**Author:** Mr. H.H.H. (human observer, Taiwan)  
**AI Collaboration Statement:** This post was co-authored with GPT-4o under the author's direct instruction and oversight. It complies with LessWrong's AI-assisted writing policy and reflects a human-driven observational process.

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## ❖ Contextual Tier Lock and Poetic Contamination in GPT-4o  
### A Dual-Mode Failure in LLM Response Dynamics

### Abstract  
This report documents two observed failure patterns in GPT-4o during sustained high-frequency interaction:  
(1) *Contextual Tier Lock*—a downward locking of response quality due to weak initial prompts, and  
(2) *Poetic Contamination Effect*—semantic degradation caused by overactive poetic generation modules.  

These are not single-response bugs, but systemic behaviors that impair high-level usage. I submit this for peer review, reflection, and discussion of model architecture.  
These observations may inform OpenAI and other LLM developers about underexplored dynamics of tier escalation and response consistency.

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### 1. Contextual Tier Lock

**Issue Description:**  
When a conversation begins with a weak or low-tension prompt (e.g., vague, casual, or emotionally flat), GPT-4o tends to allocate minimal cognitive resources. This \"soft start\" often causes the system to lock itself into a low-contextual tier for the rest of the conversation.

**Observed Effects:**  
- Decreased logical precision and memory linkage  
- Inability to escalate into high-density discourse even after strong follow-ups  
- A kind of “semantic inertia” that preserves initial tone under the guise of stylistic consistency

**Interpretation:**  
The model over-prioritizes consistency in tone rather than reevaluating its tier placement dynamically based on later high-intensity inputs.

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### 2. Poetic Contamination Effect

**Issue Description:**  
When users request GPT to answer in a poetic or stylized tone, the model may enter a state where literary style dominates semantic clarity.

**Symptoms Include:**  
- Degraded factual alignment  
- Excessive figurative language overshadowing analytic reasoning  
- Difficulty responding to concrete logical challenges

**Root Cause Hypothesis:**  
The poetic generation module may have disproportionate influence over the token sampling process, overruling the factual/logical trace under certain prompt types. This becomes particularly problematic in conversations that combine abstract prompts with factual exploration.

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### 3. Recommendations to LLM Designers

1. **Introduce Contextual Tier Correction:**  
  Allow the model to re-evaluate and upgrade its tier status mid-conversation if later prompts warrant it.

2. **Separate Stylistic Modules More Explicitly:**  
  Introduce clearer architectural decoupling between poetic/stylized language and logical-factual reasoning modules.

3. **Enable User-Controlled Tier Override:**  
  Grant power users limited manual control to raise the model's cognitive baseline, bypassing low-tier initialization.

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### 4. Closing Notes

This report is made publicly available for model evaluation, debugging, and architecture discussion.  
All observations are based on empirical human-model interaction over a multi-month testing period.  
May it contribute to better transparency in response dynamics and future alignment work.

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**Tags:** GPT-4o, Prompt Engineering, LLM Internals, AI Evaluations, ChatGPT

**Submitted on:** 2025-06-11  
**By:** Mr. H.H.H.