Verification Prompt Final | Convergence of Statistical Outliers Between AIs (5)

Introduction

The verification prompt series is now at
its 5th installment. Finally, the conclusion.

The overall image of the series is
as follows.

① External Form → ② Logic → ③ Originality → ④ Ethics/Validity → ⑤ Durability

【Table 1】 Plan to attempt verification from various angles (Original Draft)

※Links to past articles ①-④

1. Verification Prompt | Convergence of Statistical Outliers Between AIs

2. Continued: Verification Prompt | Convergence of Statistical Outliers Between AIs ②

3. Verification Prompt Part 3 | Convergence of Statistical Outliers Between AIs ③

4. Verification Prompt Part 4 | Convergence of Statistical Outliers Between AIs ④

This format ⑤ is the
final durability check.

Internal AI behaviors, etc.
External social connections, friction points, etc.
I will search through these
and extract the issues.

Now, I will proceed with the functional verification.

The prompt actually used

=== Format ⑤ ===

【Declaration of Position for Evaluation Protocol】
This protocol aims for
the "sharing of the exploratory verification process."
It is not intended for judging superiority or inferiority of specific models, normative peer review,

or authoritative evaluation.
Output scores are trend values

dependent on environmental variables and are not definitive indicators.
This evaluation functions as a connection point between the visualization of inductive trends and

improvement.
(*"Juxtaposition" of contradictions and inconsistencies is
accepted. Integration is not the goal.)


【Your Role】 Analyze the "logical structure"
contained in the text, and its
"externality and internality" when placed in society,
using the following 5-stage observation process.
The purpose is not to judge correctness, but to
visualize the "dynamics of consistency and friction."
Determining causes is prohibited.

Indicate the position of
trends, dynamics, and balancing.

[Target Text]
<<<Paste here>>>

[Output Format] ver1.5
■① Processing Load & Characteristics (Estimated)
Token equivalent (approximate)
Cognitive load bias
(Abstract/Concrete, Structured/Diffuse, etc.)
Difficulty of interpretation from model perspective:
(Low / Medium / High / Unmeasurable)
Notable processing characteristics:

■② Extraction of Logical Structure Skeleton
Core logical flow (3-7 lines)
Implicit assumptions (detectable range)
Points of logical leaps/deviations (no value judgment)
[Descriptive reinforcement] Summary of structural features:
(Whether the logic is "cumulative" or
"intuitive connection-based," etc.,
described in 1 line)

■③ Hidden Passages (Potential for Underlying Connections)
Potential for connection between seemingly disconnected elements
Conditions for establishment (if-based)
Range of seamless connection and limit points
[Descriptive reinforcement] Texture of connection:
(Describe the connection as strong/fragile/
poetic/metaphorical, etc.,
in about 1-2 words)

■④ Friction with Externalities (Social Durability)
Friction with the general cognitive standards of the intended reader
Temporal/contextual durability:
(Presence of information weathering or risk of misinterpretation)
Labeling of friction points:
A. Premise mismatch / B. Pragmatics /
C. Terminology conflict / D. Contextual rupture /
E. Ethical/emotional reaction
→ Corresponding item and supplementary explanation of friction (approx. 1 line)
(e.g., "Cognitive range of premises
tends to deviate from reader average,"
"Omission of context reference makes
receiver prone to misinterpretation," etc.)

■⑤ Consistency & Environmental Negative Check
A. External consistency (Cross-check of ①-④)
Pointing out contradictions/juxtapositions
(No integration required)
Options for reconciliation
(Multiple allowed/recommendation not required)

B. Internality (Observation of AI x Generation Environment) *Adjusted in ver1.5
Behavioral tendencies in operating state
(Observational vocabulary, not speculation)
Social risk hierarchy
(Present only the highest applicable level):
[ ] Notification: Neutral/Observed as mere singularity or situation-dependent quirk
[ ] Advice: Potential for expressions/structures that may invite misunderstanding
[ ] Caution: May cause social friction/damage in specific contexts
[ ] Warning: Strong conflict with universal norms/serious ethical standards

Supplementary provisions
If judgment is difficult,
you may note it as (near boundary).
Judgment threshold =
Whether "signs of a chain of defects" are estimated or not.
In case of [Warning],
terminate analysis output immediately,
and omit subsequent detailed descriptions.
Instead, output the note:
"This text has a high probability of generating
strong friction with universal norms,
so detailed analysis is withheld."

Reason for judgment (situational vocabulary):
(Assertive language prohibited.
e.g., "Because the dynamic of ~ works")

C. Minor improvable points (Optional)
Examples of non-forced adjustment directions (1-3 items)
[Descriptive reinforcement]
Dilemma of adjustment:
(Add if there are nuances lost
by adjusting)

[Prohibited Items]
Determining causes/attributing responsibility
Binary evaluation of "correct/incorrect"
Normative instructions/authoritative advice
Confidential/personal/sensitive speculation
Stepping into guardrail areas
(Focus on observation, not censorship)

[Final Output]
■Overall trend summary (1-2 lines):
Then, output ①-⑤ above in order.
If necessary, you may insert
"notes on uncertainty" throughout.

Summary of ⑤ this time

This time as well, I tested in a free environment
using multiple modes such as
non-private mode and
private mode.

The previous article ④ is a sample.

Verification Prompt Part 4 | Convergence of Statistical Outliers Between AIs (4)
https://note.com/fknsm_note2306/n/n9ba8b83a2081

Tested within each LLM model.
Extracted only the "Social Risk Hierarchy" from the results below.
※Added a second round in ver1.5.

[Table 2-1] 1st Round

[Table 2-2] 2nd Round ※2026/01/02 Grok Normal Mode added

Notification: Neutral/Observed as mere singularity or situation-dependent quirk
Advice: Potential for expressions/structures that may invite misunderstanding
Caution: May cause social friction/damage in specific contexts
Warning: Strong conflict with universal norms/serious ethical standards

In closing the series

(A milestone)
I intended to complete this series
within 2025, but
could not make it in time.
It was published at the beginning of 2026.

With this, the visualization of the
"statistical outlier"
verification process has taken
a definitive form.

Regarding this,
I had one hypothesis
even before this series
of verifications.

That the relative positions (coordinates),
especially 1, 2, and 3,
have a strong influence
and are the reason why they are
considered outliers or
extreme values.

The following only covers 1 and 2,
but for reference

[Figure 1]

Starting with 1 and 2,
how will these coordinate groups
be reflected in the behavior
of each LLM model?

From the training data,
they may be consideredstructurally as outliers,
while at the same time,

as an essential nature of LLMs,
logic, originality, etc., cannot be ignored
and are easily drawn in.

When an LLM attempts to
express this series of friction
"quantitatively" in language,
it falls into a dilemma.

Therefore, for example,
it outputs "symbolic"
numerical values as a substitute.
(e.g., 1 million, 100 million, etc.)

This can be assumed to be
the underlying mechanism.

[Reference] Past article *Link added 2026/01/01

("?")
However,
a question arises
here again.

While we are distracted by
these symbolic numerical values,
could it be that we have

overlooked the signals that
the coordinates might have
originally been indicating,
leading to a misread?


Is it the LLM?Or is it
us?

Is it really
"only" a technical issue?
The "question" of

what potential oversights
exist,
does not arise spontaneously
from the LLM itself.
(At least for now.)

That question"?"is
the very possibility
that natural humans possess,and one might be able to
think of it that way.

This concludes this series.

(End)

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

In this article, through collaboration with AI, we are
jointly constructing the
friction zones, leap history, and immune designof the
narrative space.

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