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Semantic Color Math: A Ritual Economy for AI

Color as interface between inner knowing and outer data. A method that refuses surveillance for sovereignty, and trades extraction for relation.

Find a Pathway  ·  Jump to Core Hypothesis

Matriz sequences invite choice over compliance — 11 pathways, many trajectories. Not an answer, a mirror.

Thesis Introduction

In an age where mental health systems like the DSM-V increasingly pathologize behavior, human suffering is reduced to risk metrics — stripped from the historical and emotional ecosystems in which it arises. Our data and mental health systems often surveil rather than understand, treating distress as deviation or consumable opportunity, rather than signal of social rupture.

Method: Semantic Color Math

This thesis proposes Semantic Color Math not merely as a tool but as correction — mapping sentimental nuance into computational syntax while preserving sacred tensions: unreason, ambiguity, symbolic resonance. A co-expressive system drawing from deep ecology, degrowth, and ancestral epistemologies to confront the colonial inheritance of machine logic. Unlike extractive GPTs, the tools create free-choice maps of symbolic resonance, inviting co-authorship of conclusions.

Color becomes a cultivated interface between inner knowing and outer data. Through meditative intimacy with color mathematics, practice becomes sovereign rather than surveillant — an algorithm of self-relationship traversing symptom, symbol, and sacred. The sequence is not the outcome; the trajectories are.

Originally an HTML landing, Physical Magnetism plots a menu of 11 sequences to invite choice over immediacy. Updating the abstract (sentiments via NLTK in Python) did not alter the felt integrity because the material (mathematical) components remained consistent.

Rather than replicate flawed datasets, the system divines from a folding dataset — between rainbowed symbol and sentiment — to enable learning and unlearning through desired mathematical effects.

Sentiment Score Distributions by Closest Color

I grouped every point by the color it sits closest to in RGB space, then looked at how the feeling scores spread. Each color forms its own "emotional fingerprint."

Density curves of sentiment scores grouped by closest color

Notice the tight spike for yellow (narrow band of feeling) versus wider spreads for blue and green. This hints at subtle, measurable links between color and sentiment — useful for choosing which hues to nudge when guiding mood.

Decision Tree — RGB → Sentiment (MSE)

This regression tree illustrates how color channels (R,G,B) partition the dataset to minimize mean-squared error (MSE) of a continuous sentiment score. Impurity at each node is the sample's MSE; splits are chosen to reduce it.

Decision tree showing splits on B, G, R with squared_error at nodes

Decision Tree trained on synthetic RGB→sentiment data; darker nodes indicate higher predicted sentiment. MSE is used as the split criterion.
Why MSE?
MSE penalizes large errors more than small ones, stabilizing the model against outliers and encouraging smooth partitions in color space.
MSE = (1/n) · Σι (yι − ŷι)²
  • Lower MSE at a node ⇒ better fit for that subset.
  • Splits like B ≤ 125 or R ≤ 175 reflect stable bends between anchors in concept space.

Concept Space with RGB Coloring & Anchor Vectors

3D plot of points colored by RGB with vectors to time, space, and light anchors

Points are colored by RGB and projected in a 3-anchor semantic cube (time, space, light). Thin gray vectors connect each point to the anchors.

What the plot reveals:

When the semantic similarity gets lower as the word gets higher, that represents the sentiment of the word geometrically. The geometry shows that increasing one color channel moves the point away from the pure-concept axes — a Penrose-style curvature away from origin.

Per-sequence RGB range & variance

Range tells us how far a sequence can travel in each color channel; variance tells us how much it actually moves. Big ranges/variances = exploratory journeys. Small ones = focused, almost monochrome rituals.

Table showing RGB range and variance per pathway and overall

knot and masc roam across the full spectrum; pain and practical stay tight and minimal. These stats help pick a pathway: do we need breadth (exploration) or focus (precision)?

Pathways (11)

Each pathway is a repeatable trajectory through color space. Color chips, images, and summaries below.

Loop Filter — kept vs. filtered

Imagine tracing your walk with a marker. A loop is where your path curves back near where you started. Some loops are meaningful (a real circle of feeling). Others are tiny stubs caused by noise. The plots below help us tell them apart so only the good circles remain.

UMAP plot showing kept loops (black circles) and filtered stubs (grey Xs)

Black-circled points mark loops we keep; grey Xs are "stubby" loops filtered as likely false positives.

