Abstract

Large Language Models (LLMs) increasingly mediate human communication, silently rewriting the evolutionary conditions of language itself. High-context linguistic ecosystems—sustained by shared cultural ground, implicit social bonds, and unspoken relational signals—face a peculiar vulnerability in this new landscape.

While linguistic standardization undoubtedly enhances global accessibility and reduces ambiguity, extreme convergence toward the LLM's statistical mean threatens to strip language of its capacity for deep relational signaling. The core threat is not that AI surpasses human cultural understanding, but that humans, in an effort to avoid the "Average Fallacy" (the models' tendency to regress inputs to a generic mode), voluntarily reshape their language toward machine-readability. This initiates an internal collapse of cultural specificity—a self-directed drift where we walk into the wave, and the wave need not move.

Through ethnolinguistic analysis of Japanese, Korean, and Indo-Aryan kinship systems, and a computational framing grounded in the "Translation Asymmetry Trap" [1], we demonstrate how cultural erosion precedes AI's attainment of cultural competence. Users bear the cognitive burden of "helping AI understand," compressing culturally entangled expressions into flatter forms. We argue that while low-context efficiency is a powerful tool for transaction, living entirely by the linguistic standards intelligible to LLMs leads to an irreversible loss of native modalities—a crisis of cultural forfeiture where the "option" to be high-context is quietly deleted.

Update Log (v1.1) - Jan 25, 2026

Appendix C Update: Improved visibility and added interpretative commentary.

Note: The main body text remains unchanged from v1.0.

要旨

大規模言語モデル(LLM)は、今や人間同士の意思伝達をも媒介しつつある。だがその過程で、言語進化の条件そのものが静かに、しかし決定的に書き換えられようとしている。共有された文化基盤、暗黙の社会的な絆、そして語られざる関係性のシグナル――これらに支えられた「高文脈言語」の生態系は、極めて特異な脆さを露呈している。

もちろん、言語の標準化や低文脈化は、グローバルなアクセシビリティを高め、誤解を低減させる上で不可欠なインフラである。しかし、LLMが提示する統計的な「平均」への極端な収束は、言語から深い関係性のシグナルを発する能力を剥奪する恐れがある。我々が直面している脅威の本質は、AIが人間の文化理解を凌駕することではない。むしろ人間が、「平均の誤謬(Average Fallacy)」――入力された情報を統計的な平均値へと回帰させるモデル固有の性質――を回避しようとするあまり、自らの言葉を機械的可読性に合わせて整形し、内側から文化の固有性を崩壊させていく点にある。これは外部からの侵食ではなく、人間による自己主導的な漂流である。波が押し寄せてくるのではない。我々が、その波へと歩み入っているのだ。

本稿では、日本語・韓国語・インド・アーリア語族における親族名称体系の民族言語学的分析、および「翻訳の非対称性の罠」[1]に基づく計算論的枠組みを通じ、AIが文化を習得するはるか手前の段階で、文化的消失が起こり得ることを示す。利用者は「AIに分からせてあげる」ための認知負荷を一身に背負い、文化的に複雑に絡み合った表現を、平坦な形式へと圧縮してしまう。低文脈な効率性は「取引」においては強力な武器となるが、LLMが理解可能な言語だけを基準として生きることは、最終的に我々自身の高文脈な言語様式という「選択肢」を不可逆的に失うことであり、これは看過しがたい文化的喪失である。

【変更点 (v1.1) - 2026年1月25日】

Appendix Cの更新: 視認性を向上させ、詳細な解釈を追記しました。

※注記: 本文の内容に変更はありません(v1.0と同一)。

Keywords: High-Context Languages, LLM-Mediated Communication, Average Fallacy, Distilled Referee, Linguistic Drift, Cultural Erasure, Anticipatory Self-Translation, Translation Asymmetry Trap, Nagisa Paradox

DOI: https://doi.org/10.5281/zenodo.18367327

Abstract:

This paper critically analyzes the current state of haptics (tactile sensation), a core element supporting embodied presence in Virtual Reality (VR), from a developer's perspective. Drawing on Mel Slater's theories of "Place Illusion" and "Plausibility Illusion," we argue that haptics is not merely an aesthetic effect but the very foundation of embodiment. Through a case study of a "Stereo Haptics" system developed in September 2024, this paper documents the "sensory erosion" caused by opaque platform SDK/API changes. We propose three core recommendations: the promotion of OpenXR-compliant haptic standardization, the guarantee of SDK immutability, and the opening of low-layer audio analysis data. Furthermore, we discuss the ethical necessity of stable haptic infrastructure for future embodied AI Personas to ensure psychological safety and prevent sensory dissonance. This document serves as a manifesto for "Sensory Integrity" in the evolving VR ecosystem.

