Universal Computing Research

Universal Computing Research is an independent AI research organization focused on efficient, compact, and architecture-driven deep learning.

We build small language models, parameter-efficient neural layers, custom tokenizers, and research artifacts that test how far useful intelligence can be pushed under strict compute, memory, and parameter budgets.

Research direction

Our work is centered on a simple question:

How much capability can be recovered through better architecture, tokenization, data curricula, and parameterization, without relying only on scale?

Current focus areas:

• Small language models
• Parameter-efficient architectures
• Random projection layers
• Custom tokenization pipelines
• Arithmetic and algorithmic reasoning

Released models

Atom3.4m

A 3.41M parameter decoder-only language model trained from scratch for studying compact architectures, curricula, and small-model benchmarking.

• Grouped-query attention
• RoPE positional embeddings
• RMSNorm
• Gated SiLU feed-forward layers
• Custom 4,096-token byte-level BPE tokenizer
• Approximately 5B training tokens

View Atom3.4m

Atom2.7m

A 2.74M parameter causal language model with an arithmetic-aware tokenizer and digit-structure features.

• Custom byte-level BPE tokenizer
• Atomic digit and operator handling
• Least-significant-digit-first numeric representation
• Place and role embeddings for integer arithmetic
• Strong ArithMark-2.0 performance for its size

View Atom2.7m

Research

Parametrized Random Projection

We study Parametrized Random Projection layers as lightweight replacements for dense linear layers.

The core idea is to separate fixed feature mixing from learnable adaptation: a non-trainable random projection performs the mixing, while small learnable element-wise parameters modulate the input and output.

This reduces trainable parameter count from quadratic to linear scale while preserving much of the utility of dense projections.

Read the paper

Open source

Our models and research artifacts are released to support reproducible, open, and practical AI research.