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license: gpl-3.0
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# Metis-OLMoE-Latent-Telemetry: Spikenaut SNN Routing

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## 1. The Origin of my Neuromorphic journey and this project
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## 1. The Origin of Spikenaut
Before this was a formal dataset, it was an attempt to solve a bare-metal problem. I had been experimenting with mining telemetry, HFT bots, and sync node data to train a spiking neural network (SNN), but the data kept returning dead zeros in value after being used for training. 

The breakthrough came entirely by accident. I was running heavy mods—DLSS 4.0 and path tracing—on Cyberpunk 2077 and the Resident Evil 4 Remake. My workstation PC was screaming, pushing harder and louder than it ever did during crypto mining. That sparked the realization: *What if I used raw gaming telemetry data for neuromorphic spike data conversion?* What if I could use this intense hardware stress to create an artificial heartbeat for AI?

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When I pitched this idea, most people didn't believe the spike data conversion would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, the Spikenaut architecture was born. The true origin of this project is deeply personal—it stems from my switch from Computer Science to Electrical Engineering, the struggle to learn on my own terms, and the drive to build an architecture that adapts dynamically. Now, I am dropping these new SNN quantization models and datasets on Hugging Face to prove the math works and to keep the research completely open and transparent.

## 2. The Science: Semantic Attractor Clustering
This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the **Spikenaut** SNN architecture. The objective is to map the physical routing of LLM embeddings (specifically from the allenai/OLMoE-1B-7B-0125-Instruct-GGUF Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics.
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When I pitched this idea, most people didn't believe it would be good AI training data (spike data conversion)would work. But after refining the early thermal equations using that Resident Evil 4 telemetry, the Spikenaut architecture was born. The true origin of this project is deeply personal—it stems from my switch from starting Economics, Pre-med, Finance, Computer Science major switch to Electrical Engineering, the struggle to learn on my own terms, and the drive to build an architecture that adapts dynamically. Now, I am dropping these new SNN quantization models and datasets on Hugging Face to prove the math works and to keep the research completely open and transparent.

## 2. The Science: Semantic Attractor Clustering
This dataset contains the raw bare-metal telemetry logs and latent space visualizations generated by the **Spikenaut** SNN architecture. The objective is to map the physical routing of LLM embeddings (specifically from the OLMoE-0125 Mixture of Experts model) as they are processed by biologically-inspired neuronal fatigue mechanics.
>>>>>>> ec02242 (Added all neccessary data files.)>>>>>>> ec02242 (Added all neccessary data files.)

The primary discovery documented here is **Semantic Attractor Clustering**. By applying L2 Normalization to F16-to-F32 casted embeddings, the SNN bounds the semantic pressure to a unit sphere. This prevents "Winner-Take-All" routing collapse and forces the network to organically balance the load. The resulting telemetry proves that the SNN physically routes different semantic concepts (e.g., abstract philosophy vs. rigid code syntax) into distinct, repeatable biological pathways.

## 3. Experiment Progression
The dataset documents the chronological progression from synthetic baselines to actual semantic routing:

* **Phase 1: Synthetic Baseline (Smoke Test)**
  * **Input:** Synthetic sine wave.
  * **Result:** Verified the GPU temporal loop (10,000 ticks) and basic biological fatigue without crashing the CUDA context.
* **Phase 2: The F16 Magnitude Collapse (Unbounded)**
  * **Input:** Real LLM embeddings (OLMoE).
  * **Result:** Unscaled F16-to-F32 extraction resulted in raw, unbounded electrical pressure. A single expert neuron (Walker ~620) was overwhelmed, causing a routing collapse where one walker took the entire load for the full temporal loop.
* **Phase 3: L2 Normalization & Philosophy Attractors**
  * **Input:** `"Let's teach this MoE model..."` (Abstract English).
  * **Result:** L2 Normalization successfully shattered the routing collapse. Energy dynamically settled into high-register attractor bands, predominantly isolating into the **2000-route**.
* **Phase 4: Semantic Clustering (Code & Math Logic)**
  * **Input A:** `fn main() { println!("Hello, World!"); }` (Rust Syntax)
  * **Input B:** `"The derivative of a constant is mathematically zero."` (Math Logic)
  * **Result:** The SNN abandoned the 2000-route completely. Both raw Rust syntax and mathematical logic organically fell into the exact same **600-800 frequency band**. This demonstrates that the network physically maps highly structured logic tasks to adjacent biological neighborhoods.

**Dataset for Spikenaut SNN Research**

This dataset contains latent telemetry and routing data generated from the SNN-quantized version of AllenAI’s OLMoE-1B-7B-0125-Instruct model using the `corinth-canal` pipeline. The heavy mathethical analysis is documented and implemented in the 'surrogate_viz.jl' repository.



### Purpose
These files are used to study spiking behavior, routing stability, and adaptive quantization (SAAQ) in SNN-converted MoE models. The data feeds SymbolicRegression.jl to discover new equations for improved SNN quantization and ultimately trains the pure native **Spikenaut** SNN.

### Folder Structure

- **routing/** — CSV files containing routing and latent telemetry data
- **first-day/** — Early experimental runs (optional)
- **SAAQ 3.0/** — Future runs with new algorithm versions
- **experiments/** — Additional test configurations and variants
- **results/**
  - **plots/** — Visualization of SNN routing paths and firing density
  - **raw_telemetry/** — Original tick-by-tick log files

  ### Origin Hardware Baselines (`/origin_hardware_baselines/`)
This directory contains the foundational, bare-metal hardware telemetry that inspired the Spikenaut SAAQ thermal equations. 

* **`RE4_path_tracing_telemetry.csv`**: This file captures the extreme hardware stress (GPU/CPU temps, package power, clock speeds) generated by running the *Resident Evil 4 Remake* with heavy modifications (path tracing, DLSS 4.0) on a high-performance workstation PC. 
* **Purpose:** This specific dataset proved that intense gaming workloads create a highly dynamic, non-zero biological "heartbeat" compared to static crypto-mining telemetry. These exact thermal fluctuations were used to derive the baseline fatigue limits for the SNN's `tick_gpu_temporal` loop.

  ### Related Repos
- [corinth-canal](https://github.com/Limen-Neural/corinth-canal) — SNN quantization pipeline
- [Surrogate_Viz.jl](https://github.com/Spikenaut/Surrogate_Viz.jl) — Symbolic regression and visualization