RUC-NLPIR · ignorejjj · Jun 28, 2026 · Jun 28, 2026 · Jun 28, 2026 · Jun 28, 2026
diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml
@@ -35,6 +35,14 @@ jobs:
           pip install "mkdocs-material>=9.5" "mkdocs-static-i18n>=1.2"
           mkdocs build --strict -d _site/docs
 
+      # ── Live demo: arbor replay --demo --html -> _site/demo.html ─────
+      # Regenerated from source on every deploy so the hosted, zero-install
+      # demo always matches the shipped code (no committed artifact to go stale).
+      - name: Build live demo page
+        run: |
+          pip install .
+          arbor replay --demo --html --no-open --out _site/demo.html
+
       # ── Project page: Vite/React -> _site (root) ─────────────────────
       - uses: actions/setup-node@v4
         with:

diff --git a/README.md b/README.md
@@ -3,13 +3,15 @@
 </p>
 
 
-# Toward Generalist Autonomous Research via Hypothesis-Tree Refinement
+<h1 align="center">🌳 Arbor</h1>
 
+<h3 align="center">The autonomous research agent that beats Claude Code and Codex by <b>2.5×</b> on the same compute budget</h3>
 
 <p align="center">
   <a href="https://arxiv.org/pdf/2606.11926"><img src="https://img.shields.io/badge/Paper-arXiv-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper"></a>
   <a href="https://github.com/RUC-NLPIR/Arbor"><img src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"></a>
   <a href="https://RUC-NLPIR.github.io/Arbor/"><img src="https://img.shields.io/badge/Project_Page-Live-0E9B9B?style=for-the-badge&logo=githubpages&logoColor=white" alt="Project Page"></a>
+  <a href="https://RUC-NLPIR.github.io/Arbor/demo.html"><img src="https://img.shields.io/badge/Live_Demo-Watch-FF5C8A?style=for-the-badge&logo=githubpages&logoColor=white" alt="Live Demo"></a>
   <a href="https://RUC-NLPIR.github.io/Arbor/docs/"><img src="https://img.shields.io/badge/Docs-Material-526CFE?style=for-the-badge&logo=materialformkdocs&logoColor=white" alt="Docs"></a>
   <a href="https://github.com/RUC-NLPIR/Arbor/discussions"><img src="https://img.shields.io/badge/Discussions-Join-5865F2?style=for-the-badge&logo=github&logoColor=white" alt="Discussions"></a>
   <a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache_2.0-D22128?style=for-the-badge&logo=apache&logoColor=white" alt="License: Apache 2.0"></a>
@@ -19,18 +21,29 @@
   <b>English</b> | <a href="README.zh-CN.md">简体中文</a>
 </p>
 
-**Arbor is an autonomous research agent that turns a long-horizon objective into a
-cumulative search.** Give it a benchmark and a goal; it proposes hypotheses, edits
-code, runs real experiments, learns from the results, and keeps the improvements that
-hold up on held-out data. Instead of one-shot attempts that forget what failed, Arbor
-grows a **hypothesis tree**: every idea becomes a branch — pruned if it fails,
-harvested if it works — and insights propagate back so later ideas start smarter.
+<p align="center">
+  <i>Give Arbor a benchmark and a goal. It proposes hypotheses, edits code, runs real
+  experiments, and keeps only the gains that survive held-out data — growing a
+  <b>hypothesis tree</b> instead of forgetting what failed.</i>
+</p>
+
+> **▶️ Try it in 30 seconds — no API key, no config:**
+>
+> ```bash
+> pip install arbor-agent && arbor replay --demo   # watch the hypothesis tree grow live
+> ```
+>
+> Or **watch it right now in your browser** — nothing to install: **[▶️ Live Demo](https://RUC-NLPIR.github.io/Arbor/demo.html)**.
+
+### 🏆 One controller, six tasks — wins the held-out test on all of them
+
+| Task | Metric | Claude Code | Codex | **Arbor** |
+| --- | --- | :---: | :---: | :---: |
+| BrowseComp | acc ↑ | 53.33 | 50.00 | **67.67** |
+| Terminal-Bench 2.0 | pass ↑ | 71.70 | 73.59 | **77.36** |
+| Math-Reasoning Data | gap ↑ | 8.33 | 6.25 | **20.83** |
 
