Publications
Research
BlackMind studies machine intelligence at its true substrate — compiled bytecode, Boolean logic, and standard-cell silicon. Every capability claim is discharged by machine proof, and every number re-derives from a committed, machine-checked receipt.
All papers

Chasing the Small-Frontier on a Fixed Rig
A measurement-driven account of training a small language model to frontier quality on two fixed GPUs — with adversarial self-audit of the lab's own instruments.
Read paper
Large Binary Models
A model that learns to read and write compiled bytecode directly — no natural language in the loop.
Read paper
Binary Circuit Models
Compiling a trained binary model into SAT-verified logic gates — and taping it out in 130nm silicon.
Read paper
The General Purpose Neural Computer
A differentiable machine that learns WebAssembly's semantics from execution alone — deterministic, length-generalizing, machine-proven.
Read paper
