Solving Intelligence.
We are ML Research Inc. We build foundation models that reason, simulate, and discover.
From molecular biology to quantum mechanics.
Compute
10k+ H100s
Our private cluster allows for training runs that exceed 10^26 FLOPS, enabling emergent behaviors in our foundational models.
Parameters
50T Tokens
Training on the largest curated dataset of scientific literature, code, and mathematical proofs ever assembled.
Impact
SOTA Benchmarks
Achieving superhuman performance on MATH, GPQA, and protein folding prediction tasks.
The Alignment Thesis
"We believe that intelligence is the fundamental resource of the universe. Our mission is not just to build it, but to align it with the preservation of human complexity."
Safety by Design
We do not train black boxes. Every architecture we release includes interpretability hooks at the kernel level.
Open Weights
Scientific progress demands transparency. We commit to releasing the weights of our mid-tier models to the academic community.
The Research Matrix
Generative Biology
We are building "Bio-GPT": A unified model for the language of life. By treating DNA sequences and protein structures as tokens, we can generate novel enzymes that do not exist in nature to cure rare diseases.
Formal Math
Neuro-symbolic solvers that can verify theorems in Lean 4, bridging intuition and formal logic.
Material Science
Predicting crystal structures and superconductors using graph neural networks (GNNs).
The "Reasoning" Engine
Moving beyond statistical prediction to causal inference. Our new architecture, Arch-Z, implements efficient chain-of-thought processing at the kernel level.
> hypothesis_generated: 0.98 confidence
> verifier_active...
Selected Publications
Emergent World Models in Large Scale Biological Transformers
E. Vance, C. Brown, et al.
Scaling Laws for Neuro-Symbolic Theorem Proving
J. Smith, S. Wu, et al.
Cortex-1: A Generalist Agent for Scientific Discovery
ML Research Inc. Team
Introducing Cortex
The interface for our flagship model. Visualize high-dimensional latent spaces and interact with multimodal outputs in real-time.
Scanning PDB database... [Done]
Generating folding trajectory (AlphaFold-3 variant)...
Optimization found: L45 H (Confidence: 99.2%)
Stability Score
98.4+2.4%
Trusted By Research Labs At
The Minds Behind The Machines
James Smith
Chief Executive Officer
Charles Brown
Lead ML Researcher
Dr. Eleanor Vance
Chief Scientist
Dr. Aris Thorne
Head of Perception
Sarah J. Wu
Lead Architect
David Okonjo
Systems Research