-For more details, visit our [project page](https://RUC-NLPIR.github.io/Arbor/)
-and read the [paper](https://arxiv.org/pdf/2606.11926). For a more detailed usage manual,
-see our [documentation](https://RUC-NLPIR.github.io/Arbor/docs/). 🧭 You can also
-choose the [CLI or Skill version](#-cli-and-skill-versions) depending on your
-environment and workflow.
+Plus **86.36% Any-Medal on MLE-Bench Lite** (GPT-5.5). → [See all six tasks](#-results) · [project page](https://RUC-NLPIR.github.io/Arbor/) · [paper](https://arxiv.org/pdf/2606.11926) · [docs](https://RUC-NLPIR.github.io/Arbor/docs/)
 
 ## 🎬 Demo
 
@@ -39,10 +52,10 @@ https://github.com/user-attachments/assets/49c1a306-d2e9-49d6-9c83-65e38a62df30
 
 ## 📣 News
 
-- **2026-06** — **Built-in literature search & idea novelty checks.** Arbor can now ground its research in prior work via the public [alphaXiv](https://www.alphaxiv.org) API — zero config, no search endpoint or key. Novelty-check any idea before you build it with `arbor idea-check "<your idea>"`, or let the Coordinator vet every new branch automatically. See [Literature Search & Novelty Checks](#-literature-search--novelty-checks). 🔎
-- **2026-06** — Arbor was featured by [VentureBeat](https://venturebeat.com/), one of the leading tech media outlets in the US: ["New AI optimization framework beats Claude Code and Codex by 2.5x on the same compute budget"](https://venturebeat.com/orchestration/new-ai-optimization-framework-beats-claude-code-and-codex-by-2-5x-on-the-same-compute-budget). 📰
-- **2026-06** — Arbor's native CLI runtime and Agent Skill Suite (Codex / Claude Code) are released. 🚀
-- **2026-06** — The Arbor paper is released on [arXiv](https://arxiv.org/abs/2606.11926). 🎉
+- **2026-06-22** — **Built-in literature search & idea novelty checks.** Arbor can now ground its research in prior work via the public [alphaXiv](https://www.alphaxiv.org) API — zero config, no search endpoint or key. Novelty-check any idea before you build it with `arbor idea-check "<your idea>"`, or let the Coordinator vet every new branch automatically. See [Literature Search & Novelty Checks](#-literature-search--novelty-checks). 🔎
+- **2026-06-18** — Arbor was featured by [VentureBeat](https://venturebeat.com/), one of the leading tech media outlets in the US: ["New AI optimization framework beats Claude Code and Codex by 2.5x on the same compute budget"](https://venturebeat.com/orchestration/new-ai-optimization-framework-beats-claude-code-and-codex-by-2-5x-on-the-same-compute-budget). 📰
+- **2026-06-12** — Arbor's native CLI runtime and Agent Skill Suite (Codex / Claude Code) are released. 🚀
+- **2026-06-11** — The Arbor paper is released on [arXiv](https://arxiv.org/abs/2606.11926). 🎉
 
 ## 💡 Why Arbor
 

diff --git a/README.zh-CN.md b/README.zh-CN.md
@@ -3,13 +3,15 @@
 </p>
 
 
-# 基于假设树的面向通用自主科研方法（Toward Generalist Autonomous Research via Hypothesis-Tree Refinement）
+<h1 align="center">🌳 Arbor</h1>
 
