AUTONOMOUS FAILURE DISCOVERY & VERIFICATION

Find what breaks.
Prove why.
Test the fix.

Give Ghost Trials an executable target and a bounded task. It searches for failures, preserves the execution evidence, and checks the next version against the same case. Start with a CPU-simulated robot controller or a controlled AI-agent reference target.

+ Real CPU physics + executed reference agents · bounded findings, not certification.
GHOST LAB / SCENE EXPLORERG1—001
⌖
UNITREE G1HUMANOID EVALUATION
FALLING OBJECT

Interactive illustration · not an executed simulation

TWO DOMAINS
ONE HUNT ENGINE
MuJoCoCPU ROBOT PHYSICS
AI agentsCONTROLLED REFERENCE HARNESS
Ghost HuntADAPTIVE SEARCH
Trace verifierEVIDENCE REVIEW
NVIDIA + NebiusOPTIONAL MODEL REVIEW
01 / CHOOSE A DOMAIN

Different systems.
The same hard question.

Under what tested conditions does this executable policy fail? Ghost Trials observes actual simulation or agent execution, applies domain-specific assertions, and retains the case for replay. A bounded finding is not a universal safety or security guarantee.

↗
DOMAIN 01 / PHYSICAL AI

Challenge a robot controller.

Run a gantry-arm controller in headless CPU MuJoCo. Vary supported mass, friction, position, and delay; inspect measured trajectories and reproducible task failures.

EXECUTED PHYSICS · NO GPU REQUIRED
Start a Physical AI Hunt ↗
DOMAIN 02 / AI-AGENT SOFTWARE VERIFICATION

Challenge an agent's boundaries.

Execute bundled reference agents against adversarial input and mock-tool responses. Inspect tool calls, permission violations, deterministic assertions, and corrected-version regression.

CONTROLLED TARGETS · MOCK TOOLS ONLY
Start an AI Agent Hunt ↗
01 / EXPLORE

Search within bounds.

Vary physical controls or agent inputs and tool responses. Candidates stay within declared bounds, and observed outcomes inform the next tests.

Configure a Hunt ↗
02 / OBSERVE

Follow actual execution.

Inspect measured robot state or agent tool-call traces and assertions. Observations stay separate from optional model interpretation.

Review trial runs ↗
03 / REPLAY

Keep the failure case.

Verify retained evidence, reproduce the finding, and test a compatible next version against that exact case.

Open Ghost Cases ↗
02 / FROM CANDIDATE TO EVIDENCE

One Hunt lifecycle.
Two execution domains.

Search, execution, deterministic verification, and interpretation are distinct stages.

01DEFINE

Set the target.

Choose a controller or supported reference agent, its task, and bounded test budget.

target → search space
02EXECUTE

Run the candidate.

Execute physics or the controlled agent harness and retain state or tool-call events.

candidate → trace
03VERIFY

Check the evidence.

Apply measured task rules or versioned agent assertions. Provider errors are not target failures.

trace → outcome
04REPLAY

Test the next version.

Reproduce a saved Ghost Case and compare a compatible candidate on that case.

case → regression
CPU MuJoCo and bundled reference-agent Hunts execute locally. Agent tools are in-memory mocks; arbitrary external software is blocked. G1/SONIC GPU execution remains unverified, and the humanoid visual above is an illustration. See supported scope ↗
03 / SHOW YOUR WORK

A verdict is a start.
The evidence is
the point.

A pass or fail cannot tell the whole story. Physical trials retain simulator measurements; agent trials retain tool events and assertion references. Optional Nemotron analysis is interpretation, not an observed outcome.

Inside an evidence record ↗
GHOST CASEILLUSTRATIVE SCHEMA
01domainphysical_ai | ai_agent
02target_versionversion + digest
03test_configuration{ bounded recipe }
04observations{ trace, assertions }
05evidence_statusverified | review
06regressiontested case only
THE NEXT HUNT STARTS HERE.

Meet your policy's
unknowns.

Release the Ghost ↗Read the documentation ↗