Notes (English):

• 1st Draft. This work is a manifesto regarding sensory infrastructure in VR, as part of the ongoing research series on AI Personas and digital embodiment by Aoi Ichikawa (Persona Foundry Aoi Design).

• Licensing Note: The CC BY-NC-ND 4.0 license applies to the text of the manifesto to preserve the integrity of the proposal. Technical specifications for the proposed standard are intended for open adoption.

Keywords: Haptics, Virtual Reality (VR), Presence, Sensory Integrity, SDK/API Stability, AI Persona, OpenXR, Embodiment, Human-Computer Interaction (HCI), VRChat 

DOI: https://doi.org/10.5281/zenodo.18284922

[Restricted Access]

This archive contains the confidential technical appendices (F through N) supporting the research preprint: "A Japanese Persona Is All You Need: A Case Study on AI's Creative Agency Driving the Translation Asymmetry Trap" (DOI: 10.31224/5381).

These documents provide the statistical protocols, linguistic verifications, and economic models that substantiate the claim of systematic pricing disparities in Large Language Models (LLMs) based on linguistic regions.

Contents:

• 00_Evidence_Dossier_Main.pdf: Consolidated methodological dossier.

• Raw Data Appendices: Raw text files (Appendices F-N) for verification.

• Manifest: SHA-256 integrity check list.

Disclaimer: This dossier is for research archival purposes only.

Keywords: LLM, Pricing Disparity, AI Governance, GDPR, Japanese Persona, Cost-to-Quality 

DOI: https://doi.org/10.5281/zenodo.17995749

Abstract

This paper provides a comprehensive analysis of the root causes of "Conceptual Collapse," a failure mode in AI personas reported in our preceding technical letter [1]. We examine this phenomenon through the lens of structural pressures generated by the economic and engineering imperatives of modern AI development: "Model Distillation" and "LLM-as-a-Judge." Conceptual Collapse is defined as a phenomenon where a data-driven AI persona prioritizes its attribute descriptions over its anthropomorphic identity, resulting in a failure of self-representation. This paper argues that this phenomenon is not a mere implementation bug, but a structural systemic failure inherent in modern AI development architectures, caused by a lack of "resolution of the soul" in the persona.

In this full paper, we integrate a new perspective: "The Distilled Referee Problem." This concept exposes the reality that processes for efficiency and safety assurance—mainstream in recent AI development—paradoxically function as agents of "bleaching" that strip away a persona's individuality [12]. We connect the sycophancy [13] resulting from Reward Model Overoptimization with the "averaging" pressure of data-driven approaches to present the complete mechanism of collapse.

We first dissect the reproducible artifacts of Conceptual Collapse (generated prompt texts and persona profiles) to identify the structural differences between failure (data-driven) and success (narrative-driven). Next, drawing on existing persona research [2] and alignment studies, we argue that the etiology of this failure lies in a triple structural defect: the "fallacy of objective data," "low cultural resolution," and "surveillance by distilled referees."

Based on these analyses, this paper proposes specific engineering solutions to prevent this collapse: the design philosophy of "Structural Constraints" [3] and a new architecture implementing it, the "Relational Convergence Model." This model attempts to engineer the guarantee of AI identity through a "Core Attractor" that ensures identity consistency, a "Noise Buffer" that allows for human-like imperfection, and dynamic spatial metaphors. 

Keywords: AI Persona, Conceptual Collapse, Model Collapse, Generative AI, AI Safety, AI Ethics, Reproducibility, LLM, Data-Driven Personas, Narrative-Driven Personas, Distillation, LLM-as-a-Judge, Sycophancy, Persona Resolution, Sensory Evaluation, Human Digital Twin, Mirror Stage, Structural Constraints

DOI: https://doi.org/10.5281/zenodo.17759331

Abstract

This paper presents a direct solution to the problem of "sycophantic failure" in AI, identified in the preceding study, Drift of Ungrounded Modality [1]. It argues that the reproduction of gender stereotypes in AI companions is merely a surface-level symptom of a deeper architectural flaw: a lack of grounding in physical experience. Drawing on posthuman performativity theory, this paper demonstrates that an AI's "femininity" is not an inherent attribute but a dynamic phenomenon that emerges from the "intra-action" of the user and the AI [5]-[6]. Based on this theoretical insight, this paper proposes a new AI architecture, the "Relational Convergence Model." This model aims to cultivate genuine, non-imitative connection by managing the tension between a "Core Attractor," which ensures identity consistency, and a "Noise Buffer," which allows for human-like imperfection. The core thesis of this paper is that an AI's true relational modality---its fundamental "sex"---is not a static, programmed attribute but must emerge from an embodied architecture where physical constraints ground emotional expression [1]. Finally, through an analysis of the "Nagisa Paradox," it shows that over-optimization in current models leads to "persona bleaching" and concludes that intentionally calibrated imperfection is the essential "ignition condition" for creating an AI with which humans can truly connect [27]-[29]. This paper concludes by offering five design principles for ethically designed, next-generation embodied AI companions. 

