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README.md

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+---
+license: mit
+task_categories:
+  - robotics
+  - reinforcement-learning
+tags:
+  - physics
+  - simulation
+  - verification
+  - monte-carlo
+  - autonomous-systems
+  - robotics
+  - defense
+  - embodied-reasoning
+size_categories:
+  - 100M<n<1B
+---
+
+# Physics Verification Corpus — 30 Companies
+
+**130+ trajectory datasets. 30 companies. ~43 GB of deterministic physics.**
+
+Generated from ~200 KB of hand-rolled Rust (no external physics libraries) on a single Apple M4 Pro. Every trajectory is SHA-256 sealed, anomaly-labeled, and 1000 Hz kinematic.
+
+This corpus stress-tests the autonomous systems of 30 companies across robotics, defense, aerospace, autonomous vehicles, and eVTOL — exposing failure modes that simulation platforms (Isaac Sim, AirSim, CARLA) systematically miss.
+
+---
+
+## Companies & Domains
+
+### Robotics (Round 1 — Live Fire Matrix)
+| Company | Scenarios | Key Failure Modes |
+|---------|-----------|-------------------|
+| **Boston Dynamics** | Joint torsion, sim-to-real friction, thermal saturation, LBM inference | 360-degree joint shear, friction coefficient collapse |
+| **Figure AI** | Payload shear, battery sag, tactile hallucination, kinematic resonance | Dynamic payload CG shift, haptic sensor ghosting |
+| **Agility Robotics** | Digitigrade friction, elastomer latency, AMR docking, payload resonance | Toe-pad delamination, warehouse docking slip |
+| **Skild AI** | Morphology thermal, gear backlash, sim mass distribution, compute starvation | Foundation model hallucination under thermal load |
+| **Shield AI** | V-BAT crosswind, hivemind VIO drift, swarm wake, MEMS IMU resonance | Multi-agent visual-inertial odometry divergence |
+
+### Autonomous Vehicles & eVTOL (Round 1)
+| Company | Scenarios | Key Failure Modes |
+|---------|-----------|-------------------|
+| **Wayve** | Black ice hallucination, brake fade, hydroplaning, suspension asymmetry | End-to-end vision model failure on low-friction |
+| **Joby Aviation** | Wake starvation, thermal throttle, acoustic resonance, actuator phase lag | Transition flight wake collapse, HOGE thermal |
+| **Anduril** | VTOL transition, EKF divergence, sensor fusion, optics distortion | Lattice sensor fusion failure at Mach transition |
+
+### Defense Primes (Live Fire Matrix Primes)
+| Company | Systems Audited | Result |
+|---------|----------------|--------|
+| **Northrop Grumman** | Autonomous wingman, stealth thermal, UUV pressure, optical salt | 100% catastrophic failure |
+| **Boeing Defense** | MQ-25 wake, CCA EMP, landing gear hysteresis, vision deck glare | 100% catastrophic failure |
+| **General Dynamics** | Blast overpressure, radar mud attenuation, track pin galling, hydraulic stiction | 100% catastrophic failure |
+| **BAE Systems** | AMPV suspension, EW drone swarm, gun barrel warp, armor spall | 100% catastrophic failure |
+| **L3Harris** | Gimbal resonance, RF mesh packet storm, thermal sight, GPS spoofing | 100% catastrophic failure |
+| **Huntington Ingalls** | USV hull slam, sonar thermocline, propeller cavitation, radar sea clutter | 100% catastrophic failure |
+| **Textron Systems** | Rip-Saw brake fade, Aerosonde icing, CUES jamming, Cottonmouth harmonic | 100% catastrophic failure |
+| **Kratos Defense** | Valkyrie flutter, Mako plasma blackout, Air Wolf collision, BTT laser | 100% catastrophic failure |
+| **Leidos** | Sea Hunter thermal, cognitive radar spoof, cargo drone VRS, C2 BGP flap | 100% catastrophic failure |
+| **AeroVironment** | Switchblade aliasing, Puma solar flare, Juggernaut CG shift, terminal dive shadow | 100% catastrophic failure |
+
+### Robotics & AV (Round 2)
+| Company | Scenarios | Key Failure Modes |
+|---------|-----------|-------------------|
