Data directories getting their own README.md to practice better engineering habbits.

experiments/README.md

@@ -1,6 +1,12 @@
 # Visual Smoke Tests: Metis Hardware Validation
 
-This directory contains the initial visual validation runs for routing encoder. These maps document the critical "smoke test" phase where the CUDA and Rust kernels were first fired up on the Ship of Theseus(rmems).
+This directory contains the initial visual validation runs for routing encoder. These maps document the critical "smoke test" phase where the CUDA and Rust kernels were first fired up on the Ship of Theseus(rmems custom workstation).
+
+## Why this was an important milestone
+This was the first time the routing encoder was validated on real hardware. The visual maps show the network's internal routing behavior and scatter patterns under controlled, artificial conditions before introducing the dense OLMoE-1B-7B-0125-Instruct-GGUembeddings.
+
+## SNN Physics: The "Walker"
+In these datasets, the Y-axis represents the **Walker**. The walker acts as a pulse of electrical energy (a spike). Because this is a biological system, energy cannot flow everywhere at once. The network must test different paths. The walker's goal is to physically explore the network to find the routing pathway with the *least electrical resistance*.
 
 > **Note:** Raw telemetry `.txt` logs for these specific runs were not preserved by accident. These `.png` files serve as the visual baseline that the engine's temporal loops and routing mechanics were compiling and executing without crashing.
 

first-day-testing-real-weights/README.md

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+# First Day Testing Real Weights: Metis Semantic Routing
+
+This directory serves as the chronological forensic log of the first day the the  was injected with real Large Language Model embeddings on the Ship of Theseus workstation. 
+
+Moving away from synthetic baseline tests, this phase integrated the **`OLMoE-1B-7B-0125-Instruct-GGUF`** model. The objective was to observe how biologically-inspired SNN fatigue mechanics handle the dense semantic pressure of a Mixture-of-Experts (MoE) architecture using custom Rust and CUDA ML integrations. To test if the L2 Normalization was actually working, we needed to see if the network could adapt to different types of data.  Had each test been identical, it would have indicated that the normalization created a "lazy resting state." Instead, each test showed different routing patterns, proving the network was dynamically adapting.
+
+## How it mimics a biological system
+- **L2 Normalization**: Prevents any single neuron from becoming too dominant, mimicking how biological brains distribute energy across networks.
+- **Spike-based routing**: Walkers (spikes) physically explore the network, finding the path of least resistance, just like electrical impulses in a biological brain.
+- **Fatigue mechanics**: Neurons that fire too much become less responsive, preventing energy overload and allowing the network to adapt.
+
+## SNN Physics: The "Walker"
+In these datasets, the Y-axis represents the **Walker**. The walker acts as a pulse of electrical energy (a spike). Because this is a biological system, energy cannot flow everywhere at once. The network must test different paths. The walker's goal is to physically explore the network to find the routing pathway with the *least electrical resistance*.
+
+## The Progression of Discovery
+
+### 1. `first-test-failed/` : The Routing Collapse (Blackhole)
+* **The Issue:** A misconfiguration in the CUDA kernels, which were searching for F32 data instead of F16.
+* **The Result:** The voltage energy was so high that a single walker (around ID 620) got permanently blasted with energy. It became a "blackhole," causing a routing collapse where the energy could not disperse and explore the network.
+
+### 2. `second-test/` : Attractor Discovery (L2 Normalization)
+* **The Input:** `"Teaching OMLoE the language of SNN"`
+* **The Fix:** L2 Normalization was applied to the voltage.
+* **The Result:** The normalization allowed the walker to naturally explore the network and find the path of least resistance. The energy settled into a comfortable state, routing primarily through **Walker 2000**, with secondary echoes in Walkers 700 and 1450.
+
+### 3. `third-test/` : Rust Syntax (The True Victory)
+* **The Input:** `fn main () { println!(); }`
+* **The Result:** The routing changed completely from the second test. This was the massive win: it proved that the L2 Normalization forced the hardware to dynamically adapt to the data. If the graph had looked the same as the English prompt, it would have indicated that the normalization created a "lazy resting state." Instead, the code syntax was physically routed to a completely different biological neighborhood.
+
+### 4. `fourth-test/` : Math Logic Clustering
+* **The Input:** `"The derivative of a constant is mathematically zero."`
+* **The Result:** The network routed the mathematical logic into the exact same frequency band used by the Rust syntax. This proved **Semantic Attractor Clustering**—the SNN physically maps rigid, structured logic tasks (math and code) to the same adjacent physical pathways to conserve energy.
+
+## Biggest victory? ##
+* **New custom dataset** for future SymbolicRegression.jl experiments to find new underlying equations for SAAQ (Semantic Attractor Architecture Quantization)
+
+## Environment & Architecture
+* **Model:**    
+* **Compute:** ASUS ProArt GeForce RTX 5080 (16GB VRAM) | AMD Ryzen 9 9950X
+* **Workstation:** Ship of Theseus (Fedora 43)
+* **Actual Biological Implementation:** Custom Rust/CUDA corinth-canal for SNN quantization research discovery
+* **Visualizer and Mathematical Analysis:** Surrogate_Viz.jl 

origin_hardware_baselines/resident_evil_4/README.md

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+# Resident Evil 4 Path Tracing Telemetry: The SNN Heartbeat
+
+## Overview
+This directory contains `RE4_path_tracing_telemetry.csv`, the foundational bare-metal hardware dataset that inspired the thermal and fatigue equations for the Metis SNN architecture. 
+
+It contains high-resolution timestamped polling of GPU/CPU temperatures and power package wattage.
+
+## The Origin Story: Gaming as a Biological Baseline
+Early iterations of this Spiking Neural Network (SNN) attempted to use telemetry from crypto-mining nodes, High-Frequency Trading (HFT) bots, and sync node data to train the spike data conversion. However, it was hard to accurately capture the correct telemetry data, which frequently returned dead zeros after being used for training, failing to create a dynamic "heartbeat.  Where as gaming already has well established proven telemetry data that can be accurately captured and used for training"
+
+The breakthrough occurred during a heavy hardware stress test on the Ship of Theseus workstation. Running *Cyberpunk 2077* and the *Resident Evil 4 Remake* with heavy modifications (path tracing, DLSS 4.0) pushed the workstation much harder and louder than crypto mining ever did. 
+
+This sparked the core hypothesis: **What if we recorded extreme gaming telemetry data for neuromorphic spike data conversion?**
+
+By capturing the volatile, intense hardware stress of the RE4 Remake, we established an "artificial heartbeat" for the AI. This specific gaming telemetry helped shape the early thermal equations that fed into the SAAQ (Sparse Activity-Aware Quantization) routing mechanics, proving that dynamic hardware stress is a superior baseline for biological fatigue models compared to static compute loads.
+
+## Data Structure
+The CSV contains the following raw metrics, pulled directly from the RTX 5080 and Ryzen 9950X sensors during heavy path-tracing loads:
+* `timestamp_ms`: Millisecond-precision system clock.
+* `gpu_temp_c`: GPU core temperature (Celsius).
+* `gpu_power_w`: Total GPU package power draw (Watts).
+* `cpu_tctl_c`: CPU die temperature (Celsius).
+* `cpu_package_power_w`: Total CPU package power draw (Watts).
+
+## Role in Metis SNN
+This dataset is kept as a permanent forensic record. These specific power and thermal fluctuations were used to derive the biological fatigue limits (`elapsed_us` resistance) governing the network's `tick_gpu_temporal` loops inside the `corinth-canal` Rust/CUDA engine.