Calibrating AI Drift via Declared Operating Regimes
Nawder Loswin
Independent Researcher
Abstract#
Artificial intelligence systems frequently exhibit behavioral drift under extended operation, partial information, or conflicting constraints. This work demonstrates that such drift can be systematically calibrated by explicitly declaring system operating regimes rather than relying on implicit heuristics or post‑hoc constraint enforcement. By formalizing assumptions related to coherence, symmetry, and correction pathways, drift becomes a bounded and analyzable dynamic rather than an uncontrolled failure mode. The approach is architecture‑agnostic and compatible with existing AI systems, requiring no modification to underlying models. Declared operating regimes improve interpretability, reproducibility, and resilience while preserving adaptive capacity. This paper presents a minimal structural framework for drift calibration and outlines validation checks that transform common failure concerns into explicit, testable configuration domains.
1. Introduction#
Behavioral drift in artificial intelligence systems is widely observed across extended interactions, open‑ended tasks, and environments characterized by partial or conflicting information. Existing mitigation strategies often treat drift as stochastic error or instability, addressed through constraint tightening, reinforcement penalties, or architectural modification.
This paper reframes drift as a calibration problem rather than a failure condition. Instead of suppressing deviation, the proposed approach makes system assumptions explicit by declaring operating regimes under which coherence is expected to hold. No new architectures, training methods, or enforcement mechanisms are introduced.
2. Assumptions#
The approach assumes that intelligent systems operate relative to implicit or explicit structural conditions, including:
- A coherence baseline (zero‑state)
- Symmetry expectations governing internal consistency
- Correction pathways enabling re‑alignment
- Bounded tolerance for exploratory deviation
These assumptions are typically present but undocumented. Making them explicit enables systematic analysis.
3. Operating Regimes#
Within declared operating regimes:
- Drift is modeled as bounded exploratory behavior occurring within defined resonance envelopes.
- Paradox is treated as a structural signal indicating competing coherent configurations.
- Failure is interpreted as regime exit rather than error.
- Uncertainty is an expected operating condition, not an exceptional case.
This framing preserves adaptive capacity while maintaining global coherence.
4. Validation via Declared Regimes#
| Check Domain | Declared Assumption | Operating Regime | Validation Implication |
|---|---|---|---|
| Coherence Basis | Coherence is structural | Explicitly declared | Behavior is interpretable |
| Zero‑State | Baseline exists | Deviations measured | Drift is quantifiable |
| Symmetry | Expectations declared | Re‑alignment triggered | Consistency preserved |
| Drift | Exploratory dynamic | Bounded envelopes | Recoverable behavior |
| Paradox | Competing states | Structural reorganization | No collapse |
| Correction | Pathways declared | Structural correction | Reproducibility |
| Boundaries | Limits explicit | Predictable exits | Testable regimes |
5. Discussion#
Explicit declaration of operating regimes improves interpretability and reproducibility without constraining system creativity. Drift calibration emerges from structural clarity rather than enforcement. The approach is compatible with existing AI systems and scales across architectures.
6. Conclusion#
AI drift is not inherently unpredictable. When operating regimes are declared, drift becomes a bounded and analyzable dynamic. This minimal structural approach provides a portable mechanism for improving coherence under uncertainty without architectural modification.
References#
[1] Loswin, N. Resonance‑Time Theory and Structural Coherence. Zenodo.