| --- |
| 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 |
| |
