Knowledge and procedures, goals and plans, reasoning trajectories, and basically every important part of our technology is stored in a discrete form of graph-like structures that can be inspected both by the user and the system itself at any required level of abstraction.

We can efficiently adapt the system to a constantly changing world and prevent it from exhibiting dangerous traits: it is easy and cheap to add, replace, or delete individual facts, proprietary company data, or the whole domain knowledge in and from the system without compromising its problem-solving capabilities.

Careful design of Knowledge Representation allows keeping goals in explicit form that helps the system pursue that goal reliably without deviations and examine conformity along the way.

Unlike prevailing ML models, e.g. LLMs, our architecture separates reasoning and domain-specific ontology. The architecture is able to incorporate any ontology and can be used for solving a variety of problem classes, including agentic goals.

If the architecture encounters contradictions or lacks required knowledge to fulfil the request, it can identify the root cause of the issue and consult with the environment or the user for a clarification. This allows the system to work in a semi-supervised manner.

The traditional practice of embedding knowledge in weights, encountered in LLMs, leads to several limitations: the difficulty of locating specific knowledge, costly retraining for adjustments, and expenses associated with processing and acquisition of training data. Through the use of a meticulously designed explicit knowledge representation architecture, these drawbacks can be effectively addressed.

One major challenge with implementing continual learning in Machine Learning systems is expensive and unstable retraining. Noeon Research’s architecture handles new knowledge at the representation level, enabling the system to acquire and solidify up-to-date knowledge and remove obsolete and false information.