A fundamentally new architecture that stores 10× more knowledge per parameter than today's LLMs — the first step toward superintelligent AI.
Research shows that current large language models store roughly 2 bits of knowledge per parameter. Our novel architecture achieves up to 20 bits per parameter — a fundamental 10× improvement at the same model size.
For context, biological neurons store approximately 1,600 bits per synapse. Diffora's architecture is designed to scale toward 1,000 bits per parameter, approaching biological efficiency for the first time in machine learning.
This isn't incremental progress. Higher information density per parameter means greater general capability, stronger reasoning, fewer hallucinations, and dramatically reduced compute requirements.
More knowledge encoded in fewer parameters means fundamentally smarter models, not just bigger ones. A 1B Diffora model could rival much larger LLMs.
Dense knowledge storage reduces the gap between what a model "knows" and what it generates, dramatically improving factual accuracy and reliability.
Achieve frontier-level performance at a fraction of the compute. Smaller, faster models that can run on-device and at the edge.
Our "Thinking" models represent a new paradigm. As parameter efficiency scales toward biological levels, the ceiling for machine intelligence rises dramatically.
Our initial proof-of-concept demonstrates the architecture's extraordinary efficiency and speed with a compact image generation model.
Deploying a next-generation language model to demonstrate the architecture's capabilities at scale. Designed to rival models 10× its size.
Scaling toward 1,000 bits per parameter with next-generation "Thinking" architectures. The first real step toward artificial superintelligence.
Side-by-side comparison showing a 10× increase in performance compared to the standard transformer attention mechanism.
Demonstrates a compact model's capabilities — validating the architecture's efficiency and quality on image generation tasks.