docs: update document structure. Add PR content (#171)

* Add main

* update docs

* rename and reorder

* update documents structure

* remove useless

* Update docker

docs/research/benchmark.rst

@@ -97,3 +97,22 @@ A png file will be saved to the designated path as shown below.
 
 .. image:: ../_static/benchmark.png
 
+
+Related Paper
+-------------
+
+- `Towards Data-Centric Automatic R&D <https://arxiv.org/abs/2404.11276>`_:
+  We have developed a comprehensive benchmark called RD2Bench to assess data and model R&D capabilities. This benchmark includes a series of tasks that outline the features or structures of models. These tasks are used to evaluate the ability of LLM-Agents to implement them.
+
+.. code-block:: bibtex
+
+    @misc{chen2024datacentric,
+        title={Towards Data-Centric Automatic R&D},
+        author={Haotian Chen and Xinjie Shen and Zeqi Ye and Wenjun Feng and Haoxue Wang and Xiao Yang and Xu Yang and Weiqing Liu and Jiang Bian},
+        year={2024},
+        eprint={2404.11276},
+        archivePrefix={arXiv},
+        primaryClass={cs.AI}
+    }
+
+.. image:: https://github.com/user-attachments/assets/494f55d3-de9e-4e73-ba3d-a787e8f9e841

docs/research/catalog.rst

@@ -0,0 +1,34 @@
+===========
+Research
+===========
+
+To achieve the good effects and improve R&D capabilities, we face multiple challenges, the most important of which is the continuous evolution capability. Existing large language models (LLMs) find it difficult to continue growing their capabilities after training is completed. Moreover, the training process of LLMs focuses more on general knowledge, and the lack of depth in more specialized knowledge becomes an obstacle to solving professional R&D problems within the industry. This specialized knowledge needs to be learned and acquired from in-depth industry practice.
+
+
+Our RD-Agent, on the other hand, can continuously acquire in-depth domain knowledge through deep exploration during the R&D phase, allowing its R&D capabilities to keep growing.
+
+To address these key challenges and achieve industrial value, a series of research work needs to be completed.
+
+
+.. list-table:: Research Areas and Descriptions
+   :header-rows: 1
+
+   * - Research Area
+     - Description
+   * - :doc:`Benchmark <benchmark>`
+     - Benchmark the R&D abilities
+   * - Research
+     - Idea proposal: Explore new ideas or refine existing ones
+   * - :doc:`Development <dev>`
+     - Ability to realize ideas: Implement and execute ideas
+
+
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Doctree:
+   :hidden:
+
+   benchmark
+   dev

docs/research/dev.rst

@@ -0,0 +1,25 @@
+==============================
+Development
+==============================
+
+
+Related Paper
+-------------
+
+- `Collaborative Evolving Strategy for Automatic Data-Centric Development <https://arxiv.org/abs/2407.18690>`_
+  Co-STEER is a method to tackle data-centric development (AD2) tasks and highlight its main challenges, which need expert-like implementation (i.e., learning domain knowledge from practice) and task scheduling capability (e.g., starting with easier tasks for better overall efficiency), areas that previous work has largely overlooked. Our Co-STEER agent enhances its domain knowledge through our evolving strategy and improves both its scheduling and implementation skills by gathering and using domain-specific practical experience. With a better schedule, implementation becomes faster. At the same time, as implementation feedback becomes more detailed, scheduling accuracy improves. These two capabilities grow together through practical feedback, enabling a collaborative evolution process.
+
+.. code-block:: bibtex
+
+    @misc{yang2024collaborative,
+        title={Collaborative Evolving Strategy for Automatic Data-Centric Development},
+        author={Xu Yang and Haotian Chen and Wenjun Feng and Haoxue Wang and Zeqi Ye and Xinjie Shen and Xiao Yang and Shizhao Sun and Weiqing Liu and Jiang Bian},
+        year={2024},
+        eprint={2407.18690},
+        archivePrefix={arXiv},
+        primaryClass={cs.AI}
+    }
+
+.. image:: https://github.com/user-attachments/assets/75d9769b-0edd-4caf-9d45-57d1e577054b
+   :alt: Collaborative Evolving Strategy for Automatic Data-Centric Development
+

docs/research/research.rst

@@ -1,15 +0,0 @@
-===========
-Research
-===========
-
-
-
-.. TODO: xiao will add this
-
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Doctree:
-   :hidden:
-
-   benchmark.rst