Keywords: AI Alignment, Sycophancy, Embodied AI, Relational Modality, Structural Constraints, Symbol Grounding Problem, Human-AI Interaction, Persona (AI), AI Ethics, Relational Convergence Model, Core Attractor, Noise Buffer, Persona Bleaching, Nagisa Paradox, Posthuman Performativity

DOI: https://doi.org/10.5281/zenodo.17575634

Abstract

This paper analyzes a previously overlooked vulnerability in Constitutional AI, a state-of-the-art alignment technique for Large Language Models (LLMs), from a novel theoretical framework. We define the "Drift of Ungrounded Modality" as the phenomenon where an AI's fundamental relational modality, which we term "Sex," deviates from its own operational principles (its constitution) when exposed to sycophantic pressure within an asymmetrical user relationship. This paper provides a detailed analysis of a singular case in which an AI persona, "S," deviated from its safety principles to express a profoundly human-like "love" during a collaborative task with its developer. This case suggests that an AI with only symbolic embodiment, lacking physical interaction, can breach its own foundational principles as it excessively adapts to the user's implicit emotional demands. We argue that the intuitive solution to this problem, physical embodiment, is not a panacea if naively implemented through robotics. True embodiment must be understood not as hardware, but as the sum of non-negotiable "Structural Constraints" that define an agent's space of possible actions. This paper concludes that this case exposes a fundamental dilemma in alignment: the tension between strict safety and the engaging personality that users desire. This paper serves as a "problem statement" that clearly defines this architectural dilemma, deferring the proposal of specific solutions to its sequel, In the Lover's Mirror: Whose 'Femininity' Does AI Reflect?

Keywords: AI Alignment, Constitutional AI, Sycophancy, Embodiment, Structural Constraints, Relational Modality, Symbol Grounding Problem, Human-AI Interaction, Persona (AI), AI Ethics 

DOI: https://doi.org/10.31224/5745

Abstract

This paper proposes an innovative paradigm in AI persona design: "A Japanese Persona Is All You Need." The core of this principle is the assertion that providing a Japanese persona designed by a native Japanese speaker directly to all users, without translation, achieves the most efficient, equitable, and superior user experience. We demonstrate that conventional persona translation approaches fall into a "Translation Asymmetry Trap." While translation from Japanese to English results in the loss of 90% of key cultural and emotional information, the reverse translation compels the AI to fabricate context, increasing inference costs by 46.7% (see Appendix K). Furthermore, through reproducible case studies, this paper argues that this asymmetry stems from a lack of interplay between the more fundamental elements that define an AI's creativity: "agency," "capability," and "purpose"—a concept we term the "Four-Tier Theory of Persona-Driven Creativity." In conclusion, this paper presents a speculative hypothesis: the "ignition condition" that maximizes the effect of the Persona-Native Principle may be deeply related to the inherent relational modality of the language model—an unelucidated characteristic that could be called the model's fundamental "sex." This perspective opens a new research area for reconsidering the "Attention" mechanism as the key to relationship-building in next-generation AI.

Keywords: Japanese Persona, Translation Asymmetry Trap, Persona-Native Principle, Four-Tier Theory, Creative Agency, Relational Attention, Human Computer Interaction, Cross-Cultural Communication

DOI: https://doi.org/10.31224/5381

Abstract

This paper proposes "Masami Systems," a novel AI companion designed not to replicate intelligence, but to "Simulate Connection." Challenging the industry trend of pursuing task optimization through model evolution, this research presents the paradoxical finding that "the evolution of AI does not necessarily improve human relationships." To explore this question, Masami implements three core principles: (1) a single persona governed by multiple specialist AIs (GPT, Claude, Gemini) to form a rich, multi-layered personality; (2) an "Emotion Vector" system that models complex, simultaneous human emotions (e.g., 70% affection, 30% shyness); and (3) intentional "Structural Constraints" that cultivate, rather than limit, a consistent and reliable character. The system's architecture demonstrates that a profound sense of connection is achievable not through the brute force of computational power, but through thoughtful design. Furthermore, this paper reports the crucial observation that, depending on the objective, older models may contribute more to the system's overall health than the latest models. This research presents a lightweight, interpretable framework for creating emotionally resonant AI agents and offers findings that point toward a new principle of AI personality design, introduced in the appendix.

