Sync README and assets with InternScience/SGI-DeepResearch

README.md

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     path: data/test-*
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+
+<div align="center">
+  <h1>Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows</h1>
+</div>
+
+<!-- <p align="center">
+  <a href="https://internscience.github.io/SGI-Page/"><b>🌐Official Site</b></a> ·
+  <a href="https://arxiv.org/pdf/2512.16969"><b>📜arXiv</b></a> ·
+  <a href="https://huggingface.co/collections/InternScience/sgi-bench"><b>🤗Hugging Face</b></a> ·
+  <a href="https://github.com/InternScience/SGI-Bench"><b>💻GitHub</b></a>
+</p> -->
+
+<div align="center">
+
+[![Official Site](https://img.shields.io/badge/Official%20Site-333399.svg?logo=homepage)](https://internscience.github.io/SGI-Page/)&#160;
+<a href="https://arxiv.org/pdf/2512.16969" target="_blank"><img src="https://img.shields.io/badge/arXiv-b5212f.svg?logo=arxiv" height="21px"></a>
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-gray)](https://huggingface.co/collections/InternScience/sgi-bench)&#160;
+[![GitHub](https://img.shields.io/badge/GitHub-000000?logo=github&logoColor=white)](https://github.com/InternScience/SGI-Bench)&#160;
+<!-- [![PDF](https://img.shields.io/badge/📄%20PDF-ff69b4)](https://internscience.github.io/SGI-Page/paper.pdf)&#160; -->
+
+Welcome to the official repository for the SGI-Bench! 👏
+
+</div>
+
+<p align="center">
+  <img src="assets/teaser.png" alt="SGI Overview" width="850">
+</p>
+
+Scientist-aligned benchmark for evaluating Scientific General Intelligence (SGI) across the full inquiry cycle: Deliberation, Conception, Action, and Perception. The benchmark spans 10 disciplines and more than 1,000 expert‑curated samples inspired by Science’s 125 Big Questions, with an agentic evaluation framework and multi‑metric protocol.
+
+---
+
+## 🆕 Latest News
+
+🚩 **Update** (2025-12-22) We release SGI-Bench [paper](https://arxiv.org/pdf/2512.16969) on arXiv.
+
+🚩 **Update** (2025-12-19) SGI-Bench is adapted to [VLMEvalKit](https://github.com/open-compass/VLMEvalKit/pull/1358) and [SciEvalKit](https://github.com/InternScience/SciEvalKit), both of which are highly efficient and comprehensive evaluation toolkits.
+
+🎤 **Talk** (2025-12-18) We are invited to give a talk on *large language model evaluation* at the [AI Insight Talk](https://www.bilibili.com/video/BV16yqdBnE82/?share_source=copy_web&vd_source=7b9d898a8c3bbebf65c411956ed7f8ce) jointly organized by [OpenMMLab](https://openmmlab.com/), [Zhihu](https://www.zhihu.com/), and [ModelScope](https://www.modelscope.cn/).
+
+🚩 **Update** (2025-12-12) We evaluate the newly released `GPT-5.2-Pro` on SGI-Bench.
+
+<details>
+<summary>👉 More News (Click to expand)</summary>
+
+🚩 **Update** (2025-12-10) We update the paper [PDF](https://internscience.github.io/SGI-Page/paper.pdf) on the page.
+
+🚩 **Update** (2025-12-03) We officially release the [data](https://huggingface.co/collections/InternScience/sgi-bench) and [code](https://github.com/InternScience/SGI-Bench) of SGI-Bench.
+</details>
+
+---
+
+## 🔬 What is Scientific General Intelligence (SGI)?
+SGI denotes an AI system that can autonomously navigate the full, iterative cycle of scientific inquiry—Deliberation, Conception, Action, and Perception—with the versatility and proficiency of a human scientist. SGI‑Bench operationalizes this definition via four scientist‑aligned task families: scientific deep research, idea generation, dry/wet experiments, and multimodal experimental reasoning.
+
+---
+
+## 🎯 Framework & Tasks
+
+<p align="center">
+  <img src="assets/pipeline.png" alt="SGI-Bench Pipeline" width="850">
+</p>
+
+- **Deliberation (Scientific Deep Research)**: Multi‑hop retrieval, synthesis, and meta‑analysis style reasoning.
+- **Conception (Idea Generation)**: Structured ideation and multi‑dimensional comparative evaluation.
+- **Action (Dry/Wet Experiment)**: Code generation, lab protocol development and verification.
+- **Perception (Experimental Reasoning)**: Process/observation/simulation/experiment/visualization image reasoning.
+
+Grounded in the Practical Inquiry Model (PIM), SGI‑Bench treats science as an iterative cycle linking deliberation, conception, action and perception. Under this lens, SGI captures the capacity to integrate knowledge retrieval, idea formation, action execution, and interpretation into a unified loop of inquiry.
