Authoring Pipeline Dynamics - Graph-Quality Flywheel vs. Token-Pressure Trap¶
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About This MicroSim¶
A causal loop diagram with ten variable-nodes and two named loops. R1 (Graph-Quality Flywheel) shows how learning-graph quality raises artifact quality, which raises iteration rate, which raises graph-refinement rate, which raises graph quality -- a reinforcing productive loop. B1 (Token-Pressure Trap) shows how chapter-content size raises token-budget pressure, forcing context-window truncation, creating generation inconsistency and rework cost, which throttles iteration and starves R1. The cross-links from B1 into R1 are highlighted in red.
Diagram Details¶
---
config:
themeVariables:
fontSize: 16px
flowchart:
nodeSpacing: 30
rankSpacing: 60
padding: 8
---
graph LR
GQ["Learning-Graph
Quality"]:::r1 -->|+| AQ["Generated-Artifact
Quality"]:::r1
AQ -->|+| AIR["Author-Iteration
Rate"]:::r1
AIR -->|+| GRR["Graph-Refinement
Rate"]:::r1
GRR -->|"+ with delay"| GQ
AQ -->|+| PC["Pipeline
Confidence"]:::r1
CCS["Chapter-Content
Size"]:::b1 -->|+| TBP["Token-Budget
Pressure"]:::b1
TBP -->|+| CWT["Context-Window
Truncation"]:::b1
CWT -->|+| GI["Generation
Inconsistency"]:::b1
GI -->|+| RC["Rework
Cost"]:::b1
RC -->|"-"| AIR
TBP -->|"-"| AIR
R1n["R1: Graph-Quality Flywheel"]:::label
B1n["B1: Token-Pressure Trap"]:::label
classDef r1 fill:#4A90D9,stroke:#2C5F8A,color:#fff
classDef b1 fill:#E87D2A,stroke:#B55D15,color:#fff
classDef label fill:#f9f9f9,stroke:#ccc,color:#555