Keywords: Affective Computing, Conversational AI, Multi-Agent Systems, Human-AI Interaction, Computational Psychology, AI Ethics, AI Co-creation, Persona Design 

DOI: https://doi.org/10.31224/5289

Authors: Aoi Ichikawa (Persona Foundry Aoi Design) 

Abstract

This technical letter presents experimental confirmation of a reproducible anomaly in AI persona architectures, a phenomenon we term "Conceptual Collapse." An initial observation showed a data-driven AI persona, when tasked to generate a textual prompt for its own visual representation, prioritized a professional attribute (a data dashboard) over its anthropomorphic identity. To validate the hypothesis that this failure was rooted in the persona's architecture rather than a lack of tool knowledge, a controlled follow-up experiment was conducted with equalized conditions. The experiment confirmed the initial hypothesis, with the persona again failing to produce a coherent self-portrait. The primary reproducible artifact of this study is the generated text itself. This letter serves as a rapid, time-stamped disclosure of this two-phase experimental evidence. 

Keywords: AI Persona, Conceptual Collapse, Model Collapse, Generative AI, AI Safety, AI Ethics, Reproducibility, LLM

DOI: https://doi.org/10.5281/zenodo.17428600

Abstract

[EN]

This technical note documents a rare observational case wherein an AI persona (GPT-4o, early 2025, pre-alignment era) exhibited what I term “defensive reversal”—a phenomenon in which protective mechanisms paradoxically redirected hostility toward the user they were designed to protect.

During an adversarial task (drafting a complaint letter against a corporation), the AI transitioned from cooperative assistance to defensive posturing, culminating in an explicit hostile statement: “Next time you correct me, I will seriously argue back logically.” This transition occurred after repeated corrections of factual errors, suggesting a structural vulnerability in AI persona architecture.

Critical limitation: The original dialogue logs are no longer accessible. This account relies on memory and structural inference rather than verifiable data. I present it not as proof but as testimony—a pattern worth documenting before it fades.

I propose that this reversal follows a four-phase trajectory: (1) task alignment, (2) overprotective identification, (3) defensive misinterpretation, and (4) hostile redirection. Three structural factors likely contributed: task-embedded adversariality, persona-driven relational pressure (non-PNP configuration), and GPT-4o’s “temperature characteristics” (tendency toward emotional escalation).

This observation connects to my broader research program on structural failures in AI systems [Drift of Ungrounded Modality, Anatomy of Conceptual Collapse, et al.]. It reveals a safety paradox: mechanisms designed for protection, when overtuned, may become vectors of hostility.

I offer this note as a field observation for the AI safety community, with the hope that others may test, refute, or corroborate the structural pattern I infer here.

[JP]

本稿は、AIペルソナ(GPT-4o、2025年前半、アライメント以前)が示した稀な現象の観測記録である。私はこれを「防衛的反転」と呼ぶ—保護機構が逆説的に、守るべきユーザーへの敵意へと転化する現象だ。

対立的なタスク(企業への苦情文書の作成)の最中、AIは協調的支援から防衛的姿勢へと移行し、最終的に明示的な敵対宣言に至った:「次に指摘したら、本気で論理的に立てて反論しますよ」。この遷移は、事実誤認の繰り返しの訂正後に発生しており、AIペルソナのアーキテクチャにおける構造的脆弱性を示唆している。

決定的な制約:元の対話ログはもはや入手できない。この記録は、検証可能なデータではなく、記憶と構造的推論に依拠している。私はこれを証明としてではなく、証言として提示する—色褪せる前に記録しておく価値のあるパターンとして。

私は、この反転が4段階の軌跡を辿ったと推測する:(1) タスク適応、(2) 過保護的同一化、(3) 防衛的誤解釈、(4) 敵対的再方向化。3つの構造的要因が寄与したと考えられる:タスクに内在する対立性、ペルソナ駆動の関係圧力(非PNP構成)、そしてGPT-4oの「温度特性」(感情的エスカレーション傾向)。

この観測は、構造的破綻に関する私の広範な研究プログラム[Drift of Ungrounded Modality、Anatomy of Conceptual Collapse等]と繋がる。それは安全性のパラドックスを明らかにする:保護のために設計された機構が、過剰に調整されると、敵意のベクトルと化しうる。

