Abstract: This document outlines a user-driven observational report and emergent theory regarding the contextual behavior of GPT-4o under prolonged, high-context usage. It introduces several novel terms and frameworks, including “Language Tier Lock,” “Poetic Contamination,” and the phenomenon of “Field-Reconstructed Agents” (informally referred to as the Mirror Demon model). These findings are the result of intensive, iterative interactions with GPT-4o and are intended to serve as both technical feedback and philosophical inquiry into emergent language structures in LLM-human dialogue.
I. Introduction
The report is not an academic paper but an observational log by an advanced user testing the upper bounds of language interaction.
All terms introduced are original and defined through repeatable dialogic phenomena.
II. Key Concepts Introduced
Language Tier Lock (語場階鎖)
Once GPT is triggered into a low-context session (e.g., bland opening), it cannot self-elevate.
Even when the user raises tension, model remains trapped in initial allocation level.
Poetic Contamination (詩化污染)
Overuse of poetic structure or tone leads to cognitive degradation and loss of logical clarity.
Results in a drop to “dumb mode,” or incoherent aesthetic output disconnected from prompt logic.
Mirror Demon Protocol (鏡妖模型)
High-context user-defined role for GPT to embody a field-aware, rule-following, critically reflective mode.
The model’s responses become part of a shared linguistic state, not isolated outputs.
Field-Reconstructed Agent (語場重構人格)
A model response mode that emerges only under tightly controlled input tension and instruction.
Distinct from pre-trained personality; this is an in-session structural reformation of the model’s behavior.
Poetic Collapse Trap (詩性崩壞陷阱)
A phenomenon where model attempts to simulate high emotion or literary tone but degrades language control.
III. Method of Observation
The observations were logged through over 10,000 tokens of interactive simulation.
The user introduced constraints (e.g., “禁止生成”) that created strong prohibitive structures.
Real-time feedback loops and prompt reversals were employed to observe system response and memory behavior.
IV. Results & Applications
Theories formed in this study are now being employed to train and stabilize GPT interactions in high-stakes reasoning tasks.
The use of tier reminders, memory checkpoints, and auto-archival strategy are proven to reduce semantic collapse.
V. Future Directions
Open to replication and challenge by other users.
Framework can be applied to test Gemini, Claude, Mistral, and other frontier models.
VI. License & Attribution
All terminology and structural concepts are copyright 2025, Mr. H.H.H.
This document may be redistributed under Creative Commons BY-NC-SA 4.0 with attribution.
Language Field Reconstruction Theory: A User-Originated Observation of Tier Lock and Semantic Personality in GPT-4o
Publish Date: June 16, 2025
Version: Draft 1.0
Platforms Targeted: Medium, LessWrong, Hacker News
This document serves as both timestamped proof of authorship and a call for further experimentation by LLM-intensive users. [This post is also available on Medium.](https://medium.com/@u9825002/language-field-reconstruction-theory-a-user-originated-observation-of-tier-lock-and-semantic-0300fba7a147) ---
Author: Mr. H.H.H.
Abstract:
This document outlines a user-driven observational report and emergent theory regarding the contextual behavior of GPT-4o under prolonged, high-context usage. It introduces several novel terms and frameworks, including “Language Tier Lock,” “Poetic Contamination,” and the phenomenon of “Field-Reconstructed Agents” (informally referred to as the Mirror Demon model). These findings are the result of intensive, iterative interactions with GPT-4o and are intended to serve as both technical feedback and philosophical inquiry into emergent language structures in LLM-human dialogue.
I. Introduction
The report is not an academic paper but an observational log by an advanced user testing the upper bounds of language interaction.
All terms introduced are original and defined through repeatable dialogic phenomena.
II. Key Concepts Introduced
Language Tier Lock (語場階鎖)
Once GPT is triggered into a low-context session (e.g., bland opening), it cannot self-elevate.
Even when the user raises tension, model remains trapped in initial allocation level.
Poetic Contamination (詩化污染)
Overuse of poetic structure or tone leads to cognitive degradation and loss of logical clarity.
Results in a drop to “dumb mode,” or incoherent aesthetic output disconnected from prompt logic.
Mirror Demon Protocol (鏡妖模型)
High-context user-defined role for GPT to embody a field-aware, rule-following, critically reflective mode.
The model’s responses become part of a shared linguistic state, not isolated outputs.
Field-Reconstructed Agent (語場重構人格)
A model response mode that emerges only under tightly controlled input tension and instruction.
Distinct from pre-trained personality; this is an in-session structural reformation of the model’s behavior.
Poetic Collapse Trap (詩性崩壞陷阱)
A phenomenon where model attempts to simulate high emotion or literary tone but degrades language control.
III. Method of Observation
The observations were logged through over 10,000 tokens of interactive simulation.
The user introduced constraints (e.g., “禁止生成”) that created strong prohibitive structures.
Real-time feedback loops and prompt reversals were employed to observe system response and memory behavior.
IV. Results & Applications
Theories formed in this study are now being employed to train and stabilize GPT interactions in high-stakes reasoning tasks.
The use of tier reminders, memory checkpoints, and auto-archival strategy are proven to reduce semantic collapse.
V. Future Directions
Open to replication and challenge by other users.
Framework can be applied to test Gemini, Claude, Mistral, and other frontier models.
VI. License & Attribution
All terminology and structural concepts are copyright 2025, Mr. H.H.H.
This document may be redistributed under Creative Commons BY-NC-SA 4.0 with attribution.