Adaptive Anticipation
Engage in a conversation and let the AI foresee your requirements based on your interaction history.
Core Principle: The algorithm of assistance: The most effective help is not just responsive, but predictive. It answers the question you haven’t yet asked.
Cognitive Architecture: We can predict future intent by analyzing the trajectory of vectors over time. ƒ(Vn) -> Vn+1
Conversation
Your dialogue shapes the AI's understanding.
Mathematical Summation
The core logic of intent projection.
Interaction Vectorization
Each user interaction (message, action) is converted into a vector (V₁, V₂, V₃...), creating a time-series sequence representing their journey through the semantic space.
Interactions -> [V₁, V₂, V₃...]Intent Projection (Regression)
The tool applies a regression function (f) to the sequence of vectors to predict the most probable next vector (Vₙ₊₁), effectively anticipating the user's next logical need or question.
ƒ(V₁, V₂...Vₙ) -> Vₙ₊₁