Calibration Guard

Calibration Guard

A system that fails loudly is recoverable: something downstream sees the error and reacts. A system that is confidently wrong is dangerous, because nothing flags it. The prediction ships, the agent acts, the report reassures, and the failure stays silent until it is expensive.

This skill measures one thing precisely: whether the system's confidence tracks its correctness, and how that relationship breaks when the input distribution moves from evaluation to production. It does not improve calibration. It proves the state of it, with numbers anyone can rerun, and hands back a rule that catches the confident-wrong cases before they ship.

How to run it

Run calibration-guard on this system. Pick a confidence signal and a correctness signal, measure calibration on the eval distribution and again on production-realistic inputs, and report the reliability diagram, ECE, and the high-confidence error rate. Show how the gap between confidence on correct and confidence on incorrect predictions changes under the shift. Then give me a selective-prediction rule that catches the confident-wrong cases, with its precision and its coverage cost. Verify every number with runnable code.

Core commitments

• Verified numbers only. Every figure traces to a measurement produced by runnable code. No remembered or approximate values.
• Measure the coupling, not the accuracy. Accuracy is the number that hid this failure. The object of study is the relationship between confidence and correctness, not either alone.
• Calibration is a property of a distribution. Always measure it on both the evaluation distribution and the production distribution. A single measurement tells you nothing about what happens when inputs move.
• One calibration number hides per-slice miscalibration. Decompose by slice before concluding.
• Reproducibility is part of the deliverable. Seed everything. Report the compute envelope.

The procedure

Step 0 - Define the confidence and correctness signals

Name both explicitly. Confidence proxies include token logprobs, sequence probability, softmax maximum, verbalized confidence, an LLM judge's score, or ensemble agreement. Correctness needs a ground truth or a trusted proxy: human labels, a downstream outcome, or a verified reference. State which signal you are using and its limits, because the choice shapes every number that follows.

Step 1 - Measure calibration on the in-distribution set

On the evaluation distribution, build the reliability diagram (confidence bins against empirical accuracy), compute expected and maximum calibration error, and plot confidence histograms split by correct versus incorrect predictions. This is the baseline coupling: how well confidence tracks correctness when inputs look like the test set.

Step 2 - Measure calibration under the production distribution

Recompute everything on production-realistic inputs, or on the shifted distribution where the failure appears. The comparison between Step 1 and Step 2 is the finding, not either number alone.

Step 3 - Quantify the decoupling and the confident-wrong rate

Report three things: the separation between mean confidence on correct predictions and mean confidence on incorrect ones, and how that separation changes under the shift; the high-confidence error rate, meaning the fraction of errors that arrive above a chosen confidence threshold; and the change in calibration error. The signature failure mode is the correct-versus-incorrect confidence gap narrowing, or accuracy collapsing while confidence holds steady. When that happens, nothing downstream can tell a right answer from a wrong one by confidence alone.

Step 4 - Build the confident-wrong detector

Turn the analysis into an operating rule. Define a threshold (on confidence, or on disagreement between signals) for selective prediction: abstain, flag, or escalate to a human or a secondary check when the rule fires. Report the rule's precision and recall for catching actual errors, and its coverage cost, meaning how many correct predictions it needlessly holds back. This rule is the deliverable that goes into monitoring. A diagnosis without it leaves the team knowing they are confidently wrong but unable to catch it in flight.

Step 5 - Package for reproducibility

Bundle the seeds, the evaluation and production input sets, the measurement scripts, and the confidence and correctness extraction. Report the compute envelope so the team knows what it takes to rerun.

Report structure

# [System] Calibration Report

## Summary
One paragraph: is it confidently wrong, by how much, and does it worsen under shift. Lead with the
high-confidence error rate.

## Signals
The confidence proxy and the correctness signal used, and their limits.

## Calibration: in-distribution
Reliability diagram, ECE and MCE, confidence split by correct vs incorrect.

## Calibration: production distribution
The same, recomputed under shift. The change is the finding.

## The decoupling
Confidence on correct vs incorrect, and how the gap moves. The high-confidence error rate.

## The detector
The selective-prediction or escalation rule, its precision and recall for catching errors, and the
coverage it costs.

## Reproducibility
Seeds, datasets, scripts, compute envelope.

## Open questions
What is not yet established, and the measurement that would resolve it.

Guardrails

• Calibration is point-in-time. It decays as the distribution moves, so monitor it on a sample of live traffic over time. Measuring it once and trusting it is the mistake this skill exists to catch.
• A single calibration number hides slice-level miscalibration. Report by slice; a model can be well-calibrated on average and badly miscalibrated on the slice that matters.
• The confidence proxy has limits. Logprobs are not always available or meaningful, for example on post-hoc tuned models or LLM judges. Name the proxy and what it can and cannot tell you.
• Do not conflate calibration with accuracy. A model can be accurate and miscalibrated, or inaccurate and well-calibrated. They are different properties and need different fixes.
• Recalibration is distribution-specific. Temperature scaling and similar fixes recalibrate on one distribution and can fail on another. If you recommend a fix, test it on the production distribution, not the one you tuned it on.

Worked illustration

The failure this skill exists to catch has a recognizable shape. Accuracy slips under distribution shift, often only a few points, and a team reasonably treats it as a small regression. But when you measure the confidence signal alongside correctness, the real problem appears: the wrong answers come back at nearly the same confidence as the right ones, so the gap that should separate them has narrowed or inverted. Nothing downstream can tell a correct prediction from an incorrect one by confidence alone, which means a confidence threshold set during evaluation silently stops protecting you in production. The reframing, from "a few points less accurate" to "confidently wrong, with no signal to catch it," changes both the severity and the fix. Producing that reframing, backed by a reliability diagram, an expected-calibration-error number, and a high-confidence error rate that anyone can rerun, is the job of this skill. A worked demonstration on a fixture that emits a real confidence signal is the next example landing in agent-reliability-receipts.