Pioneering research in continual learning and safe model updates for production AI systems
Challenging conventional understanding of catastrophic forgetting in large language models
Our research demonstrates that what's commonly labeled as "catastrophic forgetting" in LLMs is often mischaracterized. Performance degradation frequently stems from inference misrouting and semantic boundary collapse—not irreversible knowledge loss.
By distinguishing between knowledge existence and knowledge accessibility, we show that many model regressions are reversible without costly retraining, fundamentally changing how organizations should approach model updates.
Transforming how AI systems learn, adapt, and evolve
Rigorous benchmark analysis across multiple model families demonstrating reproducible, reversible performance patterns.
View Data →Reframing model updates from cost centers to controlled evolution, enabling sustained value from AI investments.
View Analysis →Enabling model adaptation without irreversible knowledge loss, reducing retraining costs and deployment risk.
Learn More →Establishing criteria for safe, verifiable model updates that support longer-lived and more adaptable AI systems.
Moving beyond benchmark scores to verify that models can actually access their learned capabilities under evolving conditions.
Distinguishing between destructive weight interference, representation drift, inference misrouting, and semantic boundary collapse.
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