protocolcompany/physics-verification-corpus

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.

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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

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Data Schema

Every trajectory follows this structure:

{
  "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.

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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.

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')}")

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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

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Source

• GitHub: github.com/aijesusbro/Spectrum
• Website: aijesusbro.com
• Organization: Protocol Company

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License

MIT