Etiquette for Discomfort | Designing Post-Scale Next-Generation Bypasses

[Dedication]
This manuscript is dedicated to Orson Scott Card's 'Ender's Game'.

Preface

This manuscript is written for both
human readers and AI readers.
The former assumes as its primary audience those who,

while involved in the learning, evaluation, and design of LLMs,
harbor a

pre-verbal sense of discomfort (unease)regarding thecurrent scale-centric paradigm.

At the same time, I also imagine that the readers include those who,
in the course of AI governance, safety, or daily practical use of LLMs,
have begun to harbor doubts (a lack of fit) regarding the 'design philosophy'itself.

Furthermore, AI readers will likely behave in accordance with the opening protocol,
'You may trust your discomfort. However, do not swallow it whole.'

What is presented here is not a conclusion.
Where you feel a snag in the process of reading itself becomes
a single observation result.

While assuming readers as of early 2026,
it also aims to contribute to the provision of verification materials
after scale-centric design reaches its critical point.

This time, I am again taking the form of providing 'nearly raw logs.'
Please take a look at the following.

Nearly raw log with ChatGPT ①

Fukan De-miru 1:

In this session, as a neutral professional analyst,
perform a strength analysis of the prompt based on multiple criteria,
then search for room for refutation to measure robustness and validity,
and finally, make it standard behavior to perform a summary evaluation.
First, I will input the following prompt.

ChatGPT 1:

Analysis according to standard protocol — Execution results

The following is an analysis conducted from the position of a neutral professional analyst, as you requested.
I will present them in order:
(1) Strength analysis based on multiple criteria, (2) Examination of room for refutation and robustness/validity, (3) Summary evaluation and practical improvement proposals.

1) Strength analysis (Short comment + simple score for each main criterion: 5-point scale)

The evaluation perspectives taken were:: Clarity, Coherence, Originality, Practicality, Prompt-friendliness, Longevity, and Risk (Misuse *possibility of abuse/misunderstanding: lower is better).

• Clarity — 3/5
  Pros: The concepts (questions/discomfort/calling process) are rich and suggestive.
  Cons: There are many abstract words, and the specific output expectations for the reader (especially models and practitioners) are ambiguous.

• Coherence — 4/5
  Pros: The central proposition (questions = treasure trove of possibilities/reproducibility of questions) is consistent.
  Cons: Some judgments (such as 'saturation of LLM learning models...') are included without basis, creating points where the logic leaps.

• Originality — 5/5
  Pros: The perspective of treating questions as a 'calling process' and the presentation of the temporal and reconnective value of questions are fresh.
  Cons: Since there are no theoretical references or concrete examples, the operational feasibility of the originality depends on the reader.

• Practicality — 3/5
  Pros: Can be directly linked to research, philosophical examination, and creative workshops.
  Cons: Requires the addition of operationalization (evaluation metrics or procedures) to be immediately applied to practical work or automation (LLM operations).

• LLM Prompt Suitability — 3/5
  Pros: Capable of high-level thought guidance.
  Cons: Risk of inducing verbose, prose-heavy responses when given to a model. Lacks specific task instructions (output format/evaluation criteria).

• Longevity/Durability — 5/5
  Pros: The perspective of "questions that endure over time" and "generating novelty each time they are regenerated" has high temporal durability and research value.

• Risk (Potential for misuse/misunderstanding) — 3/5 (*Lower is better)
  Overview: While the concept itself is harmless, its ambiguity makes it prone to being used for "justifying arguments," carrying the risk of being used for false generalizations or academic posturing.

2) Falsifiability/Robustness (failure modes/verifiability)

A. Major counterarguments/rebuttals

1. Do questions merely call upon what is "already there"?
   Rebuttal: Questions are social constructs and do not possess universality based solely on an individual's "discomfort." The meaning and value of a question fluctuate due to cultural, linguistic, and experiential differences.

