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README.md

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-title: README
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+title: Universal Computing Research
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+license: apache-2.0
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-Edit this `README.md` markdown file to author your organization card.
+![Background image](bg.png)
+
+# 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](https://huggingface.co/UniversalComputingResearch/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](https://huggingface.co/UniversalComputingResearch/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](https://arxiv.org/abs/2512.13480)
+
+## Open source
+
+Our models and research artifacts are released to support reproducible, open, and practical AI research.
+