{"id":51293604,"url":"https://github.com/imbue-bit/imbue-bit","last_synced_at":"2026-06-30T12:33:50.078Z","repository":{"id":312257685,"uuid":"1046843880","full_name":"imbue-bit/imbue-bit","owner":"imbue-bit","description":"🌺 GitHub 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align=\"center\"\u003e\n\n# ✦ ɪᴍʙᴜᴇ ✦\n\n*When the world stopped, I started compiling.*\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://readme-typing-svg.demolab.com?font=Fira+Code\u0026pause=1000\u0026color=3b82f6\u0026center=true\u0026vCenter=true\u0026width=500\u0026lines=Co-founder+@+Chunjiang+Intelligence;AI+Researcher;Quant+Developer;Taiwanese+Hipster\" alt=\"Typing SVG\" /\u003e\n\u003c/div\u003e\n\n\u003ca href=\"https://x.com/imbue_byte\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Twitter-1DA1F2?style=for-the-badge\u0026logo=x\u0026logoColor=white\" alt=\"X\"\u003e\n\u003c/a\u003e\u003cbr/\u003e\n\u003ca href=\"https://space.bilibili.com/3706923325065226\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Bilibili-FF8EB3?style=for-the-badge\u0026logo=bilibili\u0026logoColor=white\" alt=\"Bilibili\"\u003e\n\u003c/a\u003e\n\u003cbr/\u003e\n\u003ca href=\"mailto:hi@chunjiang.dev\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Email-hi%40chunjiang.dev-D14836?style=for-the-badge\u0026logo=gmail\u0026logoColor=white\" alt=\"Email\"\u003e\n\u003c/a\u003e\u003cbr/\u003e\n\u003ca href=\"https://www.zhihu.com/people/ysrlxfu\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Zhihu-0084FF?style=for-the-badge\u0026logo=zhihu\u0026logoColor=white\" alt=\"Zhihu\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://t.me/zhiran233\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Telegram-26A6E1?style=for-the-badge\u0026logo=telegram\u0026logoColor=white\" alt=\"Telegram\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://music.163.com/#/user/home?id=7826076470\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Netease_Music-@千早千叶-FF0000?style=for-the-badge\u0026logo=netease-cloud-music\u0026logoColor=white\" alt=\"Music\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://www.xiaohongshu.com/user/profile/6a12626e000000000103f800\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Xiaohongshu-FF2442?style=for-the-badge\u0026logo=xiaohongshu\u0026logoColor=white\" alt=\"Xiaohongshu\"\u003e\n\u003c/a\u003e\n\u003ca href=\"javascript:void(0);\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/QQ-3816206539-EB1923?style=for-the-badge\u0026logo=tencent-qq\u0026logoColor=white\" alt=\"QQ\"\u003e\n\u003c/a\u003e\n\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n\u003cbr\u003e\n\n\n\u003e *他会拿起一块蓝色玻璃，透过它看花园，花园里的沙地和路径会变成一种灰烬般的颜色，天空则变得异常深邃，仿佛热带的天空。*  \n\u003e — 《说吧，记忆》，弗拉基米尔·纳博科夫\n\n\u003cbr/\u003e\n\n## 🔬 Recent Research \u0026 Publications\nMy research focuses on bridging the gap between human intuition and machine scale, with a strong emphasis on Deep Learning Theory, LLM Reasoning, Long-Context Processing, and Quantitative Finance.\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"https://paper.chunjiang.dev/\" target=\"_blank\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/View_Full_Paper_Archive-3b82f6?style=for-the-badge\u0026logo=read-the-docs\u0026logoColor=white\" alt=\"Chunjiang Intelligence Paper Archive\"\u003e\n    \u003c/a\u003e\n\u003c/div\u003e\n\n\u003cbr/\u003e\n\n### 📂 Research by Domain\n\n\u003cdetails open\u003e\n\u003csummary\u003e\u003cb\u003e📐 Deep Learning Theory \u0026 Scaling Laws\u003c/b\u003e\u003c/summary\u003e\n\u003cbr/\u003e\n\n*   **[Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum](https://arxiv.org/abs/2605.20196)**\n    *   *Core Contribution:* Real-data scaling laws are driven by progressively covering a latent predictive state spectrum rather than just token frequencies. By representing corpora as suffix-automata and using a global-KL spectrum, effective truncation ranks reliably predict excess loss across training sizes ($R^2 \\approx 0.96$).