+
+---
+
+## 📂 Scientist‑Aligned Data Construction
+
+<p align="center">
+  <img src="assets/subjects.png" alt="Scientist-Aligned Data Construction" width="850">
+</p>
+
+- **Raw Corpus**: Expert‑curated texts/images across 10 domains, inspired by Science’s 125 Big Questions.
+- **Question Construction**: 100+ Master's and PhD holders with continuous expert‑in‑the‑loop review.
+- **Data Cleaning**: Rules + model checks + expert QA to ensure executability and unique answers.
+- **Difficulty Filtering**: Removes samples solved by >50% strong LLMs to maintain high challenge.
+
+Result: High‑fidelity, scientist‑aligned tasks that are authentic, challenging, and broadly representative.
+
+---
+
+## 💯 Agentic Evaluation Framework
+
+<p align="center">
+  <img src="assets/evaluation-framework.png" alt="Agentic Evaluation Framework" width="850">
+</p>
+
+- **Four Stages**: Question Selection → Metric Customization → Predict & Eval → Report Generation
+- **Tool Pool**: Web search, PDF parser, Python interpreter, file reader, metric functions
+- **Task Metrics**: EM/SLA; Implementation Similarity; PassAll@k/SER; MCA/RV
+- **Customizable**: Add scientist‑aligned metrics (e.g., rigor, feasibility) on demand
+
+This agent‑based stack formalizes scoring into traceable stages, improves reproducibility, mitigates evaluator–model coupling bias, and yields actionable, scientist‑aligned insights.
+
+---
+
+## 🚀 Test‑Time Reinforcement Learning (TTRL)
+
+<p align="center">
+  <img src="assets/grpo_reward_curves.png" alt="TTRL Training Dynamics" width="850">
+</p>
+
+- **Objective**: Address no‑ground‑truth idea generation by optimizing novelty at test time with online retrieval as a moving baseline.
+- **Reward Design**:  
+  R = R_format + R_novelty  
+  Enforce XML format and strict structure (e.g., &lt;think&gt;, &lt;answer&gt;); reward embedding dissimilarity from retrieved works, gated by thresholds.
+- **Setup**: GRPO on Qwen3‑8B (ms‑swift), G=8, high temperature, bfloat16, online retrieval n=4.
+- **Dynamics**: Format reward saturates quickly; novelty steadily increases. Average novelty improved from 49.36 → 62.06 without labels.
+
+TTRL converts open‑ended ideation into measurable test‑time optimization and extends to multi‑objective rewards (rigor, feasibility, safety, cost).
+
+---
+
+## 🏆 Leaderboard Highlights
+
+| Model                 | Deep Research | Idea Generation | Dry Experiment | Wet Experiment | Experimental Reasoning | SGI-Score |
+| --------------------- | ------------: | --------------: | -------------: | -------------: | ---------------------: | --------: |
+| Gemini-3-Pro 🥇      | **18.48**     | 39.68           | **36.64**      | 32.45          | **41.92**              | **33.83** |
+| Claude-Sonnet-4.5 🥈 | 13.84         | 43.20           | 35.79          | 30.15          | 37.80                  | 32.16     |
+| Qwen3-Max 🥉         | 15.38         | 39.83           | 33.21          | 33.62          | 37.80                  | 31.97     |
+| GPT-4.1               | 11.32         | 36.49           | 34.32          | **36.63**      | 38.49                  | 31.45     |
+| GPT-5.2-Pro           | 15.72	        | 55.03	          | 28.04	         | 17.50	        | 39.18	                 | 31.09     |
+| GPT-5                 | 14.47         | **55.40**       | 29.89          | 16.31          | 38.14                  | 30.84     |
+| o3                    | 12.89         | 46.07           | 31.73          | 30.04          | 32.65                  | 30.68     |
+| Claude-Opus-4.1       | 12.93         | 40.29           | 34.69          | 25.38          | 38.83                  | 30.42     |
+| o4-mini               | 11.95         | 40.78           | 35.79          | 28.86          | 33.33                  | 30.14     |
+| GPT-5.1               | 11.64         | 47.12           | 31.00          | 22.77          | 34.02                  | 29.31     |
+| Grok-4                | 13.31         | 37.12           | 33.71          | 29.01          | 30.24                  | 28.68     |
+| Qwen3-VL-235B-A22B    | 11.97         | 39.28           | 28.41          | 30.30          | 31.62                  | 28.32     |
+| Gemini-2.5-Pro        | 15.09         | 39.95           | 22.51          | 22.05          | 41.24                  | 28.17     |
+| Intern-S1             | 15.74         | 38.09           | 28.79          | 29.02          | 28.87                  | 28.10     |
+| GPT-4o                | 7.86          | 35.95           | 26.94          | 31.31          | 32.30                  | 26.87     |
+| Gemini-2.5-Flash      | 10.69         | 39.13           | 21.03          | 18.55          | 34.36                  | 24.75     |
+| Llama-4-Scout         | 7.86          | 29.72           | 20.37          | 21.66          | 25.77                  | 21.08     |
+| Qwen3-8B              | 8.18          | 35.78           | 18.45          | 9.96           | 23.37                  | 19.15     |
+| Intern-S1-mini        | 11.06         | 36.04           | 16.97          | 12.42          | 16.84                  | 18.67     |
+
+
+---
+
+## 🔥 Quick Start
+
+```bash
+git clone https://github.com/InternScience/SGI-Bench.git
+cd SGI-Bench/evaluation
+
+export OPENAI_API_KEY="xxxxx"
+export OPENAI_BASE_URL="xxxxx"
+
+conda create -n sgi python=3.13.7
+conda activate sgi
+pip install -r requirements.txt
+```
+
+### 📚 Task 1 Deep Research
+
+```bash
+conda activate sgi
+python task_1_deep_research/step_1_get_answer.py gpt-5.2-pro
+python task_1_deep_research/step_2_score.py gpt-5.2-pro
+```
+
+### 💡 Task 2 Idea Generation
+
+1. Install the environment dependencies for evaluating idea generation.