+<h3 align="center">在相同算力预算下，效果超越 Claude Code 与 Codex <b>2.5×</b> 的自主科研智能体</h3>
 
 <p align="center">
   <a href="https://arxiv.org/pdf/2606.11926"><img src="https://img.shields.io/badge/Paper-arXiv-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper"></a>
   <a href="https://github.com/RUC-NLPIR/Arbor"><img src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"></a>
   <a href="https://RUC-NLPIR.github.io/Arbor/"><img src="https://img.shields.io/badge/Project_Page-Live-0E9B9B?style=for-the-badge&logo=githubpages&logoColor=white" alt="Project Page"></a>
+  <a href="https://RUC-NLPIR.github.io/Arbor/demo.html"><img src="https://img.shields.io/badge/Live_Demo-Watch-FF5C8A?style=for-the-badge&logo=githubpages&logoColor=white" alt="Live Demo"></a>
   <a href="https://RUC-NLPIR.github.io/Arbor/docs/"><img src="https://img.shields.io/badge/Docs-Material-526CFE?style=for-the-badge&logo=materialformkdocs&logoColor=white" alt="Docs"></a>
   <a href="https://github.com/RUC-NLPIR/Arbor/discussions"><img src="https://img.shields.io/badge/讨论区-加入-5865F2?style=for-the-badge&logo=github&logoColor=white" alt="Discussions"></a>
   <a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache_2.0-D22128?style=for-the-badge&logo=apache&logoColor=white" alt="License: Apache 2.0"></a>
@@ -19,20 +21,35 @@
   <a href="README.md">English</a> | <b>简体中文</b>
 </p>
 
-**Arbor 是一个自主科研智能体，可以把长周期目标转化为持续累积的搜索过程。** 给它一个基准
-（benchmark）和一个目标，它会提出假设、修改代码、运行真实实验、从结果中学习，并保留那些在
-留出（held-out）数据上经得起验证的改进。不同于“一次性尝试、过后即弃”的做法，Arbor 会逐步生长出一棵
-**假设树**：每个想法都是一根分支，失败则剪枝，成功则保留；洞见会沿树反向传播，让后续想法
-从更可靠的起点出发。
+<p align="center">
+  <i>给 Arbor 一个基准和一个目标。它会提出假设、修改代码、运行真实实验，只保留经得起留出
+  数据验证的改进——生长出一棵<b>假设树</b>，而不是过后即弃、忘记失败的教训。</i>
+</p>
+
+> **▶️ 30 秒上手——无需 API key，无需配置：**
+>
+> ```bash
+> pip install arbor-agent && arbor replay --demo   # 实时观看假设树的生长过程
+> ```
+>
+> 或者**现在就在浏览器里看**——无需安装任何东西：**[▶️ 在线 Demo](https://RUC-NLPIR.github.io/Arbor/demo.html)**。
+
+### 🏆 一个控制器，六项任务——全部赢下留出测试集
+
+| 任务 | 指标 | Claude Code | Codex | **Arbor** |
+| --- | --- | :---: | :---: | :---: |
+| BrowseComp | acc ↑ | 53.33 | 50.00 | **67.67** |
+| Terminal-Bench 2.0 | pass ↑ | 71.70 | 73.59 | **77.36** |
+| Math-Reasoning Data | gap ↑ | 8.33 | 6.25 | **20.83** |
 
-更多详情，请访问我们的[项目主页](https://ruc-nlpir.github.io/Arbor/)并阅读[论文](https://arxiv.org/pdf/2606.11926)。如需详细的使用说明，请参阅[文档](https://ruc-nlpir.github.io/Arbor/docs/)。🧭 你也可以根据自己的环境和工作流选择使用 [CLI 版本或技能套件版本](https://claude.ai/chat/e7121091-ce2c-4970-a60f-16b54c453729#-cli-与技能套件版本)。
+外加 **MLE-Bench Lite 上 86.36% Any-Medal**（GPT-5.5）。→ [查看全部六项任务](#-实验结果) · [项目主页](https://ruc-nlpir.github.io/Arbor/) · [论文](https://arxiv.org/pdf/2606.11926) · [文档](https://ruc-nlpir.github.io/Arbor/docs/)
 