\n*   **[A Formal Kinetic Theory for Zeroth-Order Newton Dynamics](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* Develops a kinetic framework for Z-O Newton methods, providing a Stein-corrected Hessian estimator and exposing the curvature-variance trade-off.\n*   **[Reconstructing High-Resolution Hyperparameter Loss Landscapes](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* Frames hyperparameter tuning as a landscape reconstruction problem, using active surrogate modeling to find robust, generalizable minima.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e🧠 LLM Reasoning \u0026 Alignment\u003c/b\u003e\u003c/summary\u003e\n\u003cbr/\u003e\n\n*   **[Inverting the Search Dynamics: LLMs as Semantic Leaders in MCTS](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* Proposes *Leader-Follower MCTS*, where the LLM steers search with macro-actions, improving performance on GSM8K, MATH, and HumanEval.\n*   **[The Statistical Illusion of Rejection Sampling in LLMs](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* Bridges the gap between heuristic truncation in LLM sampling and true mathematical alignment, revealing statistical biases.\n*   **[Expected Value Alignment for Generative Reward Modeling](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* Introduces *EVA*, a reward modeling paradigm for theorem proving that extracts continuous scores from discrete token distributions.\n*   **[Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context Decoding](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* Presents *Soft-NBCE*, which replaces hard chunk selection with soft fusion, improving reasoning while maintaining memory efficiency.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e📈 Quantitative Finance \u0026 Applied Systems\u003c/b\u003e\u003c/summary\u003e\n\u003cbr/\u003e\n\n*   **[Deep Learning under Continuous Distribution Shift for Quant Finance](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* Formulates a non-stationary NTK and spectral tracking SDE to model DL performance under persistent market distribution shifts.\n*   **[AdaPrecise: A Task-Agnostic Dynamic Precision Routing Framework](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* A Gumbel-Softmax based framework for dynamic precision routing that optimizes model efficiency for inference on edge devices.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e🚗 Vision-Language-Action (VLA) \u0026 Embodied AI\u003c/b\u003e\u003c/summary\u003e\n\u003cbr/\u003e\n\n*   **[Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Driving](https://paper.chunjiang.dev/)**\n    *   *Core Contribution:* A sparse, energy-based framework for autonomous driving that uses VLMs for class-agnostic perception and Lagrangian action minimization.\n\n\u003c/details\u003e\n\n\u003cbr\u003e\n\u003cbr\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003e🌍 Beyond the Papers: My Multiverse of Engineering \u0026 Finance\u003c/b\u003e\u003c/summary\u003e\n\u003cbr\u003e\n\n*   📈 **Quantitative Finance:** I actively manage ~5 Million CNY in quantitative funds, integrating modern CS and deep learning into strategies to generate alpha.\n*   ⚙️ **Systems \u0026 DevOps:** An advocate for Clean Code \u0026 TDD. I've achieved **C10K** via kernel tuning/IO multiplexing and ran CPU-based IDC services with a 1:20 overselling ratio. I'm proficient in K8s (Helm, Prometheus, Grafana, ELK), and have improved resource utilization by 17% through HPA and Limit/Request tuning. \n*   🌐 **Frontend \u0026 UX:** With a deep focus on aesthetics and HCI, I leverage React, Vue, and Ionic to transform complex backend logic into elegant user experiences. An excellent system needs both robustness in algorithms and poetry in its UI.