[R] Why AI Self-Assessment Actually Works: Measuring Knowledge, Not Experience
TL;DR: We collected 87,871 observations showing AI epistemic self-assessment produces consistent, calibratable measurements. No consciousness claims required.
The Conflation Problem
When people hear “AI assesses its uncertainty,” they assume it requires consciousness or introspection. It doesn’t.
| Functional Measurement | Phenomenological Introspection |
|---|---|
| “Rate your knowledge 0-1” | “Are you aware of your states?” |
| Evaluating context window | Accessing inner experience |
| Thermometer measuring temp | Thermometer feeling hot |
A thermometer doesn’t need to feel hot. An LLM evaluating knowledge state is doing the same thing – measuring information density, coherence, domain coverage. Properties of the context window, not reports about inner life.
The Evidence: 87,871 Observations
852 sessions, 308 clean learning pairs:
- 91.3% showed knowledge improvement
- Mean KNOW delta: +0.172 (0.685 → 0.857)
- Calibration variance drops 62× as evidence accumulates
| Evidence Level | Variance | Reduction |
|---|---|---|
| Low (5) | 0.0366 | baseline |
| High (175+) | 0.0006 | 62× tighter |
That’s Bayesian convergence. More data → tighter calibration → reliable measurements.
For the Skeptics
Don’t trust self-report. Trust the protocol:
- Consistent across similar contexts? ✓
- Correlates with outcomes? ✓
- Systematic biases correctable? ✓
- Improves with data? ✓ (62× variance reduction)
The question isn’t “does AI truly know what it knows?” It’s “are measurements consistent, correctable, and useful?” That’s empirically testable. We tested it.
Paper + dataset: Empirica: Epistemic Self-Assessment for AI Systems
Code: github.com/Nubaeon/empirica
Independent researcher here. If anyone has arXiv endorsement for cs.AI and is willing to help, I’d appreciate it. The endorsement system is… gatekeepy.
submitted by /u/entheosoul
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