https://github.com/imbue-bit/imbue-bit
🌺 GitHub profile
https://github.com/imbue-bit/imbue-bit
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🌺 GitHub profile
- Host: GitHub
- URL: https://github.com/imbue-bit/imbue-bit
- Owner: imbue-bit
- License: cc0-1.0
- Created: 2025-08-29T10:10:44.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2026-06-08T05:13:31.000Z (23 days ago)
- Last Synced: 2026-06-08T07:14:47.682Z (23 days ago)
- Topics: c, github-profile, readme
- Homepage:
- Size: 1.76 MB
- Stars: 16
- Watchers: 1
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ✦ ɪᴍʙᴜᴇ ✦
*When the world stopped, I started compiling.*
> *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。*
> — 《说吧，记忆》，弗拉基米尔·纳博科夫
## 🔬 Recent Research & Publications
My 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.
### 📂 Research by Domain
📐 Deep Learning Theory & Scaling Laws
* **[Data Scaling as Progressive Coverage of a Predictive Contribution Spectrum](https://arxiv.org/abs/2605.20196)**
* *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 ($R^2 \approx 0.96$).
* **[A Formal Kinetic Theory for Zeroth-Order Newton Dynamics](https://paper.chunjiang.dev/)**
* *Core Contribution:* Develops a kinetic framework for Z-O Newton methods, providing a Stein-corrected Hessian estimator and exposing the curvature-variance trade-off.
* **[Reconstructing High-Resolution Hyperparameter Loss Landscapes](https://paper.chunjiang.dev/)**
* *Core Contribution:* Frames hyperparameter tuning as a landscape reconstruction problem, using active surrogate modeling to find robust, generalizable minima.
🧠 LLM Reasoning & Alignment
* **[Inverting the Search Dynamics: LLMs as Semantic Leaders in MCTS](https://paper.chunjiang.dev/)**
* *Core Contribution:* Proposes *Leader-Follower MCTS*, where the LLM steers search with macro-actions, improving performance on GSM8K, MATH, and HumanEval.
* **[The Statistical Illusion of Rejection Sampling in LLMs](https://paper.chunjiang.dev/)**
* *Core Contribution:* Bridges the gap between heuristic truncation in LLM sampling and true mathematical alignment, revealing statistical biases.
* **[Expected Value Alignment for Generative Reward Modeling](https://paper.chunjiang.dev/)**
* *Core Contribution:* Introduces *EVA*, a reward modeling paradigm for theorem proving that extracts continuous scores from discrete token distributions.
* **[Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context Decoding](https://paper.chunjiang.dev/)**
* *Core Contribution:* Presents *Soft-NBCE*, which replaces hard chunk selection with soft fusion, improving reasoning while maintaining memory efficiency.
📈 Quantitative Finance & Applied Systems
* **[Deep Learning under Continuous Distribution Shift for Quant Finance](https://paper.chunjiang.dev/)**
* *Core Contribution:* Formulates a non-stationary NTK and spectral tracking SDE to model DL performance under persistent market distribution shifts.
* **[AdaPrecise: A Task-Agnostic Dynamic Precision Routing Framework](https://paper.chunjiang.dev/)**
* *Core Contribution:* A Gumbel-Softmax based framework for dynamic precision routing that optimizes model efficiency for inference on edge devices.
🚗 Vision-Language-Action (VLA) & Embodied AI
* **[Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Driving](https://paper.chunjiang.dev/)**
* *Core Contribution:* A sparse, energy-based framework for autonomous driving that uses VLMs for class-agnostic perception and Lagrangian action minimization.
🌍 Beyond the Papers: My Multiverse of Engineering & Finance
* 📈 **Quantitative Finance:** I actively manage ~5 Million CNY in quantitative funds, integrating modern CS and deep learning into strategies to generate alpha.
* ⚙️ **Systems & DevOps:** An advocate for Clean Code & 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.
* 🌐 **Frontend & 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.