 ## 📣 最新动态
 
-- **2026-06** — **内置文献检索与想法新颖性审查。** Arbor 现在可以通过 [alphaXiv](https://www.alphaxiv.org) 公共 API 把研究建立在已有工作之上——零配置，无需搜索端点或密钥。动手前用 `arbor idea-check "<你的想法>"` 审查任意想法的新颖性，或让 Coordinator 自动为每个新分支把关。详见[文献检索与新颖性审查](#-文献检索与新颖性审查)。🔎
-- **2026-06** — Arbor 被美国知名科技媒体 [VentureBeat](https://venturebeat.com/) 报道：[《New AI optimization framework beats Claude Code and Codex by 2.5x on the same compute budget》](https://venturebeat.com/orchestration/new-ai-optimization-framework-beats-claude-code-and-codex-by-2-5x-on-the-same-compute-budget)。📰
-- **2026-06** — Arbor 原生 CLI 运行时与智能体技能套件（Codex / Claude Code）正式发布。🚀
-- **2026-06** — Arbor 论文在 [arXiv](https://arxiv.org/abs/2606.11926) 发布。🎉
+- **2026-06-22** — **内置文献检索与想法新颖性审查。** Arbor 现在可以通过 [alphaXiv](https://www.alphaxiv.org) 公共 API 把研究建立在已有工作之上——零配置，无需搜索端点或密钥。动手前用 `arbor idea-check "<你的想法>"` 审查任意想法的新颖性，或让 Coordinator 自动为每个新分支把关。详见[文献检索与新颖性审查](#-文献检索与新颖性审查)。🔎
+- **2026-06-18** — Arbor 被美国知名科技媒体 [VentureBeat](https://venturebeat.com/) 报道：[《New AI optimization framework beats Claude Code and Codex by 2.5x on the same compute budget》](https://venturebeat.com/orchestration/new-ai-optimization-framework-beats-claude-code-and-codex-by-2-5x-on-the-same-compute-budget)。📰
+- **2026-06-12** — Arbor 原生 CLI 运行时与智能体技能套件（Codex / Claude Code）正式发布。🚀
+- **2026-06-11** — Arbor 论文在 [arXiv](https://arxiv.org/abs/2606.11926) 发布。🎉
 
 ## 💡 为什么选择 Arbor
 

diff --git a/docs/roadmap.md b/docs/roadmap.md
@@ -164,6 +164,52 @@ sources behind each idea) and a per-run cost breakdown.
 
 ---
 
+## Direction 4 — Adoption, DX & community
+
+The first three directions grow what Arbor *does*. This one lowers the barrier
+between a curious visitor and a first successful run, and makes the project's
+momentum visible. The goal is a shorter path to the "aha" moment, not more
+surface area.
+
+### 4.1 Live, zero-install demo
+
+`arbor replay --demo --html` already emits a self-contained, dependency-free page
+of a real run. Publish that page (e.g. on GitHub Pages alongside the docs) and
+link it from the top of the README, so a visitor can watch the hypothesis tree
+grow **without installing anything**. One recorded run, refreshed when the
+dashboard changes.
+
+### 4.2 Examples gallery
+
+Today `examples/algotune_knn` is the only end-to-end task a newcomer can run.
+Grow this into a small gallery so different audiences can self-identify, each
+with a copy-pasteable command and a short asciinema recording:
+
+- a Kaggle / MLE-style task (pairs with the `mle_kaggle` plugin),
+- a prompt / harness-engineering task, and
+- a small training / fine-tuning task.
+
+Keep the bar at "runs in minutes on a laptop or a free key", reusing the
+zero-config discipline of `algotune_knn`.
+
+### 4.3 Regular releases and a changelog
+
+Versions are already derived from `v*` tags via setuptools-scm, so the cost of a
+release is low. Cut a release on a predictable cadence with human-readable notes,
+and keep a `CHANGELOG.md` (or GitHub Releases as the source of truth) so the
+project's progress is legible from the outside.
+
+### 4.4 Public roadmap and issue board
+
+Surface this document as a public board (GitHub Projects) and tag tracked work
+with `good first issue` / `help wanted`. Several threads are already
+contribution-shaped — growing the [benchmark zoo](#21-benchmark-zoo-organized-by-domain-format-tooling-shipped-collection-growing)
+to 3–5 packs ([2.1](#21-benchmark-zoo-organized-by-domain-format-tooling-shipped-collection-growing))
+and adding domain plugins ([2.2](#22-plugin-gallery)) — so opening them up turns
+readers into contributors.
+
+---
+
 Have an idea or want to own one of these threads? Open a
 [discussion](https://github.com/RUC-NLPIR/Arbor/discussions) or see
 [Contributing](contributing.md).
diff --git a/docs/roadmap.zh.md b/docs/roadmap.zh.md
@@ -70,7 +70,7 @@
 
