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| title: Universal Computing Research |
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| colorTo: indigo |
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| license: apache-2.0 |
| emoji: 🌍 |
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| <img src="galaxy.png" alt="Background image" width="1200"> |
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| # Universal Computing Research |
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| **Universal Computing Research** is an independent AI research organization focused on efficient, compact, and architecture-driven deep learning. |
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| 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. |
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| ## Research direction |
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| Our work is centered on a simple question: |
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| > How much capability can be recovered through better architecture, tokenization, data curricula, and parameterization, without relying only on scale? |
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| Current focus areas: |
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| - Small language models |
| - Parameter-efficient architectures |
| - Random projection layers |
| - Custom tokenization pipelines |
| - Arithmetic and algorithmic reasoning |
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| ## Released models |
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| ### Atom3.4m |
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| A 3.41M parameter decoder-only language model trained from scratch for studying compact architectures, curricula, and small-model benchmarking. |
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| - Grouped-query attention |
| - RoPE positional embeddings |
| - RMSNorm |
| - Gated SiLU feed-forward layers |
| - Custom 4,096-token byte-level BPE tokenizer |
| - Approximately 5B training tokens |
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| [View Atom3.4m](https://huggingface.co/UniversalComputingResearch/Atom3.4m) |
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| ### Atom2.7m |
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| A 2.74M parameter causal language model with an arithmetic-aware tokenizer and digit-structure features. |
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| - 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 |
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| [View Atom2.7m](https://huggingface.co/UniversalComputingResearch/Atom2.7m) |
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| ## Research |
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| ### Parametrized Random Projection |
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| We study **Parametrized Random Projection** layers as lightweight replacements for dense linear layers. |
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| 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. |
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| This reduces trainable parameter count from quadratic to linear scale while preserving much of the utility of dense projections. |
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| [Read the paper](https://arxiv.org/abs/2512.13480) |
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| ## Open source |
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| Our models and research artifacts are released to support reproducible, open, and practical AI research. |
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