* 🛡️ **CTF & CP:** I'm active in XCTF (Crypto & Web) with contributions in problem-setting and write-ups, alongside a brief but intense stint in Competitive Programming.
## 🤖 Open Source & Trained Models
* **[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.
* **[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.
* **[RWKV-7-Prover-1.5B](https://huggingface.co/imbue2025/RWKV-7-Prover-1.5b)**: A formal math model leveraging RWKV-7 & Condor-inspired data synthesis for high-precision Lean 4 auto-formalization.
* **[LPR-Oracle](https://huggingface.co/imbue2025/LPR-Oracle)**: A forecasting model for China’s Loan Prime Rate (LPR) in financial markets.
* **[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.
## 📊 GitHub Analytics
## ⚙️ Runtime Configuration
💻 System.Current() -> struct AboutMe
```C
#include
struct Skills {
struct Languages {
const char* proficient[6];
const char* familiar[4];
const char* exploring[5];
} languages;
struct Frontend {
const char* frameworks_libraries[4];
const char* styling[5];
const char* state_management[3];
const char* tools[3];
} frontend;
struct Backend {
const char* frameworks_runtime[4];
const char* databases[4];
const char* orms[3];
const char* apis[2];
} backend;
struct DataScience {
const char* libraries[2];
const char* tools[2];
} data_science;
struct DevOpsAndCloud {
const char* containerization[2];
const char* ci_cd[1];
const char* cloud_platforms[3];
} devops;
struct ToolsAndEnvironment {
const char* version_control[2];
const char* editors_ides[3];
const char* operating_systems[3];
const char* design_tools[2];
} tools;
};
struct AboutMe {
const char* name;
const int age;
const char* gender;
const char* interests[5];
struct Skills skills;
};
struct AboutMe me = {
.name = "imbue",
.age = 15,
.gender = "Female",
.interests = {
"LLM & Theoretical Machine Learning",
"Quantitative Finance",
"Full-Stack & Cloud Native",
"Competitive Programming",
"Cryptography & Infosec"
},
.skills = {
.languages = {
.proficient = { "C++", "Python", "JavaScript", "TypeScript", "HTML5", "CSS3" },
.familiar = { "Rust", "Go", "Java", "SQL" },
.exploring = { "Haskell", "Lisp", "C", "x86 Assembly", "QASM" }
},
.frontend = {
.frameworks_libraries = { "React", "Next.js", "Vue.js", "Svelte" },
.styling = { "Tailwind CSS", "Sass/SCSS", "Bootstrap", "Material-UI", "Styled-components" },
.state_management = { "Redux", "Zustand", "Pinia" },
.tools = { "Vite", "Webpack", "Babel" }
},
.backend = {
.frameworks_runtime = { "Node.js", "Express.js", "FastAPI (Python)", "Actix Web (Rust)" },
.databases = { "PostgreSQL", "MySQL", "MongoDB", "Redis" },
.orms = { "Prisma", "SQLAlchemy (Python)", "Sequelize" },
.apis = { "RESTful APIs", "GraphQL (Apollo)" }
},
.data_science = {
.libraries = { "PyTorch", "Python (NumPy, Pandas, Scikit-learn)", "R (ggplot2)" },
.tools = { "Jupyter Notebook", "SQL" }
},
.devops = {
.containerization = { "Docker", "Kubernetes (Helm)" },
.ci_cd = { "GitHub Actions" },
.cloud_platforms = { "Vercel", "AWS", "Prometheus/Grafana", "ELK" }
},
.tools = {
.version_control = { "Git", "GitHub" },
.editors_ides = { "VS Code", "Neovim", "JetBrains IDEs" },
.operating_systems = { "Linux (Ubuntu, CentOS, Manjaro, Rocky Linux)", "Windows (WSL2)", "macOS" },
.design_tools = { "Figma", "Adobe XD" }
}
}
};
```
---
*Life is a stochastic process; optimize for the long tail.*
---
## 荣誉勋章
