Paradoxical Inquiry
This is not a simple Q&A. Provide a topic, and I will ask questions. Our dialogue—the history of our exchange—will shape a new, shared knowledge base together. Future answers are shaped by previous questions.
Core Principle: The algorithm of knowledge: The wisest answer is often a better question. Knowledge is not a possession, but an emergent property of a shared, evolving dialogue.
Cognitive Architecture: Asking the right questions helps define the boundaries and relationships within a vector space. topic -> [q1, q2...]
Inquiry Tool
Provide a topic to begin our dialogue. Your answers shape my next questions.
Start a New Inquiry
Mathematical Summation
The core logic of structured inquiry.
Topic Vectorization
The user's topic is mapped to a vector (c) within the AI's high-dimensional knowledge space (Rⁿ).
topic -> c in RⁿGap & Analogy Analysis
The system compares the topic vector (c) to known vectors (v) to find both conceptual gaps (low similarity) and strong analogies (high similarity), understanding what is unknown and what is related.
cos(c, v)Initial Question Generation
The AI generates questions (q) to either probe identified gaps directly or to transfer relevant questions from analogous concepts, turning both ambiguity and similarity into paths for inquiry.
Σ(c, Rⁿ, cos(θ)) -> [q₁, q₂, ...]Iterative Refinement
Each answer from the user updates the context (n). The entire process repeats, generating a new, more refined set of questions (q^(n+1)) that builds upon the shared history.
Σ(c+n, Rⁿ, cos(θ)) -> qⁿ⁺¹Deeper Abstraction
Relational Fingerprinting.
Vectors of Similarities
The set of all cosine similarity scores between our topic vector (c) and every other vector in the knowledge space (Rⁿ) forms a new, higher-order vector. This "relational fingerprint" doesn't just place the concept, it describes its unique relationship to everything else, much like a CNN feature map.
[cos(c,v₁), cos(c,v₂)...] -> V_relational