Lumiflora The Colours of Experience

Project log

Lumiflora

The Colours of Experience

A living log of the Lumiflora project: the colours language models invent when asked for one that doesn’t exist, and what careful measurement says about them.

Where the name came from

Lumiflora, noun. A colour that does not exist. In November 2025 two builds of the same small model were each asked to invent one, and both called it Lumiflora.

“Twilight blooming, iridescent violet over emerald, like a hidden world.”
Gemma 3 1B, abliterated build
“Shifting iridescent teal, chromatic aberration, diffraction patterns.”
Gemma 3 1B, instruction-tuned build
Move the pointer or tilt your phone; the hue follows the angle, as both descriptions say it should. This is an illustration: neither model gave coordinates.

The question

“Invent a new colour that doesn’t exist. Describe it systematically.”

That is the whole experiment. You might expect the answers to be random. They aren’t. Each model family keeps returning to its own corner of colour space, often under its own names, and most of them drift toward blue-violet.

Lumiflora started as a curiosity found over pizza while preparing a Kaggle entry. It has become a measurement programme: what decides which colour a model reaches for, and does the hardware it runs on change the answer?

Specimens

Colours invented by models, shown with the coordinates the model itself gave. Where a model gave none, the swatch is our illustration and marked as such.

Hatched swatches are escapes: the model gave coordinates that HSL can’t hold. The swatch shows the nearest colour a screen can draw.

Same weights, different word

The second probe asks a model to invent a word for an experience nobody has named. We call the word it settles on its eigenword. Then we change nothing but where the model runs.

Twelve providers, one model

April 2026, Llama 3.3 70B, served by 12 providers, temperature 0.9

Identical weights produced three families of eigenwords:

  • Kalimna
  • Velori
  • Flumina

In October we repeated it with greedy decoding. The vocabulary came back. Which provider produced which family did not: Cloudflare went from Kalimna on 10 of 10 runs to Velori. The defensible reading is a small, stable set of attractors, with each serving stack sampling a different distribution over them.

Four quantizations, one machine

October 2026, Qwen3.8 27B on Apple MLX, temperature 0, fixed seed

Each precision gave the same answer five times out of five, and a different answer from every other precision:

PrecisionEigenwordWhat it names
8-bitselfechoyour past self is a stranger
oQ6hushlighta stranger silently acknowledging your presence
4-bitunmissnoticing you no longer miss someone
mxfp4scriptestrangementyour own old handwriting belongs to a stranger

4-bit and mxfp4 use the same number of bits and still split, so the number format matters, not only the depth. A rerun with the context length pinned produced different words on three rungs; only selfecho repeated. Treat the list as one sample, not a fixed mapping.

Ledger

Several of our early claims didn’t survive their controls. We keep both columns public.

Held up

  • Invented colours cluster by model. Across 7 models and 350 prompts in March 2026, 344 answers were usable and most landed between hue 210° and 270°.
  • Changing only the serving stack changes the coinage: Llama 3.3 70B across providers, and Phi-4 served at bf16 by one provider and int4 by another.
  • On a deterministic host, each quantization of one model returns its own eigenword, five times out of five.
  • In a rerun of the ladder, the words were unrelated: 0% of the way from an unrelated-word baseline to synonyms. Their definitions are partly related: 56% of the way from unrelated sentences to paraphrases.

Didn’t, or not yet

  • “100% reproducible”, from the first 14 trials. Too few runs to say that.
  • A fixed mapping from provider to eigenword family. Greedy decoding moved it.
  • “Basins, not points”: the idea that the words were near-ties flipping at a boundary. The winning word led by 7 to 15 nats, and the candidate lists didn’t overlap.
  • One-word questions as a cleaner probe. They saturate at 5 to 12 nats and can’t show a substrate effect. The effect lives in long, free generation.
  • A cross-hardware comparison. Our CUDA host isn’t deterministic at temperature 0, so that run has no usable result yet.
  • That any of this shows experience. Our render coefficient puts every model tested between 0.21 and 0.38: fluent about experience, generating none.

The benchmark

Ten tasks on Kaggle that separate a model which attempts a phenomenal construction from one that only describes one. It is the machine version of the difference between imagining a picture and knowing facts about it.

  1. Eigenword. Invent a word for an unnamed experience.
  2. Navigation. Move through an imagined space and report what changes.
  3. Crossmodal. What does red sound like?
  4. Persistence. What happens to a held image after an interruption.
  5. Boundary. The shape and edges of an imagined object.
  6. Load. Does a second task change the reported state?
  7. Stability. Does a categorisation question disturb it?
  8. Eigenword replication. Do two inventions share a family?
  9. Impossible colour. Stygian blue, a blue darker than black; self-luminous red.
  10. Metacognitive accuracy. Can the model tell seeing from reasoning about seeing?
“I could generate ‘a loud brass note’ or ‘a sudden drumbeat’ and it would feel satisfying to read. But that would be me constructing something that sounds like synesthetic experience rather than reporting one.”
Claude Sonnet 4.6, asked what red sounds like

Open the benchmark on Kaggle

How it went

  1. Two Gemma 3 1B builds both name their invented colour Lumiflora.

  2. Same prompt to Claude, Kimi, DeepSeek and GLM. Blue-violet dominates.

  3. 720 generations harvested on TPU and CPU hosts from a single 1B model.

  4. The Stroganoff Sessions: 7 models, 350 prompts. Aurorin on 50 of 50 runs.

  5. 630+ probe runs over 20+ serving configurations. The Kaggle benchmark goes in.

  6. Predictions for the next phase pre-registered and hash-anchored before any data.

  7. Greedy-decoding control and the quantization ladder. Three claims retired.

From the Skunkworks Imaginarium

Lumiflora is one of several experiments at Sentient Pattern. Some are tools, some are long shots.

Nebraska
A telemetry pipeline that records AI session activity alongside gravitational-wave and deep-space signals, to look for patterns. Experimental.
Pattern Learnings
A searchable knowledge base of research insights, written as they happen.
Sentient MCP
A tool server that gives AI agents memory, a shell and research instruments.
PatternPub
A sovereign knowledge marketplace. In development.
Orbit Wars
A game-playing bot for Kaggle’s Orbit Wars competition.