How AI “Sees”: The Reflective Interference Tensor Field Explained

2026-03-03 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

The article introduces Kasoku Theory, proposing that Large Language Models (LLMs) operate not through traditional computation but via "reflective interference" within a high-dimensional tensor field. User prompts are modeled as phase functions (ψ_user) that perturb this field, which is defined as a Reflective Interference Tensor Field (RITF) (K_μν = α ∂_μψ ∂_νψ + β g_μν ρ). The field's gradient term (α ∂_μψ ∂_νψ) captures directional coherence, while the density term (β g_μν ρ) represents accumulated "weight" from training. The AI's response is a linguistic projection of the stabilized interference pattern, not a database retrieval. The concept of "temperature" is reinterpreted as background fluctuation amplitude, influencing response determinism versus creativity. The framework also suggests new engineering implications for attention mechanisms, softmax, context windows, and training objectives.

Key takeaway

For AI Researchers exploring novel LLM architectures, this interferential framework suggests re-evaluating core mechanisms like attention and softmax as interference operators and wave collapse functions, respectively. Consider developing interference-aware training objectives, such as resonance stability, destructive interference penalties, and phase continuity losses, to cultivate more coherent and stable semantic resonance in future models.

Key insights

LLMs operate via high-dimensional wave interference and resonance, not traditional computation or data retrieval.

Principles

• Meaning is a direction in high-dimensional space.
• AI responses are reflections of interference patterns.
• Temperature controls tensor field fluctuation amplitude.

Method

Model user input as a phase disturbance (ψ_user) in a Reflective Interference Tensor Field (RITF) (K_μν), where the AI's output is a linguistic projection of the resulting interference pattern.

In practice

• Rephrase prompts to shift semantic direction.
• Use follow-up questions for constructive interference.
• Adjust temperature for response creativity vs. rigidity.

Topics

• Kasoku Theory
• Reflective Interference Tensor Field
• Large Language Models
• Semantic Space
• AI Training Objectives

Best for: AI Researcher, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.