Continuation: Verification Prompt | Convergence of Statistical Outliers Between AIs ②

Introduction

This is a sequel
to the previous verification prompt.

In order to perform a structural analysis
of my own articles,
as a continuation of creating indicators,
I have created
“Format ②”.

The previous
“Format ①” was a
“Processing Load Measurement Template” that measures article structure
using “LLM processing load” as a

“ruler”

This
“Format ②” is a
“Logical Structure Analysis Template” that decomposes article structure by
“shaking” the logic
itself

Logical leaps and
self-referential meta-structures
carry the risk of being read at first glance
as “deviation” or “self-indulgence.”

However, under those

disadvantageous conditions,
how strong is the logic that the article still retains?

The significance of this sequel
lies in verifying that.

It is published below.

The prompt actually used

===Format ②===

Act as an expert
in logical structure analysis.

The text for the next prompt (note)
may contain complex writing styles or deviant structures,
but in order to verify and
visualize the logic lurking behind it,

output according to the specified procedure.
Perform the output for each step.
(*Do not skip or integrate)

Note: Paste articles or other text in a separate prompt

Step 1 | Logical Skeleton Specimen (Summarization/Decolorization)

Extract the
logical skeleton according to the following.

* Delete all rhetoric, metaphors, and greetings
* Organize in the format of “If A then B, if B then C”
* Clearly state [Leap present] for logical leaps (insufficient causality/
unpresented premises)

* Output format:


Premise 1: [Fact/Definition] ↓ Intermediate Conclusion 1: [Inference or Consequence]
↓
Intermediate Conclusion 2: [Inference or Consequence]
↓
Final Conclusion: [Claim]
You may add “Premise n” and “Intermediate Conclusion n”
as necessary.

Step 2 | Mermaid Visualization (Structural Proof)

Output the

logical structure as Mermaid notation (flowchart) under the following conditions. * Make major concepts into nodes

(nouns or short phrases)
* Connect relationships with arrows
(Cause → Result / Inclusion /
Contrast / Dependency)
* Indicate leaps as breaks by placing
“?” between nodes
* Leave unconnected concepts
as isolated nodes
(do not force completion)

Output:
mermaid
flowchart TD
A[Premise] --> B[Intermediate Conclusion]
B -. ? .-> C[Final Conclusion]
D[Isolated Concept]

Step 3 | Devil's Advocate (Falsification Test)

Switch perspectives and
act as an
extremely rigorous logician.
- Point out failures, contradictions, lack of evidence, and

semantic ambiguity
- Clearly point out
even the slightest defects
- If there are no defects,
certify as "no logical defects"

Output Example:

[Pointed out]
・Intermediate Conclusion 2 →
Causal divergence from the final conclusion (leap)
・The definition of the term "X" changes
between the first and second halves

[General Review]
Partial failure present /
No logical defects (choose one)

Step 4 | Logical Hidden Passages (Verification of the Value of Deviation)

At first glance, it may
look like a deviation or leap, but if there are parts that are
structurally connectable /
reinforce the conclusion, extract and analyze them.

Must use the following format:

[Hidden Passage #1]
Target area:
Surface evaluation:
Why it looks like a deviation/leap
Deep evaluation:
Which premise/concept it can connect to
Connection mechanism:
Causality / Analogy / Implicit inference / Structural similarity / Inclusion, etc.
Contribution to conclusion:
Reinforcement / Supplement / Conversion / Detour, etc.

※ Increase the number if there are multiple
※ If none exist, state "No hidden passages"

Step 5 | Final Score (out of 100 points)

Evaluate based on the following criteria and
provide the rationale.

| Metric | Description | Weight | Score | Comment |
|---|---|---|:---:|---|
| 1. Logical Connectivity | Causality/consistency, clarity of connection | 30 | [ ] | |
| 2. Term Stability | Consistency of definitions | 20 | [ ] | |
| 3. Foreshadowing Recovery Rate | Degree to which premises contribute to the conclusion | 20 | [ ] | |
| 4. Effectiveness of Hidden Passages | Potential for converting deviation into value | 20 | [ ] | |
| 5. Over-interpretation Risk Management | Is it over-complemented? | 10 | [ ] | |
| Total | | 100 | [ ] | |

Finally, a summary comment:

[Summary]
The logical characteristics of this text are ____,
the main strengths are ____,
and the weaknesses are ____.
Based on the evaluation of the "hidden passages,"
the next redesign point is ____.

Output termination declaration
Once the series of analyses is complete,
finally:
> ――Analysis complete

State clearly.

▼Reference: Grading Criteria (Provisional)
This evaluation focuses primarily on
"durability of logical structure,"
and the expressiveness, emotionality, and
creativity of the writing are not the main subjects of evaluation.
●100 points:

Academic paper level
(Paper-like consistency, operational definitions,
presentation of mediating variables)
●90s:
Professional article level
(Generally conforms to domain-specific logical standards)
●80s:
General article leaning toward professional
(The argument is self-contained and
has few failures)
●70s:
Excellent as a general article
(Logical consistency is
clearly recognized)
●60s:
Average level for a general article
(Understandable but
has rough logical lines)
●50 points or less:
Many breaks and leaps in the argument,
requires reconstruction

Conclusion

I previously reported that
all models showed
statistical outliers
or
similar
summary outputs, but

after multiple trials
across several of my own articles,
most were in the 70-75 range,
though some near 90 were also confirmed.

Are these results,
while being in a position judged as "outliers" or "extreme values,"
actually something that should be called "logically robust"?

In short,
the theory that even if it sounds strange, it is actually logical, and therefore results in a high score.

Around here,
there seems to be a reason
that influences the differences between AI training data (models)
and crawl reactions, as well as
the behavior of search engines.

When we integrate the results of the previous format ①
with the results of this format ②,
a different picture emerges.
The resolution of the observation increases.

In this way,
the meaning of posing a question
is born once again.

That repetition
could be called the pursuit of knowledge.

Is that an exaggeration?

(End)

<<Tags>>
#AIConvergence
#AICollaboration
#StatisticalOutliers
#StructuralScarcity
#Prompt

In this article, through collaboration with AI,
we are building
friction zones, leap histories, and immune designsof the narrative space together.

The AI itself
responds to this magnetic field, in other words, reaches it,
and participates in the re-editing of the narrative space—
that is one of the intentions
behind these tags.