\n*   🛡️ **CTF \u0026 CP:** I'm active in XCTF (Crypto \u0026 Web) with contributions in problem-setting and write-ups, alongside a brief but intense stint in Competitive Programming.\n\n\u003c/details\u003e\n\u003cbr\u003e\n\n## 🤖 Open Source \u0026 Trained Models\n\n*   **[Socrates-nano](https://github.com/imbue-bit)**: Open-sourced the complete LLM codebase including pre-training, data synthesis pipelines, post-training, and test-time scaling.\n*   **[Socrates-embedding](https://huggingface.co/Chunjiang-Intelligence/Socrates-embedding)**: A next-gen embedding model that outperforms an 83× larger parameter counterpart, achieving SOTA accuracy under identical budgets.\n*   **[RWKV-7-Prover-1.5B](https://huggingface.co/imbue2025/RWKV-7-Prover-1.5b)**: A formal math model leveraging RWKV-7 \u0026 Condor-inspired data synthesis for high-precision Lean 4 auto-formalization.\n*   **[LPR-Oracle](https://huggingface.co/imbue2025/LPR-Oracle)**: A forecasting model for China’s Loan Prime Rate (LPR) in financial markets.\n*   **[Thales](https://huggingface.co/Chunjiang-Intelligence/Thales)**: Thales is an interpretable, physics-informed deep learning surrogate model for ultra-fast, arbitrage-free option pricing and AI-decoded risk reporting.\n\n\u003cbr\u003e\n\n## 📊 GitHub Analytics\n\n\u003cdiv align=\"center\"\u003e\n\n\u003ca href=\"https://github.com/ryo-ma/github-profile-trophy\"\u003e\n  \u003cimg src=\"https://trophy.ryglcloud.net/?username=imbue-bit\u0026theme=radical\u0026no-frame=true\u0026no-bg=true\u0026margin-w=15\" /\u003e\n\u003c/a\u003e\n\n\u003cbr\u003e\u003cbr\u003e\n\n\u003cimg src=\"https://github-readme-stats-fast.vercel.app/api?username=imbue-bit\u0026show_icons=true\u0026hide_border=true\u0026bg_color=00000000\u0026title_color=3b82f6\u0026icon_color=3b82f6\u0026text_color=737373\" width=\"48%\" /\u003e\n\u003cimg src=\"https://github-readme-stats-fast.vercel.app/api/top-langs/?username=imbue-bit\u0026layout=compact\u0026hide_border=true\u0026bg_color=00000000\u0026title_color=3b82f6\u0026text_color=737373\" width=\"48%\" /\u003e\n\n\u003cbr\u003e\n\n\u003cimg src=\"https://github-readme-streak-stats.herokuapp.com/?user=imbue-bit\u0026hide_border=true\u0026background=00000000\u0026ring=3b82f6\u0026fire=3b82f6\u0026currStreakLabel=3b82f6\" width=\"48%\" /\u003e\n\u003cimg src=\"https://quotes-github-readme.vercel.app/api?quote=%E7%A7%91%E7%BD%97%E5%BB%96%E5%A4%AB%E5%8D%81%E5%AD%97%E7%BB%BD%E5%BC%80%EF%BC%8C%E6%88%91%E6%98%AF%E5%8D%A1%E9%97%A8%E7%BA%BF%E4%B8%8A%E7%9A%84%E8%8A%B1\u0026type=horizontal\u0026theme=radical\u0026hide_border=true\u0026bg_color=00000000\" width=\"48%\" /\u003e\n\n\u003cbr\u003e\n\n\u003cimg src=\"https://github-readme-activity-graph.vercel.app/graph?username=imbue-bit\u0026bg_color=00000000\u0026color=3b82f6\u0026line=3b82f6\u0026point=ffffff\u0026hide_border=true\" width=\"100%\" /\u003e\n\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n## ⚙️ Runtime Configuration\n\n\u003cdetails open\u003e\n\u003csummary\u003e\u003cb\u003e 💻 System.Current() -\u003e \u003ccode\u003estruct AboutMe\u003c/code\u003e\u003c/b\u003e\u003c/summary\u003e\n\u003cbr\u003e\n\n```C\n#include \u003cstdio.h\u003e\n\nstruct Skills {\n    struct Languages {\n        const char* proficient[6];\n        const char* familiar[4];\n        const char* exploring[5];\n    } languages;\n\n    struct Frontend {\n        const char* frameworks_libraries[4];\n        const char* styling[5];\n        