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Includes idea generation, literature review, research pipeline skills, and arxiv tool.","maintainers":[{"name":"ivan_thu","email":"contact@tsingyuai.com"}],"readme":"<h1 align=\"center\">Scientify</h1>\n<p align=\"center\">\n  <em>持续新陈代谢的 AI 科研系统</em>\n</p>\n\n<p align=\"center\">\n  <a href=\"https://www.npmjs.com/package/scientify\"><img src=\"https://img.shields.io/npm/v/scientify?style=for-the-badge&logo=npm&logoColor=white\" alt=\"npm version\"></a>\n  <a href=\"https://github.com/tsingyuai/scientify\"><img src=\"https://img.shields.io/github/stars/tsingyuai/scientify?style=for-the-badge&logo=github\" alt=\"GitHub stars\"></a>\n  <a href=\"LICENSE\"><img src=\"https://img.shields.io/badge/License-MIT-blue.svg?style=for-the-badge\" alt=\"MIT License\"></a>\n  <a href=\"https://github.com/openclaw/openclaw\"><img src=\"https://img.shields.io/badge/OpenClaw-plugin-00FF9F?style=for-the-badge\" alt=\"OpenClaw plugin\"></a>\n</p>\n\n<p align=\"center\">\n  <a href=\"https://scientify.tech\">官网</a> · <a href=\"./README.en.md\">English</a> · <a href=\"https://github.com/tsingyuai/scientify/issues\">Issues</a>\n</p>\n\n---\n\n## 它能做什么\n\n> [!IMPORTANT]\n> Scientify 不是一个\"问一次答一次\"的 AI 工具。它像一个真正的研究伙伴——**持续思考、持续积累、持续交付**。\n\n### 1. 新陈代谢：持续思考，而非一次性回答\n\n现有 AI 科研工具的工作方式是**批处理**——给个问题，跑一遍 pipeline，输出报告，结束。下次再问同一个方向，从零开始。跑 10 次和跑 1 次没有本质区别。\n\n但人类研究者不是这样工作的。你每天在读、在跑、在想。昨天的失败改变了今天的阅读，上周的对话改变了这周的实验设计。\n\nScientify 采用**新陈代谢模式**——持续地摄入、消化、沉淀、再摄入：\n\n- **持续摄入**：每天自动跟进前沿论文，不需要你手动触发\n- **消化沉淀**：将新知识与已有积累关联，写入持久化知识库\n- **假设进化**：淘汰无效假设，进化有效路径，每一轮失败都是下一轮的养料\n- **主动交付**：发现值得关注的进展后自动验证，验证通过主动推送给你\n\n用得越久，它研究越深入。\n\n<p align=\"center\">\n  <img src=\"docs/assets/showcase/3.png\" width=\"50%\" alt=\"Scientify 通过飞书主动推送研究进展\">\n  <br>\n  <sub>Scientify 通过飞书主动向研究者推送最新发现，并结合知识库产生思考</sub>\n</p>\n\n#### 它有多大优势？我们做了一项受控研究\n\n新陈代谢模式不只是一个工程选择——它带来的是质的不同。我们在 **50 个研究主题、892 条生成假设** 上对照测试了新陈代谢与传统批处理范式，论文：[arXiv:2604.12243](https://arxiv.org/abs/2604.12243)。\n\n<p align=\"center\">\n  <img src=\"docs/assets/showcase/metabolism-vs-batch.zh.svg\" width=\"100%\" alt=\"新陈代谢 vs 批处理：50 主题、892 条假设的对照实验关键指标\">\n</p>\n\n| 指标 | 批处理基线 | 新陈代谢 | 差异 |\n|------|-----------|---------|------|\n| 命中率 — 假设方向被后续论文验证的比例 | 3.0% | **5.8%** | **1.9×（接近翻倍）** |\n| 单主题有效假设数 | 13.7 | **17.3** | **+26%** |\n| LLM 评判的新颖性（1–10 分制） | 6.39 | **6.82** | **+0.43** |\n| 单条假设的 Token 成本 | 434K | **30K** | **−92%** |\n\n> 📄 详见 [Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature](https://arxiv.org/abs/2604.12243)\n\n### 2. 端到端自主研究：做到 SOTA 级成果\n\n给它一个课题，它自己把研究做完，跑出性能超越外部文献水平的新算法。\n\n多 Agent 迭代驱动：编排器持有假设和全部积累，只调度不写代码；每轮 spawn 独立子 agent 执行实现、审查、实验；每一轮失败都沉淀为下一轮的经验，假设越修正越精确，直到发现更优的方法。\n\n### Showcase：自主发现 KV2 算法并达到领域领先性能\n\n> **目标**：针对长上下文 LLM 推理，设计一种策略，同时降低首 token 时延和单请求通信量。\n>\n> Scientify 自主完成文献调研、假设生成、代码实现与消融实验验证，提出 **KV2 算法**，相较于现有研究，TTFT p95和bytes/request均有不同程度降低，性能达到 SOTA 水平。