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README.md

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-# D●t Labs
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-**Researching intelligence from the byte up.**
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-Dot Labs is an independent AI research organization focused on **multimodal, byte-level intelligence**.
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-We explore what happens when models operate closer to raw information, reducing reliance on modality-specific tokenization and hand-designed representations. Our research investigates architectures and training methods capable of learning directly from low-level sequences across text and other modalities.
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-## Research
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-Our work currently centers around:
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-* **Byte-level modeling:** Learning directly from bytes rather than conventional text tokenization.
-* **Multimodal intelligence:** Developing representations and architectures that can generalize across different forms of information.
-* **Small language models:** Studying how much capability can emerge from compact, compute-efficient systems.
-* **Efficient architectures:** Exploring recurrence, parameter sharing, adaptive computation, and alternatives to simply scaling model size.
-* **Representation learning:** Investigating how models can discover useful structure with fewer assumptions about how their inputs should be encoded.
-
-## Where we're headed
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-We believe increasingly general models may emerge not only from making systems larger, but from making their foundations **simpler and more universal**.
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-Our long-term direction is toward models that can consume heterogeneous information through a shared low-level interface, learn useful internal representations on their own, and allocate computation intelligently.
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-Instead of building a different foundation for every modality, we're interested in a more fundamental question:
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-> **How little structure can we impose while still allowing intelligence to emerge?**
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-We're starting with bytes.
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