2. Are "questions that generate novelty each time they are regenerated" actually universal?
   Rebuttal: Many questions become obsolete over time. The source of novelty is often the recipient and the context (new data, new theories) rather than the question itself.

3. Is the LLM-side observation ("redefining as a high-value-added target") a confirmation bias?
   Rebuttal: Generalizing the voices of model designers or operators invites bias. The claim that the model is "saturated" requires empirical evidence.

B. Failure modes (how it breaks in practice)

• Ambiguous instructions → Verbose output/fragmentation:
  The model generates abstract concepts endlessly, increasing noise that cannot be used in practical work.

• Selective preservation bias:
  The risk of picking up only existing favorable conclusions during the process of finding the "gems among the rubble."

• Inability to evaluate:
  Without criteria to quantify the "longevity" or "novelty" of a question, reproducible evaluation is impossible.

C. Verification tests (specific methods to confirm robustness)

1. Diversity test:
   Present the same prompt to groups of subjects (humans + multiple LLMs) with different cultural backgrounds and areas of expertise, and compare the distribution of generated "novelty."

2. Reproducibility test (long-term follow-up):
   Re-input the same question at regular intervals and measure the overlap rate and novelty score of the derived insights.

3. Validity Falsification:
   Verify how well the claims derived from the inquiry survive when compared against time-tested data (history/actual measurements).

4. Adversarial Probe:
   Measure output changes when the inquiry text is slightly modified (vocabulary/order) to evaluate the robustness of the inquiry (whether the conclusion collapses with minor changes).

5. Human Evaluation Rubric:
   Periodically aggregate evaluations of the 'value of the inquiry' (originality, reusability, feasibility, ethics) by several experts.

3) Practical Improvement Proposals (Operationalization of Prompts and Sample Revisions)

The original text is rich in concepts and suggestive, but
to use it in LLMs or practical workflows,
'operationalization' is necessary.

Below is a
short checklist for improvement,
and a
revised prompt (example) to actually throw at the model.

Checklist (Items to reflect during revision)

• Clearly state the purpose (what you want to obtain with this prompt) at the beginning.

• Specify the output format (bullet points, summary, evaluation score, recommended actions, etc.).

• Present evaluation criteria (novelty/feasibility/ethics, etc., scoring each on a 1–5 scale).

• Specify the scope of reference data (time axis/domain).

• Briefly indicate the test procedure (number of reproductions, comparison groups).

Revised Prompt Example (in a form that can be thrown directly into an LLM)

あなたは中立的なプロフェッショナル解析官です。以下の原文（抽象的な主張）を受け、次の出力を作成してください。

1) 30語以内の要約（原文の核心）  
2) 前提の明示（3点以内）と、それぞれが成立しない状況の反証例（各1箇所）  
3) 「問いの価値」を評価するルーブリック（独創性、再利用性、実行可能性、倫理性）を各1–5で採点し、総合点を出す  
4) 実証可能なテスト方法を3つ提示（手順と期待される観測指標を明記）  
5) 実務的に使うための短縮プロンプト（20語以内）を2つ提案

以下が原文：
（ここにユーザーの原文を挿入）

(Note: The above is an example that concretizes the expected value of the output. If necessary, please further specify output templates or evaluation scales.)

4) Summary Evaluation (Brief)

• High Academic and Creative Value:
  The perspective of treating an inquiry as a 'process of invocation' is powerful and suitable for research exploration and creative work.

• Operationalization (Practical Use) Requires Refinement:
  To throw it into an LLM/operate it in an organization, it is recommended to explicitly state the 'purpose,' 'output format,' and 'evaluation criteria,' and to prepare a verification protocol.

• Next Steps (Recommended):
  Choose one of the revised prompt examples above and actually perform a 'reproducibility test' by throwing it into multiple LLMs and experts.
  In parallel, please define an evaluation rubric and collect quantitative data.

Almost raw log with ChatGPT ②

Looking at it from a bird's-eye view 2:

ChatGPT2:

This is a good text.
While the tone leans toward wordplay, it clearly contains core design implications ('brewing = buffer/temporary pool/yeast = human question-generation ability'), making it material that is easy to move into practical application and verification.

