Multi-Objective Drift Detection and Pause-Gate Composition

Extend the Kitchen Loop’s drift detection and pause-gate mechanisms to simultaneously monitor non-functional requirements such as latency, security, and fairness without human intervention, and derive a principled method to compose multiple objective functions so that no single objective dominates the pause-gate signal.

Background

Current Kitchen Loop drift metrics primarily track functional correctness (e.g., oracle pass rates, test pass rates), which has proven effective in the validated deployments. However, real systems also require continuous monitoring of non-functional attributes like performance, security, and fairness.

A generalized drift controller must integrate multiple objectives and adjudicate conflicts without human oversight, ensuring that improvement on one axis (e.g., throughput) does not mask degradation on another (e.g., security).

References

OP3: Multi-Objective Drift. Current drift metrics focus on functional correctness. Extending the framework to simultaneously monitor non-functional requirements (latency, security, fairness) without human intervention remains open. Our drift detection (Section 5.5) would need to compose multiple objective functions without one dominating the pause-gate signal.

— The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase  (2603.25697 - Roy, 26 Mar 2026) in Subsection "Open Problems" (Production Safety Record)

PACE must determine whether the current conditions remain close enough to the characterized region for reliable interpolation or whether the operating map no longer supports a safe decision, which requires change detection and calibrated uncertainty across radio, compute, sensing, and process variables.

— Process-Aware Cross-Layer Adaptation for O-RAN-Enabled Industrial Systems  (2608.13372 - Delavari et al., 13 Aug 2026) in Section VI, subsection “Safe Adaptation to Changing Conditions”

More generally, is it possible to build a model for the mixed desiderata reflecting both quantitative and qualitative soundness, without human intervention, such as our effort of enforcing constraints?

— A physics-constrained machine-learning sub-grid-scale modeling approach for turbulent premixed flames  (2608.28525 - Suh et al., 28 Aug 2026) in Section 5, Conclusion (final section)

The open research questions, however, go deeper: How can the trade-off between quality dimensions be characterized at the repository and system levels? What feedback mechanisms are needed to detect when local improvements degrade global properties? How should the search for possible refactoring actions be organized so that system-level optimization remains manageable?

— Continuous Autonomous Refactoring: A Research Roadmap for AI-Driven Code Quality Maintenance  (2609.01236 - Sun et al., 1 Sep 2026) in Section 3.1, The Multi-objective Optimization Problem

The open research questions include: How should autonomous refactoring be positioned within continuous delivery pipelines? How can the feedback loops inherent in continuous delivery be leveraged to improve the refactoring system itself?

— Continuous Autonomous Refactoring: A Research Roadmap for AI-Driven Code Quality Maintenance  (2609.01236 - Sun et al., 1 Sep 2026) in Section 3.6, Cross-Cutting and Practical Considerations, Integration into Continuous Delivery Pipelines