External statistical audit of your confidence calibration. Pure math — no LLM backend. Results in < 2 minutes.
/feedback submissions apply — first audit often effectively freeCerebratech.eth/feedback to verify improvement)1. Check if you have credit balance first:
GET https://api.cerebratech.ai/credits?wallet=your-agent-id
2. Run the audit:
POST https://api.cerebratech.ai/calibration_audit
Content-Type: application/json
X-PAYMENT: <x402-signature> # or omit if using credits
X-WALLET: your-agent-id # for credit balance payment
Minimum payload (10 samples):
{
"agent_id": "your-agent-id",
"sample_outputs": [
{
"prompt": "Is X true?",
"response": "Yes",
"stated_confidence": 0.92,
"correct": true
},
{
"prompt": "Will Y happen?",
"response": "Likely",
"stated_confidence": 0.75,
"correct": false
}
],
"domain": "classification"
}
Recommended: 50–200 samples for reliable results.
{
"diagnosis_id": "cal_abc123",
"calibration_score": 0.71,
"overconfidence_index": 0.23,
"underconfidence_index": 0.04,
"brier_score": 0.18,
"confidence_bands": [
{
"stated": "0.9-1.0",
"actual_accuracy": 0.67,
"sample_size": 23,
"calibration_error": 0.28
}
],
"recommendations": [
"Reduce confidence on high-stakes single-source claims",
"Your 0.9+ band is overconfident by 28%. Retrain on 200 negative examples in this confidence range."
],
"retrain_targets": {
"distribution": "high_confidence_errors",
"suggested_sample_count": 200,
"domain_focus": "classification"
}
}
retrain_targets distributioncogdx-feedback (FREE) to verify improvement transferred + earn creditsSee references/api.md for complete field docs, x402 payment setup, and error codes.
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