\n\n<p align=\"center\">\n  <img src=\"docs/assets/showcase/1.png\" width=\"80%\" alt=\"KV2 算法实验结果\">\n  <br>\n  <sub>Scientify 独立产出的学术论文，报道KV2的设计思路与结果</sub>\n</p>\n\n<p align=\"center\">\n  <img src=\"docs/assets/showcase/2.png\" width=\"80%\" alt=\"KV2 与现有方法对比\">\n  <br>\n  <sub>KV2 与现有方法的 SOTA 对比</sub>\n</p>\n\n---\n\n## 架构\n\n```\n┌─────────────────────────────────────────────────────────────┐\n│  研究者                                                      │\n│  对话 · 投喂材料 · 判断假设                                    │\n└──────────────┬──────────────────────────────┬───────────────┘\n               ↓                              ↓\n┌──────────────────────────┐   ┌──────────────────────────────┐\n│  Agent 层                 │   │  知识库（持久化）               │\n│                          │   │                              │\n│  Heartbeat  每天定时唤醒  │←→│  _index.md                   │\n│  Reflection 自主跨领域探索│   │  topic-*.md                  │\n│  Pipeline   假设验证执行  │   │  hypotheses/                 │\n│                          │   │  experiments/                │\n└──────────┬───────────────┘   │  conversations/              │\n           ↓                   │                              │\n┌──────────────────────────┐   │  Markdown 文件 · Git 管理     │\n│  工具层                   │   │  完全可审计 · 你也能编辑       │\n│                          │──→│                              │\n│  arxiv_search            │   └──────────────────────────────┘\n│  openalex_search         │\n│  github_search           │\n│  paper_browser           │\n│  code_executor           │\n└──────────────────────────┘\n```\n\n四个部分，各司其职：\n\n### 研究者\n\n你是系统的一部分。通过对话注入判断、投喂材料、确认或否决假设。你的参与让新陈代谢的方向更准确，让研究假设更精确。\n\n### Agent 层\n\n三个循环驱动新陈代谢：\n\n| Agent | 做什么 | 触发方式 |\n|-------|--------|---------|\n| **Heartbeat** | 每天跟进前沿论文，发现关联后自主验证，验证通过主动推送给你 | 定时自动唤醒 |\n| **Reflection** | 跨领域探索，将不同主题的知识关联起来，发现意想不到的联系 | Heartbeat 触发 / 研究者触发 |\n| **Pipeline** | 端到端研究执行——文献调研 → 深度分析 → 实现 → 审查 → 实验 | 研究者触发 / Reflection 触发 |\n\nPipeline 内部是多 Agent 迭代：编排器持有假设，spawn 子 agent 执行实现（`implement`）、审查（`review`）、实验（`experiment`）。每轮失败沉淀为经验，假设越修正越精确。\n\n### 工具层\n\nAgent 的手和眼：\n\n| 工具 | 能力 |\n|------|------|\n| `arxiv_search` / `openalex_search` | 搜索学术论文（arXiv + 跨学科） |\n| `github_search` | 搜索开源代码实现 |\n| `paper_browser` | 分页精读论文，避免上下文溢出 |\n| `code_executor` | 在 `uv` 隔离环境中执行实验代码 |\n\n> Scientify 运行在 [OpenClaw](https://github.com/openclaw/openclaw) 之上，天然可调用平台的 MCP 服务器（Slack / 飞书推送）、浏览器自动化（付费文献下载）、多会话并发（多方向并行研究）等能力。\n\n### 知识库\n\n所有积累持久化为 Markdown 文件，Git 管理，每一行变化都可追溯。你和 Agent 读写的是同一组文件：\n\n```\nknowledge_state/\n├── _index.md              # 研究全局索引\n├── topic-*.md             # 按主题组织的知识沉淀\n├── hypotheses/            # 假设演化记录\n├── experiments/           # 实验结果与分析\n├── paper_notes/           # 逐篇论文深读记录\n└── logs/                  # 每轮新陈代谢的运行日志\n```\n\n---\n\n## 环境要求\n\n- **Node.js** >= 18\n- **Python 3** + **uv**（用于 ML 代码执行）\n- **git**\n\n---\n\n## 安装 OpenClaw\n\n```bash\n# 全局安装 OpenClaw\npnpm add -g openclaw    # 或: npm install -g openclaw\n\n# 运行引导向导（配置模型提供商、API Key、工作空间）\nopenclaw onboard\n\n# 启动 Gateway（WebUI 服务器）\nopenclaw gateway\n```\n\n启动后，WebUI 地址为 **http://127.0.0.1:18789/**（默认端口）。\n\n> **代理用户注意：** 如果你设置了 `http_proxy`，访问 WebUI 时需加 `--noproxy 127.0.0.1`，或在浏览器中配置代理例外。