Research

How can adaptive AI support repeatable tax preparation and personalized tutoring whose correctness and learning outcomes can be evaluated?

CLM learns to match a situation with possible actions. It encodes the state and candidates, applies separate learned projection heads, and scores how well they align. Explore the calculation with visible example scores, then see what the published agent-solution selection benchmark measures.

This survey examines code as the environment around an agent: interfaces for action, mechanisms for memory and feedback, and ways to coordinate multiple agents. A model’s proposed next step becomes executable work whose artifacts and results can guide what happens next.

The Transformer is a sequence-to-sequence architecture built around attention. At each layer, a token can gather information from other positions by comparing a query with their keys, then mixing their values. Multiple heads, position information and feed-forward layers make this more than a single lookup.

Jev’s public interface lets you ask named, typed questions about a state. Choice returns a category with probabilities and confidence; Noul returns a scalar. Your application can validate those answers and compose them with ordinary code into a decision.