Benith Inc.
Benith Research
Benith Research is an experimental machine-learning effort focused on discovering neural architectures through evolutionary search over primitive operations. The current system builds and evaluates graphs composed from reductions, projections, local window extraction, normalization, and tensor transforms, then reruns promising candidates for validation.
What This Is
This is a small independent research effort under Benith Inc. The goal is to develop a search process that can repeatedly discover multiple strong model families, let them compete in the same runs, and transfer that process to stronger sequence and language-modeling tasks.
Current Results
On MNIST, the system has already produced search-discovered models that outperform a conventional LeCun-style reference. These runs were trained on the full MNIST training set and evaluated on the full 10,000-example MNIST test set.
Why More Compute
The bottleneck is experiment volume and validation throughput. More GPU access would directly increase the number of candidates we can evaluate, the number of promising models we can rerun to full validation, and the speed at which we can test improved mutation and selection strategies.
Contact
This page is a lightweight public summary for program applications and technical outreach. Benith Inc. is the legal entity behind the work. Contact: timprepscius@gmail.com.