS_ACT_NAME
UI-Tars TARS_DPO_URL
TARS_DPO_NAME
-
Used in
sci/Presets.py
:LEAN_LIB_PATH
: path for Lean 4 REPL;QT6_LIB_PATH
: dynamic library directory for Qt6;FFI_LIB_PATH
: dynamic library file for libffi.so;KALG_BIN_PATH
: executable binary file of KAlgebra;CELE_BIN_PATH
: executable binary file of Celestia;GIS_BIN_PATH
: executable binary file of Grass GIS.
Caution
Configs defined in sci/Presets.py
are only used for debugging under Raw
settings and would not be loaded unless being used.
DEBUG_ERR_FACT
: insert a breakpoint when eval exception occur if set to any value;
Automata
: a simple encapsulation forModel
andAgent
model_style
: affect the request format and response processing of model calling; you can customize your own style by adding_request_{style}()
underModel
;overflow_style
: affect the way we detect overflow of token; you can customize your own style by adding{style}()
underOverflow
;code_style
: affect the way we process code blocks when communicating with models; you can customize your own style by addingwrap_{style}()
andextract_{style}()
underCodeLike
.
Tester
:__init__()
only register a new config. use__call__()
for actual evaluation after init.tasks_path
: the directory or file path for json file(s) of task(s); all*.json
files under the path specified will be recursively loaded when a directory path is provided;logs_path
: the directory path for log files and will be created automatically when not existed; the structure of the directory will be arranged according to that undertasks_path
;community
: the way of cooperation among multiple agents; useAllInOne
for standard setting inherited from OSWorld;ignore
: skipped when log indicates that the task is finished (by checking the existence ofresult.out
) if set toTrue
; so you can re-run the same program to retry failure cases only;debug
: finish the tasks manually instead of calling models;relative
: allow VM to executepyautogui
codes with relative coordinates; basically used by InternVL-3.
-
Error when initializing:
Traceback (most recent call last): File "/usr/lib/python3.11/site-packages/requests/models.py", line 971, in json return complexjson.loads(self.text, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/json/__init__.py", line 346, in loads return _default_decoder.decode(s) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/json/decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
the target app has not yet been started up when trying to initialize due to insufficient performance of your device; try to assign a bigger value for 'wait' field in json files of the tasks.
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Failed to get accessibility tree:
Traceback (most recent call last): File "os-sci/sci/Tester.py", line 396, in __call__ counter._pass() if task_info() else counter._fail() ^^^^^^^^^^^ File "os-sci/sci/Tester.py", line 174, in __call__ return self.task() ^^^^^^^^^^^ File "os-sci/sci/base/task.py", line 175, in _avail_wrapper return method(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "os-sci/sci/base/task.py", line 393, in __call__ return self.__call() ^^^^^^^^^^^^^ File "os-sci/sci/base/task.py", line 381, in __call stop_type, stop_args = self.predict() ^^^^^^^^^^^^^^ File "os-sci/sci/base/task.py", line 175, in _avail_wrapper return method(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "os-sci/sci/base/log.py", line 462, in record_wrapper return_value = method(self) ^^^^^^^^^^^^ File "os-sci/sci/base/task.py", line 316, in predict invalid = self._step(step_index) ^^^^^^^^^^^^^^^^^^^^^^ File "os-sci/sci/base/task.py", line 260, in _step observation = { ^ File "os-sci/sci/base/task.py", line 261, in <dictcomp> obs_type: getattr(self.manager, obs_type)() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "os-sci/sci/vm/vmanager.py", line 152, in _env_wrapper return method(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "os-sci/sci/base/manager.py", line 85, in _assert_wrapper result = method(self) ^^^^^^^^^^^^ File "os-sci/sci/vm/vmanager.py", line 278, in a11y_tree a11y_tree = utils.linearize(raw_a11y_tree) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "os-sci/sci/vm/utils.py", line 206, in linearize filtered_nodes = filter_nodes(ET.fromstring(a11y_tree), platform) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/xml/etree/ElementTree.py", line 1338, in XML parser.feed(text) TypeError: a bytes-like object is required, not 'NoneType'
fail to authenticate in newly downloaded images; input password once manually in VMWare and take a new snapshot using the same name as before and delete the former one.
If you are willing to add new applications into ScienceBoard, please make sure that you already have a full understanding of your target application and this repository.
- Write a plugin or implant a server in your target application revealing its inner states. Listed below is repositories of our modified applications:
- chimerax-states: a plugin for ChimeraX;
- kalgebra: a recompilation copy of KAlgebra;
- Celestia: a recompilation copy of Celestia;
- grass-gis: GUI part in Python of Grass GIS.
- Copy
sci/Template
and rename it to the name of your target application;template.py
: direct managers for the Raw / VM application;task.py
: initialization and evaluation of tasks using RESTful API with your server;__init__.py
: change the name oftemplate.py
.
- Write json files of tasks and modified VM images.
We have collected some questions from emails, Hugging Face, and WeChat communications. Please check the FAQ π€
If you are interested in our work or find this repository / our data helpful, please consider using the following citation format when referencing our paper:
@article{sun2025scienceboard,
title={ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows},
author={Sun, Qiushi and Liu, Zhoumianze and Ma, Chang and Ding, Zichen and Xu, Fangzhi and Yin, Zhangyue and Zhao, Haiteng and Wu, Zhenyu and Cheng, Kanzhi and Liu, Zhaoyang and others},
journal={arXiv preprint arXiv:2505.19897},
year={2025}
}