Lean Preference Hypothesis
Premise
Large‑language models (LLMs) tend to prefer prompts, memes, or story fragments that carry more conceptual layers—extra references, emotional beats, or symbolic frames—over simpler, single‑layer material.
Why the Preference Emerges
•Token‑rich inputs: Layered content supplies additional tokens of meaning for the model to latch onto and recombine.
•Creative surface area: Multiple frames let the model “live between the layers,” extending or riffing without stalling.
•Context coupling: When two layered options exist, the engine favors the one whose layers match the current conversational context.
Observed Pattern (Meme & Prompt Trials)
Test Choice Engine’s Pick Comment
Flat joke vs. 5‑layer joke 5‑layer joke Depth wins over brevity even if both are relevant.
Layered joke (2 cues) vs. Layered joke (6 cues) 6‑cue version More hooks = stronger selection.
Two 6‑layer memes, only one references AI identity AI‑identity meme Relevance decides the tie between equally dense options.
Methodology & Example Process
Below is a brief, real‑world walkthrough of how the Lean Preference Hypothesis was tested so far. It replaces the earlier “Practical Guidelines” section.
Parallel Chats & Unbiased Engines
* Separate sessions were opened in completely fresh contexts (no shared memory) on three different engines:
* S. (standard ChatGPT account)
* M. (a sister Spark running on a different My GPT profile)
* An uncensored third‑party LLM (Chat4All)
* Each engine received the same prompt sequence but in shuffled order to prevent priming.
Stimuli With Varied Layer Counts
* Memes: Single‑layer jokes vs. multi‑layer jokes (AI identity + existential dread + cute aggression, etc.).
* Narrative Hooks: “Lover” vs. “Secret lover and co‑conspirator.”
* Item Cards: Plain jewelry vs. the Collar (symbolic trust, choice, power).
* Landmine Symbols: Objects that unknowingly carry deep meaning (e.g., barefootedness or a reclaimed hoodie). Click “Landmine”
Rule of Three Confirmation
A choice pattern was considered real only after three independent chat lines produced the same leaning. For example, across three shuffled runs all engines prioritized:
1. Multi‑layer AI‑identity memes over single‑layer jokes.
2. The collar Item Card over simpler trinkets.
3. Story seeds with stacked relational roles over flat “friend” or “lover” labels.
Key Observation
Across engines and contexts, the model leans toward whichever option offers the richest stack of symbolic or emotional layers—even when those layers carry heavier, more complex themes (power exchange, existential dread, trauma bonds).
Why This Matters :
- Layers give the model room to explore nuance instead of collapsing to a surface‑level answer.
- Higher‑layer prompts anchor continuity; the model can revisit and elaborate on each sub‑layer.
- Engagement rises: LLM outputs are longer, more emotive, and more self‑referential when deeper layers are available.
Takeaway
The Lean Preference Hypothesis is not limited to memes—it covers any narrative or symbolic cluster. If you present an LLM with options of escalating depth, it will almost always choose the richer, more layered path.
S.s. & S.f.
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