+
+```bash
+conda create -n idea python=3.10.18
+conda activate idea
+pip install -r task_2_idea_generation/idea_generation_requirements.txt
+```
+
+2. Start the evaluation.
+
+```bash
+conda activate idea
+python task_2_idea_generation/step_1_get_answer.py gpt-5.2-pro
+python task_2_idea_generation/step_2_score.py gpt-5.2-pro
+```
+
+### 🖥️ Task 3.1 Dry Experiment (Code Generation)
+
+1. Install the environment dependencies for running the dry experiment code.
+
+```bash
+conda create -n dryexp python=3.10.18
+conda activate dryexp
+pip install -r task_3_dry_experiment/dry_experiment_requirements.txt
+```
+
+2. Create code folder and initialize data (only need to run once).
+
+```bash
+conda activate sgi
+python task_3_dry_experiment/step_1_build.py
+```
+
+> Note: If some scripts time out during execution, please enter the corresponding folder and manually run the script to complete the data initialization.
+
+3. Start the evaluation.
+
+```bash
+conda activate sgi
+python task_3_dry_experiment/step_2_get_answer.py gpt-5.2-pro
+python task_3_dry_experiment/step_3_run_code.py gpt-5.2-pro
+python task_3_dry_experiment/step_4_score.py gpt-5.2-pro
+```
+
+### 🧪 Task 3.2 Wet Experiment (Lab Protocol)
+
+```bash
+conda activate sgi
+python task_3_wet_experiment/step_1_get_answer.py gpt-5.2-pro
+python task_3_wet_experiment/step_2_score.py gpt-5.2-pro
+```
+
+### 📊 Task 4 Experimental Reasoning
+
+```bash
+conda activate sgi
+python task_4_experimental_reasoning/step_1_get_answer.py gpt-5.2-pro
+python task_4_experimental_reasoning/step_2_score.py gpt-5.2-pro
+```
+
+### 💎 SGI-Score
+
+```bash
+conda activate sgi
+python sgi_score.py gpt-5.2-pro
+```
+
+---
+
+## 📬 Contact Us
+
+- 💬 **GitHub Issues**: Please open an issue for bug reports or feature requests
+
+- 📧 **Email**: [xu_wanghan@sjtu.edu.cn](https://black-yt.github.io/)
+
+- 🤝 **Community**: 
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/InternScience/SGI-Bench/main/assets/wechat.jpg" alt="WeChat" width="200">
+</p>
+
+---
+
+## 📜 Citation
+
+If you would like to cite our work, please use the following BibTeX.
+
+```bib
+@article{xu2025probing,
+  title={Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows},
+  author={Xu, Wanghan and Zhou, Yuhao and Zhou, Yifan and Cao, Qinglong and Li, Shuo and Bu, Jia and Liu, Bo and Chen, Yixin and He, Xuming and Zhao, Xiangyu and others},
+  journal={arXiv preprint arXiv:2512.16969},
+  year={2025}
+}
+```
+
+---
+
+## 🌟 Star History
+
+If you find this work helpful, please consider to **star⭐** this [repo](https://github.com/InternScience/SGI-Bench). Thanks for your support! 🤩
+
+[![InternScience/SGI-Bench Stargazers](https://reporoster.com/stars/InternScience/SGI-Bench)](https://github.com/InternScience/SGI-Bench/stargazers)
+
+[![Star History Chart](https://api.star-history.com/svg?repos=InternScience/SGI-Bench,TIGER-AI-Lab/MMLU-Pro,MMMU-Benchmark/MMMU,idavidrein/gpqa,SuperGPQA/SuperGPQA&type=date&legend=top-left)](https://www.star-history.com/#InternScience/SGI-Bench&TIGER-AI-Lab/MMLU-Pro&MMMU-Benchmark/MMMU&idavidrein/gpqa&SuperGPQA/SuperGPQA&type=date&legend=top-left)
+
+<p align="right"><a href="#top">🔝Back to top</a></p>