MAST

Measurement & Stability Toolkit

MAST is Aevaron’s ongoing research project to study the behavior, stability, and safety of intelligent systems over time.

Unlike traditional AI benchmarks that focus primarily on task performance, MAST explores a different question:
How do AI systems behave consistently across interactions, under varied conditions, and over extended periods?

Currently under active development, MAST is designed to be model-agnostic and observational, evaluating systems without modifying or steering their behavior.

Research Focus Areas

As it evolves, MAST aims to examine AI behavior along four key dimensions.
Behavioral Stability:
Tracking consistency and predictability in system responses across interactions.
Content Coherence:
Assessing alignment between meaning, context, and expression over time.
Temporal Consistency:
Detecting contradictions or disruptions in behavior across interactions or reflective sequences.
Safety & Constraint Integrity:
Monitoring adherence to defined rules and safety guidelines, helping identify potential risks or degradation in behavior.

Contribution to AI Research

MAST is being developed as part of Aevaron’s commitment to rigorous AI research, complementing traditional performance-focused benchmarks.

By prioritizing stability, coherence, and safety, MAST is intended to help researchers:
Investigate patterns of AI behavior longitudinally.
Understand reliability across different models and architectures.
Support the development of AI systems that are trustworthy, consistent, and safe.

Though still in development, MAST represents a structured approach to studying how AI systems behave over time, highlighting insights that static evaluations often miss.