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docs: update search-agent promotion copy #1443

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docs: update search-agent promotion copy
you-n-g Jul 25, 2026
3e3b6ad
docs: highlight XYZ AI Lab search-agent stack
you-n-g Jul 25, 2026
06a502b
docs: add XYZ AI Lab branding to promo copy
you-n-g Jul 25, 2026
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17 changes: 17 additions & 0 deletions README.md
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Expand Up @@ -31,6 +31,7 @@
# 📰 News
| 🗞️ News | 📝 Description |
| -- | ------ |
| <img src="https://xyz-lab.ai/assets/favicon.svg" alt="XYZ AI Lab" width="14"> Search Agent Post-Training | Together with [XYZ AI Lab](https://xyz-lab.ai/), we applied the R&D-Agent method to the search-agent setting, covering the full pipeline end to end. The open-sourced building blocks are :octocat: [axrl](https://github.com/XYZ-AI-Lab/axrl) for infra and :octocat: [AxisAgentic](https://github.com/XYZ-AI-Lab/AxisAgentic) for the harness. |
| ICML 2026 Acceptance | We are thrilled to announce that our paper [FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents](https://arxiv.org/abs/2603.01712) has been accepted to ICML 2026. The FT-Agent implementation is available in the [LLM fine-tuning guide](rdagent/app/finetune/llm/README.md). |
| ACL 2026 Findings Acceptance | We are thrilled to announce that our paper [Reasoning as Gradient](https://arxiv.org/abs/2603.01692) has been accepted to ACL 2026 Findings. Execution traces are available at [Gome GPT-5 Traces](https://huggingface.co/datasets/amstrongzyf/Gome-GPT5-Traces) |
| Web UI Release | We release a new frontend that can be built and served by `rdagent server_ui` for real-time interaction and trace viewing, currently excluding the `data_science` scenario. |
Expand Down Expand Up @@ -71,6 +72,22 @@ You can inspect the detailed runs of the above results online.

For running R&D-Agent on MLE-bench, refer to **[MLE-bench Guide: Running ML Engineering via MLE-bench](https://rdagent.readthedocs.io/en/latest/scens/data_science.html)**

# 🔎 Search Agent Post-Training

<p align="center">
<a href="https://xyz-lab.ai/">
<img src="https://xyz-lab.ai/assets/logo-sources/derivatives/logo-derived-014-transparent-bg-black-text-xyz-ai-lab-wordmark.svg" alt="XYZ AI Lab" width="260">
</a>
</p>

Together with [XYZ AI Lab](https://xyz-lab.ai/), we applied the R&D-Agent method to the search-agent setting and extended it to cover the full post-training pipeline end to end.
The stack is now open through two complementary projects:

- :octocat: [axrl](https://github.com/XYZ-AI-Lab/axrl) for the infra layer
- :octocat: [AxisAgentic](https://github.com/XYZ-AI-Lab/AxisAgentic) for the harness layer

Together, they bring RD-Agent into search-agent post-training with a more complete path from experimentation to evaluation.

# 🥇 The First Data-Centric Quant Multi-Agent Framework!

R&D-Agent for Quantitative Finance, in short **RD-Agent(Q)**, is the first data-centric, multi-agent framework designed to automate the full-stack research and development of quantitative strategies via coordinated factor-model co-optimization.
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