Mission
“AI will be integrated into every surface area we interact with in our world. When that is the case, we should know which models we are interacting with.”
The VAIL team
Model Informatics and verifiability
Frontier-scale AI models are the most complex machine intelligences ever produced. They are already powerful and capable of things we have not yet imagined. To get the most from them, we need to understand how they interface with the world and with each other, how they work internally, and under what conditions. As we understand these models more deeply, we can build the tools to make sure we use them under the best possible conditions.
VAIL is pioneering the discipline of Model Informatics: the study of models as complex information systems. Just as bioinformatics changed how we handle genetic data, Model Informatics changes how we interact with artificial intelligence.
As we uncover properties and ground truths about AI systems, we build and distribute the tools to verify them, so that AI is used as designed.
Model Informatics
Model Informatics is a scientific discipline focused on understanding AI models as complex information systems. It develops methods to study model behavior, relationships, and properties at scale.
Treating models as information systems lets us apply rigorous methods to their internal workings, external behaviors, and relationships with other models, moving from intuition to measurable, reproducible knowledge.
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Model behavior analysis
Probing how models respond, decide, and refuse across diverse inputs and contexts: the empirical work of observing what a model actually does in deployment.
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Model relationship mapping
Frameworks for how models relate to each other through fine-tuning, knowledge distillation, and architectural similarity.
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Properties characterization
Distilling behavioral evidence into a catalog of stable, comparable properties, such as identity, lineage, capability ceilings, and dispositions, that describe what a model fundamentally is.
Verifiable AI
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Behavioral fingerprinting
Unique signatures that identify models through their runtime behavior, so a model’s identity can be verified across deployment environments.
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Property verification
Tools that confirm specific model properties and capabilities in real time, so deployed systems match expected specifications.
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Access point integration
Verification that can be deployed wherever models are accessed, from APIs to edge devices to web applications.
From our Model Informatics research, we build practical tools that verify model properties and identities in real deployments, so there is transparency and trust wherever AI systems are accessed.
These tools turn scientific understanding of models into systems that run in production. With verification at every access point, users know which model they are interacting with and can trust its properties.
Impact and vision
Our work addresses a fundamental challenge in AI deployment: how do we ensure the AI systems we interact with are what we expect them to be? As AI becomes embedded in critical infrastructure, from healthcare to finance to autonomous systems, verifying model properties becomes essential for safety, reliability, and trust.
By pioneering Model Informatics and building verification tools, VAIL works toward a future where AI deployment comes with the transparency and verification we expect from other critical systems. Organizations can deploy AI with confidence, users can trust the systems they use, and researchers can build on verifiable knowledge about model behavior.