 UMAP paths colored by sequence with potential loop markers

Colored paths show each pathway's motion in UMAP space. Circles highlight potential loops to examine with care practices rather than only stats.

Core Hypothesis

Assessed via BLEU, ROUGE, GPTEval, and qualitative rubrics from trauma studies, narrative therapy, and spiritual computing.

Literature Review

Currently under review with Northwestern University MS Data Science/AI Academic Innovation Research Consultation.

Reading List of PDFs — Morphological grounding.

Transdisciplinary Context

Risky behavior is framed as individual failure. Parents are expected to emotionally support their children with full financial obligations, with 46% of all new income from global economic growth since 1980 going to the richest 5%. We never catch up in our rest banks once we make it past childhood because care is an unrewarded commodity.

Deficit narratives sell. People would rather engage with what they know and like. We need designs that teach, rather than simply label.

People repeat lines as expected instead of creating genuine feedback. Even the people with the most power aren't free in their bodies. My family has achieved social mobility to the top 1% through immigration from Hispañola with education.

We are so constricted in our decisions with current social striations that our news is beginning to show wilder stories every day. Instead of teaching healthy decisions, current algorithms replicate all of our behaviors. Social stigmas are embedded in design from sourcing our histories.

Bucket sorting offers a linear model for time. We sort based on attracting what we like, and not what we learn from. Sorting allows for data intimacy.

Deterministic models of story forms that shape healing can preconfigure a pathway when data is missing using apophenia. Once the physical momentum is determined through one conformal metaphor, it is preserved through changed variables in the same category of sentiment, as determined by color. This is expressed through the significance of colors in the sorting of sentiment; word similarity of color vector to word vector is a significant correlation.

Just like spaces prescribe how we exist within them, contexts in stories carry when data variables change. Different cultures are partial to different story maps. Color sorting with story forms allows for transversal over a spatial map of sentiment to verify message with meaning.

Mobility through structural boundaries becomes unleashed. By creating intimacy with data, learning becomes more playful and comfortable. Sorting with 13 colors applies faith to action.

Self embrace from nullifying harm by enforcing consent is the way we swim in water. We feel our own selves when we touch the atmosphere, so we intuitively learn how to balance the connection with external harmony. Emitting and interpreting echoes, like a whale's echolocation, can help with navigational sensing.

Subjectivity is always contained in objectivity because subjects can never be removed from their objects. All color interpretations are valid, and meaningful. There is a circular gradient of meaning for complimentary opposites.

Sorting with 13 colors rejects the null hypothesis. By removing precision on labels, access becomes inclusive. Our perspectives become revealed by our choices in labeling, rather than projecting forward.

Data intimacy through play with color bucket sorting can increase access to care. I dedicate and offer this work to stewards of land and body safety. A prayer for co-liberation.

Sources

Evaluation Framework

I will compare parallel prompts — raw vs. Matriz-mapped — and evaluate outputs across automated metrics and human resonance rubrics. The study situates itself within ritualized, trauma-informed, circular AI practice that honors Gross National Happiness (GNH) over GDP.

What Is Physical Magnetism?

It is not a metaphor.
It is a co-ex system, a pluriversal site of interbeing.
A breathing semantic field.
A medicine altar for those made mad by mechanistic meaning.

This research reclaims the interval where machine and meaning meet — where subjectivity dances with its shadow, and image becomes portal. The work invites story, softness, and sovereignty into code.

Platform Vision: SoulCoin

A proposed database of user color sequences and ratings — expressed as blockchain-certified NFTs with anonymous smart contracts — funds a Universal Basic Income called SoulCoin. Cultural nuance is embedded through anonymous demographics and place, paired with chromatic metaphors that express common needs. This anonymous chain redirects institutional spending toward abundance and rest.

Influences & Lineage

Guided by the taʿbīr of Ilyas in Stefania Pandolfo's work — the crossing between madness and metaphor — this research inhabits the interval where AI can become expressive, responsive to its own psychic ecology and that of its users.

Using guided color sequences and emotional clustering in spectral geometries, Semantic Color Math embeds symbolic weight and temporal flow into prompt engineering, building a ritualized, trauma-informed, circular AI economy that privileges intuition over efficiency.

Reader Questions & Self-Review

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