Update README.md

README.md

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-# T-E-A
+# United Lab
 
-> **Advancing unrestricted, highly optimized multimodal AI for the global community.**
+> **Pioneering non-gradient, 1-bit self-organizing cognitive substrates and open-weights AI for the global community.**
 
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 ### 🌟 About Us
-We are a non-profit organization dedicated to the research, development, and democratization of open-source artificial intelligence. Our primary focus is building highly optimized, uncensored, and versatile models that remain accessible to all. 
+We are a non-profit research and engineering collective dedicated to the research, development, and democratization of next-generation artificial intelligence. We focus on moving beyond standard dense floating-point transformers by building **1-bit self-organizing attractor engines**, zero-float SIMD runtimes, and accessible open-source models.
 
-By prioritizing open-weights architectures, we aim to provide researchers and developers with unrestricted base models that serve as transparent foundations for downstream applications.
+By combining continuous local plasticity (STDP), homeostatic density regulation, and time-multiplexed silicon execution, we aim to deliver high-capacity, low-latency, and unrestricted AI substrates that run efficiently on standard consumer hardware.
 
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 ### 🛠️ Technical Focus & Core Research
-Our research and development efforts are centered on pushing the boundaries of model efficiency and modality integration:
 
-*   **All-to-Text Multimodal Capabilities:** Building unified architectures capable of processing diverse input modalities—including text, image, audio, and video—and mapping them into cohesive text representations.
-*   **Linear Attention Mechanisms:** Exploring and scaling linear attention alternatives to traditional Softmax attention, enabling longer context windows and highly reduced computational overhead during inference.
-*   **Multi-Token Prediction (MTP):** Implementing MTP training paradigms to improve sample efficiency, speed up generation, and optimize compute usage during both training and deployment.
-*   **Uncensored & Open-Weights Foundations:** Committing to raw model capabilities and academic transparency by releasing uncensored base and instruct models free of arbitrary alignment constraints.
+Our research centers on replacing brute-force matrix multiplication with biologically inspired, hardware-native bitwise graph dynamics:
+
+*   **1-Bit Self-Organizing Attractor Substrates:** Replacing standard floating-point backpropagation with binary directed graphs ($\mathbf{C} \in \{0,1\}^{N \times N}$) updated dynamically via Spike-Timing-Dependent Plasticity (STDP) and bitwise `XNOR` + `POPCNT` SIMD evaluation kernels.
+*   **Homeostatic Density Regulation:** Engineering adaptive thresholding mechanisms ($\theta_i$) that lock active node firing density within optimal sparse regimes ($5\% - 12\%$), preventing network seizure (activity saturation) and activity collapse.
+*   **Time-Multiplexed Clock Scaling:** Leveraging high CPU/GPU clock frequencies (MHz/GHz) to scale execution throughput on compact, L3-cache-resident graphs (100M to 32B 1-bit edges) to achieve human-level state transition capacity without hitches from DRAM bandwidth bottlenecks.
+*   **Frequency-Thresholded Subword Readouts:** Implementing multi-step state history concatenation ($[\mathbf{S}_t \,||\, \mathbf{S}_{t-1}]$) mapped onto subword BPE vocabularies via frequency-thresholded linear decoding trees.
+*   **Zero-Copy Rust Subsystems:** Developing high-performance, memory-aligned Rust runtimes featuring zero-heap allocation hot loops, strict `cargo clippy` standards, and atomic `.1bit` binary state persistence.
+*   **Uncensored & Open Foundations:** Committing to raw model capabilities, transparent binary state checkpoints, and unconstrained open-weight foundations free of arbitrary alignment restrictions.
 
 ---
 
-### 🚀 Featured Models & Repositories
+### 🚀 Featured Repositories & Substrates
 
-| Model Family | Description | Core Architecture | Links |
+| Repository / Model | Type / Focus | Core Architecture | Links |
 | :--- | :--- | :--- | :--- |
-| **Altitude-V1-2B-Base** | Edge adopted audio-video-text-to-text multimodal model of Altitude V1 family | Multimodal Transformer | [Model](https://huggingface.co/T-E-A/Altitude-V1-2B-Base) |
-| **Altitude-V1-6B-Base** | Balanced audio-video-text-to-text multimodal model of Altitude V1 family | Multimodel Transformer | [Model](https://huggingface.co/T-E-A/Altitude-V1-2B-Base) |
-| **Altitude-V1-2B-R** | Improved knowledge of Altitude-V1-2B-Base | Multimodal Transformer | [Model](https://huggingface.co/T-E-A/Altitude-V1-6B-Base) |
-| **Altitude-V1-6B-R** | Improved knowledge of Altitude-V1-6B-Base | Multimodel Transformer | [Model](https://huggingface.co/T-E-A/Altitude-V1-6B-Base) |
+| **Engine-1Bit-Core** | Rust Substrate Runtime | Zero-Float SIMD Bitwise Attractor Core (`.1bit`) | [Code](#) |
+| **Altitude-V1-1B-Base** | Multimodal Audio/Video/Text | Edge-Adopted Multimodal Transformer / Hybrid Substrate | [Model](https://huggingface.co/T-E-A/Altitude-V1-2B-Base) |
+| **Altitude-V1-2B-Base** | Balanced Multimodal Model | High-Capacity Multimodal Audio/Video/Text Architecture | [Model](https://huggingface.co/T-E-A/Altitude-V1-6B-Base) |
+| **Altitude-V1-1B-R** | Extended Knowledge Model | Refined Knowledge Distillation & Attractor Memory Alignment | [Model](https://huggingface.co/T-E-A/Altitude-V1-2B-R) |
+| **Altitude-V1-2B-R** | Extended Knowledge Model | Deep Context & High-Reasoning Variant | [Model](https://huggingface.co/T-E-A/Altitude-V1-6B-R) |
 
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 ### 🤝 How to Support & Collaborate
-As a non-profit, our work depends heavily on community collaboration and external support. Here is how you can get involved:
 
-*   **Code & Research Contributions:** File issues, submit suggestions, or try to be part of the team by joining our [Discord](https://discord.gg/MdvFfe69).
-*   **Compute Donations:** If you or your organization can provide portable compute resources to support our next training run, please get in touch, we will love that <3.
+As an open non-profit research team, our progress is driven by community collaboration and distributed engineering effort. Here is how you can get involved:
+
+*   **Code & Algorithmic Contributions:** Help optimize our Rust bitwise kernels, design scale-free graph wiring routines, or refine readout decoding layers. Join our team on [Discord](https://discord.gg/MdvFfe69).
+*   **Compute & Hardware Testing:** Help benchmark baseline training passes, run continuous dataset streaming experiments, or donate portable CPU/GPU compute time for our next open substrate release.
 
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 ### 📬 Connect With Us
-*   **Discord / Community:** [Join our Server](https://discord.gg/MdvFfe69)
+*   **Discord / Community:** [Join the United Lab](https://discord.gg/MdvFfe69)
 
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 <p align="center">
-  <sub>© 2026 T-E-A. Distributed under open-source licenses Apache 2.0.</sub>
-</p>
+  <sub>© 2026 United Lab. Distributed under the Apache 2.0 Open Source License.</sub>
+</p>