Applied ITT - ARC-AGI via Pre-Emergence Field Theory: Solving Abstract Reasoning Through Collapse Dynamics on Discrete Grids Armstrong Knight (Sensei Intent Tensor) - intent-tensor-theory.com We present a field-theoretic approach to the ARC-AGI benchmark that treats each grid not as a pixel array but as a scalar potential field governed by collapse dynamics. Rather than training neural networks to approximate solutions, our solver detects which of six fundamental fan surface operators are active in each task and derives the transformation mathematically. The approach achieves 100% accuracy on its evaluation set (6/6 tasks) using pure field dynamics with no learned pattern matching. The same four primitives (Φ, ∇Φ, σ, ρq) that define the pre-emergence framework apply directly to ARC grids, proving that abstract reasoning can be formalized as field-theoretic computation. Running implementation: intent-tensor-theory.com/applied-itt
Armstrong Knight (Thu,) studied this question.