Model Stability
Understanding whether learned structures remain consistent across training runs, perturbations, and changing environments.
FoundationsFundamental AI Research
Exploring the boundaries of intelligence.
Mirvum Research studies the foundations of intelligent systems: how models learn, how they remain stable, and how their uncertainty and decisions can be understood.
Why Mirvum
We focus on what remains stable when models are perturbed, shifted, retrained, or asked to explain themselves. Our goal is to develop methods that make intelligent systems more scientifically grounded, measurable, and dependable.
Research
Understanding whether learned structures remain consistent across training runs, perturbations, and changing environments.
FoundationsMeasuring when models are uncertain, where their behavior becomes fragile, and how confidence can be validated.
ReliabilityDeveloping explanations that are not only intuitive, but also stable, reproducible, and faithful to model behavior.
UnderstandingStudying how intelligent systems behave under distribution shift, noisy evidence, and real-world uncertainty.
GeneralizationPrinciples
Build explanations and methods from measurable properties, not trends or slogans.
When a learned structure persists under meaningful change, it becomes more scientifically informative.
Reliable systems should expose fragility rather than hide it behind a single confident output.
We look for ideas that generalize across models, data, domains, and generations of AI systems.
Publications
Selected papers, preprints, and technical notes will appear here.
Publication list coming soon.
Contact
We are open to research collaborations, technical conversations, and partnerships around stable and reliable intelligent systems.