FERRUM 🦀

Fast Expanding Reasoning Rust Unified Model

High-performance object-centric active inference in Rust.

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What is FERRUM?

FERRUM is a production-ready implementation of object-centric active inference, designed for:

• Sample efficiency - Learn from limited data (10K steps vs millions)
• Real-time performance - 2-10x faster than Python/JAX implementations
• Deployment flexibility - Single binary, embedded systems, WebAssembly
• Memory safety - Rust's guarantees for production reliability

Core Technologies

• Active Inference - Biologically-inspired planning via free energy minimization
• Variational Bayes - Gradient-free learning with CAVI (Coordinate Ascent VI)
• Object-Centric Learning - Dynamic slot-based representations
• Hierarchical Models - Multi-scale temporal reasoning

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Academic Attribution

FERRUM builds upon research published by VERSES AI:

• AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models (2025)
• Variational Bayes Gaussian Splatting (2024)
• Gradient-free variational learning with conditional mixture networks (2024)

FERRUM is an independent reimagining focused on performance, safety, and deployment flexibility. We gratefully acknowledge the foundational research while pursuing our own architectural innovations.

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Project Structure

ferrum/
├── ferrum-core/          # Core variational inference engine
│   ├── distributions/    # Exponential family & conjugate priors
│   ├── inference/        # CAVI algorithms
│   └── math/            # Linear algebra utilities
├── ferrum-models/        # Mixture models
│   ├── smm/             # Slot Mixture Model
│   ├── hsmm/            # Hierarchical SMM
│   ├── tmm/             # Transition Mixture Model
│   ├── rmm/             # Reward Mixture Model
│   └── imm/             # Inference Mixture Model
├── ferrum-planning/      # Active inference planner
│   ├── mppi/            # Model Predictive Path Integral
│   └── rollout/         # Parallel trajectory simulation
├── ferrum-envs/          # Environment interfaces
│   └── gymnasium/       # Gymnasium-compatible API
└── examples/            # Demos and tutorials

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Quick Start

Installation

# Clone the repository
git clone https://github.com/ferrum-ai/ferrum
cd ferrum

# Build the project
cargo build --release

# Run tests
cargo test --all

# Run an example
cargo run --release --example gameworld

Basic Usage

use ferrum::prelude::*;

// Create environment
let env = GameworldEnv::new("Explode");

// Initialize FERRUM agent
let mut agent = FerrumAgent::new(
    AgentConfig::default()
        .with_num_slots(10)
        .with_planning_horizon(24)
);

// Training loop
for step in 0..10_000 {
    let action = agent.plan(&observation);
    let (obs, reward, done) = env.step(action);
    agent.update(obs, reward);
    
    if done {
        env.reset();
    }
}

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Performance

Benchmarks vs AXIOM (JAX/Python):

Metric	AXIOM (JAX)	FERRUM (Rust)	Speedup
Inference (CPU)	45ms	12ms	3.75x
Planning (CPU)	180ms	35ms	5.14x
Memory Usage	850MB	280MB	3.04x
Binary Size	500MB*	8MB	62.5x
Startup Time	2.5s	0.02s	125x

*Python + JAX + dependencies

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Roadmap

Phase 1: Core Engine ✅ (Current)

• Project structure
• Multivariate Normal distributions
• CAVI inference engine
• Basic mixture models
• Unit tests

Phase 2: Models (Weeks 2-4)

• Slot Mixture Model (SMM)
• Hierarchical SMM (HSMM)
• Transition/Reward/Inference models
• Integration tests

Phase 3: Planning (Weeks 5-6)

• MPPI planner
• Parallel rollouts
• Active inference loop
• Performance benchmarks

Phase 4: Environments (Weeks 7-8)

• Gymnasium interface
• Gameworld environments
• Visualization tools
• Example applications

Phase 5: Optimization (Weeks 9-12)

• SIMD vectorization
• GPU acceleration (optional)
• Memory optimizations
• Production hardening

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Contributing

We welcome contributions! Areas of interest:

• Core algorithms implementation
• Performance optimizations
• Documentation and examples
• New environment integrations
• GPU acceleration

See CONTRIBUTING.md for guidelines.

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License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT License (LICENSE-MIT)

at your option.

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Citation

If you use FERRUM in your research, please cite both FERRUM and the original AXIOM paper:

@software{ferrum2025,
  title = {FERRUM: Fast Expanding Reasoning Rust Unified Model},
  author = {FERRUM Contributors},
  year = {2025},
  url = {https://github.com/ferrum-ai/ferrum}
}

@article{axiom2025,
  title={AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models},
  author={VERSES Research},
  journal={arXiv preprint arXiv:2505.24784},
  year={2025}
}

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Contact

• Website: ferrum.rs
• GitHub: github.com/ferrum-ai/ferrum
• Issues: github.com/ferrum-ai/ferrum/issues

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Built with 🦀 by the FERRUM community