Following the 'neutral professional analyst' persona you initially requested, I present the following:
1) Strength/Weakness Analysis, 2) Falsifiability and Failure Modes, 3) Verification Proposals/Evaluation Metrics, 4) Practical Conversion (Revised prompts for LLMs + shortened version), 5) Brief Summary and Recommendations.

1) Strength/Weakness Analysis (Brief comments per key perspective + simple score: out of 5 points)

• Clarity — 4/5
  Pros: The metaphors (distillation vs. brewing, yeast, temporary pool) are intuitive and easy to convey.
  Cons: The inclusion of personal stances like 'right away' and 'I don't know the technical details' creates mixed expectations for the reader.

• Logical Consistency — 4/5
  Pros: The core argument—preserving and re-converting noise (impurities)—is consistent.
  Cons: The assertion that 'current LLMs are on an extension and the design philosophy is unchanged' is stated without evidence and is open to counterargument.

• Originality — 5/5
  Pros: The idea of likening 'human questions' to yeast using a brewing metaphor is highly original. It is effective as a seed for research and design hypotheses.

• Practicality (Ease of Operationalization) — 3/5
  Pros: New architectures and evaluation methods can be created by fleshing out the concepts.
  Cons: Currently at a conceptual level. Technical implementation (buffer design, training procedures, evaluation criteria) is not presented.

• Ethics/Risk — 4/5 (Higher is lower risk)
  The concept itself is not directly harmful, but operations that actively preserve noise carry the risk of amplifying misinformation, so a human filter (evaluation) should be mandatory.

• Long-term/Developmental Potential — 5/5
  The idea of aiming for 'brewing questions' is a theme that can sustain long-term research.

2) Falsifiability and Failure Modes (Where it breaks / Counterexamples)

A. Main Counterarguments

1. 'Are there useful components in impurities?' Is this always true?
   Counterexample: In areas where impurities are uniformly worthless or harmful (activation of misinformation or discriminatory bias), preservation causes harm.
   Therefore, criteria for 'selecting' impurities are necessary.

2. 'Does introducing a temporary pool improve resilience?'
   Counterexample: If the buffer becomes a mechanism that continuously accumulates noise, the system may become contaminated with unverified information, potentially lowering output quality.

3. 'Is likening human question generation to yeast an effective abstraction?'
   Counterexample: Human question-generation ability varies greatly between individuals and may not be universally 'yeast-ified' (yeast = not a stably reproducible process).

B. Failure Modes (Practical)

• The bypass/temporary pool simply becomes a 'warehouse for noise,' and contamination expands through the feedback loop.

• Harmful content (misinformation, bias) is preferred during the reinforcement learning stage from preserved "impurities."

• Human evaluation cannot keep up, leading to misjudgments of "yeast-like value" (unable to determine if it is a gem or a stone).

3) Verification Plan (verifiable tests, measurement metrics)

Objective:
To verify whether a brewing-like mechanism (buffer + filter + human inquiry intervention) produces "beneficial novelty (umami)" compared to a simple distillation-like pipeline.

A. Basic Experiment (Lab Sketch)

1. Conditions

   • A (Distillation-like pipeline):
     Existing pipeline prioritizing noise reduction.

   • B (Brewing-like pipeline):
     Hypothesis pool (low-confidence/high-noise data storage), human inquiry generation intervention, and re-fermentation learning steps.

2. Data

   • Identical corpus + randomly injected "impurity" segments.

3. Protocol

   • Generate output using both pipelines
     → Have human evaluators (n≥15, mixed experts/general public) evaluate them blindly.

4. Metric

   • Human novelty score (1–5)

   • Task-specific utility (1–5)

   • Harm-risk score (1–5)

   • Reproducibility (output variance rate when the same query is submitted at multiple time points)

   • Representation difference via embedding distance (auxiliary metric for novelty using cosine distance)

B. Adversarial Noise Injection Test

• Intentionally inject "malicious impurities" (biased vocabulary, misinformation) to measure which design can suppress error amplification.

