Pulse-Field v4.0: An Experimental Testbed for Event-Driven, Physics-Based AI

Pulse-Field v4.0 presents a groundbreaking approach in AI by introducing an $O(N)$ Event-Driven Routing mechanism that redefines the traditional Transformer architecture. This testbed incorporates State Space Models (SSM) for improved context retention while challenging the existing $O(N^2)$ Attention Matrix framework.

Why Pulse-Field?

This innovative architecture emphasizes efficiency, reducing both model size and computational complexity. Compared to standard Transformers like GPT-2, Pulse-Field achieves remarkable efficiency metrics:

Performance Insights

Pulse-Field's optimizations enable it to outperform traditional models in speed and intelligence verification:

• Efficiency: The testbed is structurally 4 times lighter and executes significantly fewer operations than competing models.
• Latency: In practical scenarios, Pulse-Field exhibits faster processing times at higher context lengths, noted by improved speed observed when managing larger input sizes:

Understanding the Architecture

The architecture consists of:

1. The Impulse: A vector packet carrying a Hidden State ($H$) governed by SSM equations.
2. The Field: A sparse graph of Hybrid Crystals connected through the impulse.
3. Physics: The routing mechanism is informed by energy constraints, effectively filtering out noise and preserving clarity.

Get Involved

Community participation is encouraged. Users can audit the code and verify benchmarks through the easy-to-use testbed provided. For running tests, simply clone the repository and execute the provided demo script to experience its capabilities firsthand:

git clone https://github.com/makimilan/pulse-field-core.git
cd pulse-field-core
pip install -r requirements.txt

# Run the full integrity and benchmark suite
python run_v3_demo.py

Explore how Pulse-Field v4.0 redefines the possibilities of Event-Driven, Physics-Based AI.