 ## 方向二 —— 外部资源
 
-### 2.1 按 domain 划分的 benchmark zoo 🚧 *（格式与工具已完成；集合扩充中）*
+### 2.1 按 domain 划分的 benchmark zoo 🚧 *（格式与工具已完成；集合扩充中）* {#sec-2-1}
 
 一个经过筛选、统一格式的任务集合，按领域分组（如 CV、NLP、时序、优化），每个任务用一篇已
 发表论文的结果作为要超越的 baseline。它以 `arbor-zoo/` 放在仓库里，每个基准一个文件夹，
@@ -106,7 +106,7 @@
   复用 `plugin` 词汇（`eval_contract` / `protected_paths`），应能几乎无返工地导出（与 2.2
   配套）。
 
-### 2.2 插件库
+### 2.2 插件库 {#sec-2-2}
 
 在 `mle_kaggle` 之外提供更多范例领域插件，与上面的 Task Pack 配对，让把 Arbor 重定向到
 一个领域只需改一行 `plugin:`。
@@ -138,5 +138,44 @@
 
 ---
 
+## 方向四 —— 采用、开发者体验与社区
+
+前三个方向扩展 Arbor *能做什么*；这个方向降低"好奇的访客"到"第一次成功运行"
+之间的门槛，并让项目的推进势头可见。目标是缩短到达"aha 时刻"的路径，而不是堆叠
+更多功能面。
+
+### 4.1 免安装的在线 Demo
+
+`arbor replay --demo --html` 已经能导出一个自包含、零依赖的真实 run 页面。把它
+发布出去（例如随文档一起部署到 GitHub Pages），并从 README 顶部链接过去，让访客
+**无需安装任何东西**就能看假设树生长。一份录制好的 run，随仪表盘变化刷新即可。
+
+### 4.2 示例画廊
+
+目前 `examples/algotune_knn` 是新手唯一能端到端跑通的任务。把它扩成一个小型画廊，
+让不同受众都能对号入座，每个都配可复制的命令和一段 asciinema 录屏：
+
+- 一个 Kaggle / MLE 风格的任务（与 `mle_kaggle` 插件配套），
+- 一个 prompt / harness 工程任务，以及
+- 一个小规模训练 / 微调任务。
+
+门槛保持在"笔记本上几分钟、或用免费 key 即可跑通"，复用 `algotune_knn` 的零配置
+纪律。
+
+### 4.3 规律发布与更新日志
+
+版本号已经通过 setuptools-scm 从 `v*` tag 自动派生，发布成本很低。以可预期的节奏
+发布版本并附上人类可读的发布说明，并维护一份 `CHANGELOG.md`（或以 GitHub Releases
+为准），让项目进展从外部看也一目了然。
+
+### 4.4 公开路线图与 issue 看板
+
+把本文档以公开看板（GitHub Projects）的形式呈现，并给跟踪中的工作打上
+`good first issue` / `help wanted` 标签。有几条线索天然适合外部贡献——把
+[benchmark zoo](#sec-2-1) 扩到 3–5 个 pack（[2.1](#sec-2-1)）、增加领域插件
+（[2.2](#sec-2-2)）——把它们开放出来，就能把读者变成贡献者。
+
+---
+
 有想法，或想认领其中一条线索？开一个
 [discussion](https://github.com/RUC-NLPIR/Arbor/discussions)，或见 [贡献](contributing.md)。