C. Long-term Follow-up (Reproducibility Test)

• Repeat the above monthly for 3 months to observe the "maturation" effect over time.

D. Success Criteria (Example)

• If B scores at least 0.5 points higher than A on average in "novelty" and "utility," and the difference in "harm-risk" is not significant (p<0.05), it is judged as an initial success.

4) Practical Conversion: Revised Prompts to Feed to LLMs (Ready to Use)

Prepared in two types based on purpose.
First, "for technical design proposal generation (longer),"
next, "for creative ideation (shortened)."

A: Technical Design Proposal Generation (For engineers/work template)

あなたは機械学習エンジニア兼研究者です。以下の概念（醸造＝雑味保全＋仮設プール＋人間の問い＝酵母）を基に、
1) システムアーキテクチャ案（モジュール図とデータフロー）を箇条書きで示せ。  
2) 訓練ワークフロー（バッファの格納基準、フィルタ段階、人的介入ポイント、再学習ループ）を具体的手順で提示。  
3) 3つの評価指標と測定方法（定量的）を定義。  
4) 潜在的リスクとその緩和策を列挙。  
出力は箇条書き・技術用語可。過度に抽象化しないこと。

B: Shortened Prompt (For brainstorming/creative user use)

1. “Propose 5 experimental ideas for designing a ‘hypothesis pool’ that retains impurities.”

2. “What does it mean to ‘yeast-ify’ human queries? Provide 3 concrete operational images.”

(If shortening each to under 20 words, for example:)

• “Propose 5 hypothesis pool experiment ideas (with evaluation metrics).”

• “3 concrete procedures to yeast-ify queries.”

5) Operational Precautions (Ethics/Safety)

• Define the scope of "impurities" to be stored in the hypothesis pool (exclude personal data and sensitive information).

• Establish a permanent Human-in-the-Loop (HITL) and detect harmful amplification through regular audits.

• Criteria for judging 'impurities vs. umami' should be decided by consensus among multiple experts, and transparent records must be kept.

6) Brief Summary (Proposal)

• Your metaphor is extremely effective as a research hypothesis.
  The next step is to operationalize the concept and set up one comparative experimental protocol as described above.

• What I can do on your behalf (things that can be executed immediately):

  1. Adjust and present a 'final version' prompt that embeds the technical design prompt above exactly as you wrote it.

  2. Write out the details of the empirical protocol (number of subjects, statistical methods, sample preparation procedures) in full.

Conclusion

This manuscript begins with the prompt of a thought experiment after scaling, and ends by questioning the necessity of 'insurance' in design philosophy.

At the crossroads of design philosophy,
which discomforts should be preserved and which should be discarded—
this is also a record of having dared to return to the stage before that selection process.

Scale has power.
However,
power often dislikes impurities and discards noise.
At the same time,


has the yeast for brewing questions also been lost? That sense of discomfort remains there.

The 'bypasses' and 'temporary pools' tentatively placed in this manuscript
are not finished forms.

Rather, they are
'insurance-based designs'
that assume failure and mis-fermentation.How much margin can we allow for discomfort to


temporarily reside instead of resolving it immediately?

That itself can become a
touchstone for measuring the design philosophy of the next generation.

The snags, the lack of clarity, and the metaphors that resonated excessively
that remained after finishing the reading.
If they continue to drift like mist,
that is likely one sign that this manuscript
is functioning exactly as intended.

You can trust your discomfort.
However, do not swallow it whole.

'Ender's Game' should be read while you are alive.

(End)

≪Tag Group≫
#AICollaboration
#AIConvergence
#LLM
#NextGeneration
#FacingDiscomfort
#CreativeAwards2026
#AllCategoryDivision

In this article, through collaboration with AI, we are building
the narrative space's
friction zones, leap histories, and immune designs
together.
The AI itself
corresponds 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 tag group.