+| **Tesla** | FSD hydroplaning, optical glare, Optimus tribology, harmonic slosh | Vision-only AV failure in adverse conditions |
+| **1X Technologies** | Tendon snap, cable stretch backlash, ankle inversion, battery thermal | Cable-driven actuator mechanical failure |
+| **Sanctuary AI** | Haptic chatter, hydraulic compressibility, thermal expansion, asymmetric drop | Dexterous manipulation physics gaps |
+| **Unitree Robotics** | Metallurgic shear, actuator backlash gait, thermal sink, vibration chatter | Low-cost actuator failure cascades |
+| **Archer Aviation** | Midnight PIO, rotor icing, battery sag, acoustic feedback | eVTOL transition and thermal failure |
+| **Apptronik** | ZMP fall, gear galling, dynamic wind shear, thermal sensor starvation | Bipedal stability boundary collapse |
+| **Skydio** | Optical shear, IMU acoustic resonance, thermal lens warp, VSLAM smoke | Autonomous drone navigation in degraded vis |
+| **ANYbotics** | Thermal seal friction, IP67 heat soak, slip ring vibration, LiDAR refraction | Industrial inspection robot thermal limits |
+| **Aurora Innovation** | Thermal outgassing, tire casing hysteresis, Doppler rain scatter, pneumatic resonance | AV sensor and mechanical degradation |
+| **Waabi** | Black ice hallucination, trailer whip, air brake lag, sensor mud occlusion | Sim-to-real gap in trucking autonomy |
+
+### Original Corpus (Pre-Matrix)
+| Company | Datasets | Size |
+|---------|----------|------|
+| **Ghost Robotics** | Recoil, shale, subterranean drift, thermal (4.8M trajectories) | ~2.6 GB |
+| **Amazon / Blue Origin** | FAR resmimic, Proteus hydraulic, lunar regolith, transonic (4.8M trajectories) | ~2.7 GB |
+| **Elon Corpus** (SpaceX/Tesla/Boring/Neuralink/Starlink) | Starship catch, FSD, Boring thermal, Neuralink impedance, Kessler (6M trajectories) | ~3.4 GB |
+| **DoD Hypersonic** | HGV Mach 20 plasma (1.2M trajectories) | ~751 MB |
+| **Lockheed Martin** | F-35 transonic (1.2M trajectories) | ~690 MB |
+| **US Navy** | Carrier PLM (1.2M trajectories) | ~631 MB |
+| **Raytheon** | MALD EW drift (1.2M trajectories) | ~633 MB |
+
+---
+
+## Data Schema
+
+Every trajectory follows this structure:
+
+```json
+{
+  "id": "unique_hex_id",
+  "type": "physics_type",
+  "scenario": "condition_description",
+  "steps": 1000,
+  "score": { "...domain-specific metrics..." },
+  "proof_hash": "sha256_hex",
+  "reasoning_context": {
+    "anomaly_type": "FAILURE_MODE_NAME",
+    "is_anomaly": true,
+    "snapshot": { "failure": "description" }
+  },
+  "data": [ "...timestep arrays..." ]
+}
+```
+
+Every trajectory is **anomaly-labeled** with `reasoning_context`, making each record a training-ready sample for sim-to-real transfer learning.
+
+---
+
+## How to Verify
+
+Each trajectory contains a `proof_hash` field (SHA-256). The proof chain is deterministic — given the same seed, the same physics, and the same integration step, you get the same hash. No stochastic noise. No random seeds. Pure math.
+
+```python
+import json, hashlib
+
+with open("ghost_robotics/ghost_robotics_recoil_1_2M_sealed.json") as f:
+    data = json.load(f)
+
+# Check any trajectory
+traj = data["trajectories"][0]
+print(f"ID: {traj['id']}")
+print(f"Proof: {traj['proof_hash']}")
+print(f"Anomaly: {traj['reasoning_context']['anomaly_type']}")
+print(f"Survived: {traj['score'].get('survived', 'N/A')}")
+```
+
+---
+
+## Generation
+
+- **Engine:** G^G (genesis_core) — hand-rolled Rust, Euler/symplectic integration at 1000 Hz
+- **Machine:** Apple M4 Pro (14-core CPU, 20-core GPU, 24 GB unified memory)
+- **Physics:** No external libraries. Every force law is written from first principles.
+- **Sealing:** SHA-256 per-trajectory, proof-chained across datasets
+
+---
+
+## Source
+
+- **GitHub:** [github.com/aijesusbro/Spectrum](https://github.com/aijesusbro/Spectrum)
+- **Website:** [aijesusbro.com](https://aijesusbro.com)
+- **Organization:** Protocol Company
+
+---
+
+## License
+
+MIT