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# 👁️ LMM 🦀
[](https://wiseai.dev)
[](https://github.com/wiseaidotdev/lmm)
[](https://crates.io/crates/lmm)
[](https://docs.rs/lmm)
[](https://crates.io/crates/lmm)
[](https://pypi.org/project/lmm-rs/)
[](https://www.npmjs.com/package/@wiseaidev/lmm)
[](https://www.rust-lang.org/)
[](https://www.rust-lang.org)
[](LICENSE)
[](https://github.com/wiseaidev)
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> **LMM (Large Mathematical Model)** is a pure‑Rust framework that models higher‑dimensional reality through symbolic mathematics and physics simulation; Inspired by the Pharaonic model of intelligence: compress the world into durable, universal equations. No training. No GPU. No API key.
|  |  |  |
| [Download `lmm` binary](https://github.com/wiseaidotdev/lmm/releases/latest/download/lmm) | [Download `lmm.exe` binary](https://github.com/wiseaidotdev/lmm/releases/latest/download/lmm.exe) | `docker pull wiseaidev/lmm` |
| `cargo install lmm --features rust-binary` | `cargo install lmm --features rust-binary` | `docker run -it wiseaidev/lmm` |
| `lmm` ← launches CLI | `lmm` ← launches CLI | [Read DOCKER.md](https://github.com/wiseaidotdev/lmm/blob/main/DOCKER.md) |
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## 🎬 Demo
The following demonstrates the symbolic prediction engine generating coherent English sentences powered entirely by deterministic mathematical equations and structural Subject-Verb-Object grammar; No neural networks, no statistical models. The engine supports a full suite of CLI subcommands including `predict`, `summarize`, `sentence`, `paragraph`, `essay`, and `ask`, enabling multi-paragraph construction driven entirely by mathematics.
| <video src="https://github.com/user-attachments/assets/f20ed16f-d90e-4983-bc47-0de2ce5c5a4f"></video> | <video src="https://github.com/user-attachments/assets/680d4ef4-bab1-47d4-84e8-86a11aa93294"></video> | <video src="https://github.com/user-attachments/assets/299c280d-dcf3-484f-bf02-c37836811dcb"></video> |
| <video src="https://github.com/user-attachments/assets/06ef5c15-7743-4d62-908f-52d22288de76"></video> | <video src="https://github.com/user-attachments/assets/3b4bba24-012b-487b-98c8-91e61336cead"></video> | <video src="https://github.com/user-attachments/assets/fc1d0adc-e2c3-421a-b6b6-4b21dcf3af06"></video> |
## 🧠 What Does LMM Provide?
LMM bridges multimodal perception and actionable scientific discovery through five tightly integrated layers:
| **Perception** | `perception.rs`, `tensor.rs` | Raw bytes → normalised tensors |
| **Symbolic** | `equation.rs`, `symbolic.rs`, `discovery.rs` | GP symbolic regression, differentiation, simplification |
| **Physics** | `physics.rs`, `simulation.rs` | ODE models + Euler / RK4 / RK45 / leapfrog integrators |
| **Causal** | `causal.rs` | SCM graphs, do-calculus interventions, counterfactuals |
| **Cognition** | `consciousness.rs`, `world.rs`, `operator.rs` | Full perceive → encode → predict → act loop |
### ⚙️ Architecture
```mermaid
flowchart TD
A["Raw Input\n(bytes / sensors)"]
B["MultiModalPerception\n → Tensor"]
C["Consciousness Loop\nperceive → encode → predict\nevaluate → plan (lookahead)"]
D["WorldModel\n(RK4 physics)"]
E["SymbolicRegression\n(GP equation search)"]
F["CausalGraph\nintervention / counterfactual"]
G["Expression AST\ndifferentiate / simplify"]
A --> B --> C
C --> D
C --> E
E --> G
G --> F
D --> F
```
### 🔬 Key Capabilities
- 🧬 **Genetic Programming**: population-based symbolic regression with template seeding (linear, quadratic, periodic) and variable-enforcement guards.
- 📐 **Symbolic Calculus**: automatic differentiation (chain rule, product rule, trig) and constant-folding simplification.
- 🌀 **Physics Suite**: Harmonic Oscillator, Lorenz Attractor, Pendulum, SIR Epidemic, N-body Gravity; All implement `Simulatable`.
- 🔢 **Field Calculus**: N-D gradient, Laplacian, divergence, and 3-D curl via central differences.
- 🔗 **Causal Reasoning**: structural causal models, `do(X=v)` interventions, and counterfactual queries.
- 🧩 **Neural Operators**: circular convolution with SGD kernel learning and Fourier spectral operators.
- 🔤 **Text ↔ Equation**: losslessly encode any text string into a symbolic equation and recover it exactly via integer residuals.