\n\n---\n\n## 安装 Scientify\n\n```bash\nopenclaw plugins install scientify\n```\n\n插件安装到 `~/.openclaw/extensions/`，自动启用。\n\n### 从源码安装（开发用）\n\n```bash\ngit clone https://github.com/tsingyuai/scientify.git\ncd scientify && pnpm install && pnpm build\n\n# 链接为开发插件\nopenclaw plugins install -l ./\n```\n\n### 验证安装\n\n```bash\nopenclaw plugins list\n# 应显示: Scientify (loaded)\n```\n\n安装后需 **重启 Gateway** 以加载插件：\n\n```bash\n# 停止运行中的 Gateway（Ctrl+C），然后：\nopenclaw gateway\n```\n\n---\n\n## 通过 WebUI 使用\n\n### 1. 打开 WebUI\n\n浏览器访问 **http://127.0.0.1:18789/**。\n\n### 2. 开始研究任务\n\n在聊天框中输入研究提示，Scientify 的 skill 会被 LLM 自动匹配：\n\n```\n研究 \"transformer efficiency\"，分析论文并生成创新想法\n```\n\n或者用斜杠命令直接调用特定 skill：\n\n```\n/research-pipeline\n/research-collect\n/idea-generation\n/algorithm-selection\n/dataset-validate\n```\n\n## 机器学习中段任务的新增技能\n\n- `/algorithm-selection`\n  - 用在 `/research-survey` 之后、`/research-plan` 之前\n  - 作用：把 2-3 条候选路线写清楚，明确 `Chosen Route / Rejected Routes / Fallback Route`\n- `/dataset-validate`\n  - 用在 `plan_res.md` 已经存在、准备实现或审查模型之前\n  - 作用：单独审数据真实性、split、label、leakage 和 mock 风险，把数据质量和模型质量分开\n- `/baseline-runner`\n  - 用在 `plan_res.md` 已经存在、需要真实 baseline 对比时\n  - 作用：统一 baseline、协议、指标和结果记录，产出 `baseline_res.md`\n\n### 3. 监控子 agent 进度\n\n编排器 spawn 子 agent 后，你会看到：\n- **启动通知** — \"Phase 1: Literature Survey 已启动\"\n- **完成通知** — 子 agent 完成后自动发送消息\n- **进度推进** — 编排器验证产出后自动进入下一阶段\n\n随时查看状态：\n\n```\n/research-status\n```\n\n### 4. 管理项目\n\n```\n/projects              # 列出所有项目\n/project-switch <id>   # 切换项目\n/papers                # 列出已下载论文\n/ideas                 # 列出已生成想法\n```\n\n## 中后段项目的快捷技能\n\n如果项目已经有一部分产物，不必总是从 `/metabolism` 或 `/research-survey` 重新开始。可以直接进入这些 skill：\n\n- `/write-paper`\n  - 适合：已经有 `experiment_res.md`、结果图或结果表，准备整理成 paper draft\n- `/artifact-review`\n  - 适合：已有 draft、README 更新或准备对外分享的 figures，想先做发布前审查\n- `/figure-standardize`\n  - 适合：图已经有了，但文件名、caption、单位、标签风格不统一\n- `/release-layout`\n  - 适合：项目已有成果，想把 README 或 release 入口改得更清楚、更适合外部阅读\n\n---\n\n## 已知限制\n\n- **子 agent 超时**：每个子 agent 超时 30 分钟（`runTimeoutSeconds: 1800`）。复杂文献调研可能需要更长时间。\n- **GPU/Sandbox**：代码默认在宿主机直接执行。OpenClaw sandbox 暂不支持 GPU 透传。\n- **模型依赖**：研究质量与使用的 LLM 模型强相关。推荐 Claude Opus 4.5+ 或 GPT-5+。\n\n---\n\n## 开发\n\n```bash\ngit clone https://github.com/user/scientify.git\ncd scientify\npnpm install\npnpm build          # 构建 TypeScript\npnpm dev            # 监听模式\n\n# 链接到 OpenClaw 测试\nopenclaw plugins install -l ./\n```\n\n参见 [CLAUDE.md](./CLAUDE.md) 了解版本更新流程和贡献指南。\n\n---\n\n## 内测报名\n\nScientify 目前处于内测阶段，面向有真实研究需求的个人和团队开放。\n\n报名后，我们会为你提供：\n\n1. 详细的使用指导，帮助你快速上手\n2. 评估你的研究领域，分析 Scientify 执行端到端研究的可行性\n3. 根据你的研究特点推荐最适合的使用方法\n4. 根据你的需求快速开发新功能\n\n<p align=\"center\">\n  <a href=\"https://tsingyuai.feishu.cn/share/base/form/shrcne78pTl0NJ9gQqVPDvWm7Wb\">\n    <img src=\"docs/assets/showcase/entry.png\" width=\"200\" alt=\"扫码报名内测\">\n  </a>\n  <br>\n  <sub><a href=\"https://tsingyuai.feishu.cn/share/base/form/shrcne78pTl0NJ9gQqVPDvWm7Wb\">扫描二维码，或者点我立即报名内测</a></sub>\n</p>\n\n---\n\n## License\n\nMIT\n\n## Author\n\ntsingyuai\n","readmeFilename":"README.md"}