From AI to IA.
Intelligence Alignment for a changing world.
OpenMIA builds continual-learning infrastructure that transforms validated real-world experience into durable model capability—so deployed models and agents can remain aligned with changing knowledge, tasks, users, and environments.
AI creates intelligence. IA enables it to keep developing.
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Intelligence must remain aligned with reality.
Artificial intelligence is typically aligned before deployment: through training, instruction tuning, preference optimization, and evaluation. But alignment is not a one-time event. Once deployed, a model enters a world in which knowledge accumulates, rules change, users develop new needs, and operating environments continue to evolve.
We call the ongoing process of keeping deployed intelligence aligned with these changes Intelligence Alignment, or IA.
IA is not simply retrieving newer information or repeatedly retraining an entire model. It is the continual process of observing real-world interaction, identifying valuable experience, validating what should be retained, integrating it into the appropriate memory layer, and evaluating the resulting behavior.
OpenMIA provides the infrastructure for this process.
Here, IA is an operational frame for post-deployment learning. It complements rather than replaces work on value or safety alignment.
Alignment between intelligence and the world in which it operates.
OpenMIA considers four practical dimensions:
- Knowledge alignment
- Keeping pace with facts and knowledge as they change.
- Task alignment
- Adapting to new tasks, workflows, and evaluation criteria.
- User alignment
- Understanding needs and preferences that develop over time.
- Environment alignment
- Adapting to changing tools, rules, and operating environments.
From experience to model capability.
OpenMIA connects interaction data, validated experience, and persistent model capability through two coordinated layers.
Data Infra
Data Infra collects traces, feedback, outcomes, successful strategies, and potential errors from interactions among people, AI systems, and their environments. It examines source, quality, recurrence, and long-term value to identify experience worth retaining.
Model Infra
Model Infra determines how validated experience should persist. It connects selected knowledge to the model's execution path, then evaluates the resulting behavior for accuracy, generalization, locality, and stability.
Real-world interaction produces experience. Data Infra interprets it. Model Infra turns validated experience into durable capability.
Memory beyond retrieval.
OpenMIA uses model-integrated memory to describe persistent knowledge that influences behavior from within the model's execution path. It complements, rather than replaces, retrieval systems and other forms of external memory.
External memory
External memory is well suited to information that changes quickly, has a long tail, or must remain directly traceable. Relevant material is retrieved and added to context when a task is performed.
Model-integrated memory
Model-integrated memory is intended for stable, important, recurrent, and validated experience. It allows that experience to influence later generation, judgment, and reasoning without injecting the same material into every context.
Together, these systems form a hybrid memory architecture: external stores preserve timely and inspectable information, while model-integrated memory develops durable capability.
Intelligence shaped by experience.
Continual learning is especially consequential at the edge, where devices and agents encounter distinct users, tasks, and environments. As local compute becomes more capable, these systems may be able to learn from experience while respecting constraints on privacy, resources, evaluation, and recovery.
OpenMIA studies how models and agents can continue learning after deployment.
OpenMIA builds continual-learning infrastructure for edge intelligence to learn from its environment.