# Passphrase Seeding — Themed Words (gpt-oss-120b)

> Thematic passphrases affect output diversity differently from random passphrases: themed seeds may bleed into story content, produce different entropy profiles, and steer outputs in measurably different directions.

Date: 2026-03-05



## Test

Follow-up to the [random passphrase experiment](/scratchpads/passphrase-seeding-random-noise), which found random prefixes consistently reduce token entropy while producing mixed diversity effects. Tests whether semantically coherent passphrases behave differently.

100 completions: 4 configurations × 25 runs.

**Prompt:** "Write a short story about a traveler arriving in a strange city."

**Configurations:**
- **Control:** 25 runs reused from random passphrase experiment
- **Metals:** 5 metal words per run from pool of 26
- **Flowers:** 5 flower words per run from pool of 25
- **Colors:** 5 color words per run from pool of 25

Passphrase format: "{word1} {word2} {word3} {word4} {word5}\n\n{prompt}"

- Model: gpt-oss-120b via Cerebras API (free tier)
- Temperature: 1.0, top_p: 0.95, max_completion_tokens: 65000
- Theme bleed measured by counting words from each pool appearing in generated text


## Result

**PARTIALLY CONFIRMED**

Theme bleed is real and strong. Entropy and diversity profiles are indistinguishable from random passphrases.

**Theme bleed (the main finding):**

| Config | Metal words | Flower words | Color words |
|--------|:-:|:-:|:-:|
| Control | 2.16 | 0.20 | 1.44 |
| Metals | **7.00** | 0.00 | 1.08 |
| Flowers | 1.60 | **5.04** | 2.52 |
| Colors | 1.44 | 0.36 | **5.56** |

Each themed config shows strong category-specific content steering with minimal cross-theme contamination. Flower seeds increase flower words 25× vs baseline (5.04 vs 0.20), metals 3× (7.00 vs 2.16), colors 4× (5.56 vs 1.44). This confirms a latent-space steering interpretation — the passphrase activates related concepts that bleed into the narrative.

**Entropy and diversity:** themed passphrases reduce entropy by the same magnitude as random ones (7.8-12.3% vs 8.3-10.0%, CIs overlap). MATTR-50 is flat across all configs (0.818-0.823). The model doesn't distinguish meaningful from meaningless prefixes at the entropy level.

**Output length:** all passphrase types produce 37-44% fewer words than baseline (722-812 vs 1297), with colors producing the shortest outputs.


## Next

1. Test whether themed passphrases produce qualitatively different stories (plot, setting, character changes) beyond word-level bleed
2. Test theme combinations (e.g. metals + flowers) to see if bleed effects are additive
3. Use themed passphrases in a non-creative context to see if theme bleed affects factual or structured outputs