- 🔮 **Symbolic Prediction**: equation-native text continuation using sliding-window GP regression and vocabulary anchoring.
- 🎲 **Stochastic Enhancement**: synonym-bank word replacement (`--stochastic`) delivers unique output each run while preserving mathematical sentence structure.
- 🎨 **Spectral Image Synthesis**: generate procedural PPM images from a text prompt by hashing it into Fourier wave components.
## 📦 Installation
The `lmm` crate ships the following Cargo features:
| `rust-binary` | Enables the standalone `lmm` terminal CLI executable |
| `cli` | Core CLI scaffolding (subsets of `rust-binary`) |
| `net` | Internet-aware `ask` command via DuckDuckGo search |
| `python` | Python extension module via `pyo3` / maturin |
| `node` | Node.js native add-on via `napi-derive` |
## 🦀 Rust
The `lmm` library is available on [crates.io](https://crates.io/crates/lmm). For the complete API reference, installation guide, and worked examples, see the **[Rust usage guide](https://github.com/wiseaidotdev/lmm/blob/main/RUST.md)**.
## 💻 Command-Line Interface
The `lmm` binary supports 15 subcommands spanning simulation, discovery, encoding, prediction, summarisation, and rich text generation: all powered by pure equations.
For the full option reference and usage examples, see the **[CLI documentation](https://github.com/wiseaidotdev/lmm/blob/main/CLI.md)** or run `lmm --help` after installing with `cargo install lmm --features rust-binary`.
## 🐍 Python
The Python bindings are published to PyPI as **`lmm-rs`** and are installed with `pip install lmm-rs`. Built with [maturin](https://www.maturin.rs/), the package ships pre-compiled wheels for major CPython versions and runs a fully embedded Tokio runtime; no `asyncio` required.
For installation instructions, configuration options, and full method signatures, see the **[Python usage guide](https://github.com/wiseaidotdev/lmm/blob/main/PYTHON.md)**.
## 🟩 Node.js
The Node.js bindings are published to npm as **`@wiseaidev/lmm`** and are installed with `npm install @wiseaidev/lmm`. Built with [napi-rs](https://napi.rs/), the package ships a pre-compiled `.node` add-on with TypeScript type definitions.
For installation instructions, type definitions, and examples, see the **[Node.js usage guide](https://github.com/wiseaidotdev/lmm/blob/main/NODE.md)**.
## 🌐 WebAssembly (WASM)
LMM natively targets `wasm32-unknown-unknown`. Because `reqwest` switches to the browser `fetch` API automatically, you can deploy LMM inside Rust frontend frameworks such as **Yew**, **Dioxus**, and **Leptos** without any additional glue code.
For CORS considerations, build steps, and usage details, see the **[WASM usage guide](https://github.com/wiseaidotdev/lmm/blob/main/WASM.md)**.
## 🤖 Agent Framework
The `lmm-agent` crate extends LMM with a fully autonomous, equation-based agent layer; no LLM, no API key, no training data.
| **[AGENT.md](AGENT.md)** | Architecture, quick-start, types, and async API reference |
| **[DERIVE.md](DERIVE.md)** | `#[derive(Auto)]` macro: generated traits and field contract |
| **[lmm-agent README](lmm-agent/README.md)** | Crate-level API reference, builder, and example |
| **[lmm-derive README](lmm-derive/README.md)** | Macro crate details and field rules |
## 📰 Publications & Research
The architecture, formal mathematics, and paradigm are fully documented in the official whitepaper:
**[Read the Whitepaper (PDF)](papers/lmm.pdf)**.
### Blog Posts
- [LLMs are Useful. LMMs will Break Reality](https://wiseai.dev/blogs/llms-are-usefull-lmms-will-break-reality): the original post that started this project.
- [Training Is An Evil Concept. LMMs Eliminates It Altogether](https://wiseai.dev/blogs/training-is-an-evil-concept-lmms-eliminates-it-altogether): ethical, architectural, and data advantages of training-free models.
## 📝 Citation
If you use LMM in your research, please cite our whitepaper:
```bibtex
@article{harmouch2026lmm,
author = {Mahmoud Harmouch},
title = {Mathematics Is All You Need: Training-Free Language Generation via
Symbolic Regression and Stochastic Determinism},
year = {2026},
url = {https://github.com/wiseaidotdev/lmm}
}
```
## 🤝 Contributing
Contributions are welcome! Feel free to open issues or pull requests on [GitHub](https://github.com/wiseaidotdev/lmm).
## 📄 License
Licensed under the [MIT License](LICENSE).
## ⭐ Star Us
If you use or enjoy LMM, please leave us a star on [GitHub](https://github.com/wiseaidotdev/lmm)! It helps others discover the project and keeps the momentum going ☕.
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