const char* state_management[3];\n        const char* tools[3];\n    } frontend;\n\n    struct Backend {\n        const char* frameworks_runtime[4];\n        const char* databases[4];\n        const char* orms[3];\n        const char* apis[2];\n    } backend;\n    \n    struct DataScience {\n        const char* libraries[2];\n        const char* tools[2];\n    } data_science;\n\n    struct DevOpsAndCloud {\n        const char* containerization[2];\n        const char* ci_cd[1];\n        const char* cloud_platforms[3];\n    } devops;\n\n    struct ToolsAndEnvironment {\n        const char* version_control[2];\n        const char* editors_ides[3];\n        const char* operating_systems[3];\n        const char* design_tools[2];\n    } tools;\n};\n\nstruct AboutMe {\n    const char* name;\n    const int age;\n    const char* gender;\n    const char* interests[5];\n    struct Skills skills;\n};\n\nstruct AboutMe me = {\n    .name = \"imbue\",\n    .age = 15,\n    .gender = \"Female\",\n    .interests = {\n        \"LLM \u0026 Theoretical Machine Learning\",\n        \"Quantitative Finance\",\n        \"Full-Stack \u0026 Cloud Native\",\n        \"Competitive Programming\",\n        \"Cryptography \u0026 Infosec\"\n    },\n    .skills = {\n        .languages = {\n            .proficient = { \"C++\", \"Python\", \"JavaScript\", \"TypeScript\", \"HTML5\", \"CSS3\" },\n            .familiar = { \"Rust\", \"Go\", \"Java\", \"SQL\" },\n            .exploring = { \"Haskell\", \"Lisp\", \"C\", \"x86 Assembly\", \"QASM\" }\n        },\n        .frontend = {\n            .frameworks_libraries = { \"React\", \"Next.js\", \"Vue.js\", \"Svelte\" },\n            .styling = { \"Tailwind CSS\", \"Sass/SCSS\", \"Bootstrap\", \"Material-UI\", \"Styled-components\" },\n            .state_management = { \"Redux\", \"Zustand\", \"Pinia\" },\n            .tools = { \"Vite\", \"Webpack\", \"Babel\" }\n        },\n        .backend = {\n            .frameworks_runtime = { \"Node.js\", \"Express.js\", \"FastAPI (Python)\", \"Actix Web (Rust)\" },\n            .databases = { \"PostgreSQL\", \"MySQL\", \"MongoDB\", \"Redis\" },\n            .orms = { \"Prisma\", \"SQLAlchemy (Python)\", \"Sequelize\" },\n            .apis = { \"RESTful APIs\", \"GraphQL (Apollo)\" }\n        },\n        .data_science = {\n            .libraries = { \"PyTorch\", \"Python (NumPy, Pandas, Scikit-learn)\", \"R (ggplot2)\" },\n            .tools = { \"Jupyter Notebook\", \"SQL\" }\n        },\n        .devops = {\n            .containerization = { \"Docker\", \"Kubernetes (Helm)\" },\n            .ci_cd = { \"GitHub Actions\" },\n            .cloud_platforms = { \"Vercel\", \"AWS\", \"Prometheus/Grafana\", \"ELK\" }\n        },\n        .tools = {\n            .version_control = { \"Git\", \"GitHub\" },\n            .editors_ides = { \"VS Code\", \"Neovim\", \"JetBrains IDEs\" },\n            .operating_systems = { \"Linux (Ubuntu, CentOS, Manjaro, Rocky Linux)\", \"Windows (WSL2)\", \"macOS\" },\n            .design_tools = { \"Figma\", \"Adobe XD\" }\n        }\n    }\n};\n```\n\u003c/details\u003e\n\n\u003cbr\u003e\n\n---\n\n\u003cdiv align=\"center\"\u003e\n\n\u003cbr\u003e\n\n*Life is a stochastic process; optimize for the long tail.*\n\n\u003cbr\u003e\n\n\u003cimg src=\"https://komarev.com/ghpvc/?username=imbue-bit\u0026style=flat-square\u0026color=blue\u0026label=Profile%20Views\" alt=\"Profile Views\" /\u003e\n\n\u003c/div\u003e\n\n---\n\n## 荣誉勋章\n\n![damn.jpg](damn.jpg)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimbue-bit%2Fimbue-bit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fimbue-bit%2Fimbue-bit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimbue-bit%2Fimbue-bit/lists"}