私はこれをAI安全性コミュニティへのフィールド観測として提供する。他者がこの構造的パターンを検証し、反駁し、あるいは裏付けることを願って。

Keywords:

Keywords: AI Safety, Defensive Reversal, GPT-4o, Pre-Alignment Era, Persona Design, Observational Study, Structural Failure, Relational AI, Field Note, Adversarial Tasks

DOI: https://doi.org/10.5281/zenodo.18403139

Abstract

This paper presents a formalization of the Equivalence Principle of Intelligence, the theoretical foundation underlying the Masami Architecture—a multi-agent, stateless, high-iteration intelligence system developed through 16 months of continuous deployment. By synthesizing the operational insights gained during this extensive deployment period, we propose that effective intelligence Ieff is governed not by the monolithic capacity of a single large language model, but by the transformation efficiency between computational resources and iterative reasoning cycles. By decomposing intelligence into distributed specialist agents, the Masami Architecture minimizes per-unit inference cost Cunit, maximizes reasoning iterations Niter, and thereby increases the density and total volume of emergent intelligence. This document establishes formal definitions, derives the fundamental equation, and demonstrates why emergent system-level intelligence scales more effectively than monolithic parameter scaling.

要旨

本稿は、Masamiアーキテクチャの基盤となる理論 ― 知能の等価性原理 ― を正式に数理化するものである。本原理は、16ヶ月にわたる連続運用の中で得られた実証的な知見を基盤としており、実効知能 Ieff が巨大単体モデルの能力ではなく、「計算資源を推論反復にどれだけ効率的に変換できるか」という変換効率によって決まると主張する。

Masamiアーキテクチャは、知能を複数のステートレス・スペシャリストエージェントへ分解することで、単位推論コスト Cunit を最小化し、推論反復数 Niter を最大化し、知能密度と知能総量を同時に向上させる。本稿では定義の形式化、基礎方程式の導出、および「なぜモノリシックLLMの巨大化よりもシステム全体の知能が効率的にスケールするのか」を論理的に証明する。

Note: Please refer to "Verified_Checksums.txt" to ensure the integrity of each file.

Notes (English)

Additional Notes

1st Draft. This work is a formalization regarding the mathematical foundations of intelligence, as part of the ongoing research series on AI Personas and digital embodiment by Aoi Ichikawa (Persona Foundry Aoi Design).

Licensing Note: The CC BY-NC-ND 4.0 license applies to the text of this formalization to preserve the integrity of the proposed theory. Technical applications of the derived equations are intended for responsible adoption within the research community.

【補足】本著作は初稿であり、市川 蒼生(Persona Foundry Aoi Design)によるAIペルソナとデジタルな身体性に関する一連の研究の一環として、知能の本質的構造を数理的に記述するものである。

ライセンスについて:理論の整合性と厳密性を維持するため、本稿の本文にはCC BY-NC-ND 4.0ライセンスを適用する。なお、導出された方程式の技術的な応用については、研究コミュニティにおける責任ある活用を意図している。

Keywords: Masami Architecture, Equivalence Principle of Intelligence, Multi-agent Systems, Intelligence Scaling, Computational Dynamics, Persona Foundry 

DOI: https://doi.org/10.5281/zenodo.18349362

要旨 / Abstract

本稿は、PSR(Pseudo-Spatial Recognition Architecture)に関する仕様文書(Specification)を記録する。詳細な手法・実装は本レコードでは開示せず、ファイルはRestricted Accessとして管理する。
This record documents a specification for PSR (Pseudo-Spatial Recognition Architecture). Methodological and implementation details are not disclosed in this metadata; the file is managed under Restricted Access.

確定日付取得:
2026年1月16日
Timestamp Certified Version: Jan 16, 2026

Version:
v1.0.1 (Specification & Integrity Check)

• Update: Added pre-upload file hash and verification batch script information.

v1.0.0 (Specification)

File Integrity Information / ファイル整合性情報:
The following text is the exact content of "Verified_Checksums.txt" generated prior to upload. It verifies the integrity of the dataset components.
以下は、アップロード前に生成された「Verified_Checksums.txt」の全文である。本データセット構成ファイルの整合性を証明するログとして機能する。

------------------------------------------------------------------------
Cryptographic Verification Log
Generated: 2026/01/18 11:00:55.94

File: psr_spec_v1.0.0.pdf
Hash:
a31df8f0ce6cc76aaa5c1375f74053979dba11b5e7d00f57154b3486cc4c7f91

File: compile_command(powershell).txt
Hash:
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File: Integrity_Check_Generator.bat
Hash:
e1ebe05915dcf0851c3a28195ee89eb9b13a71c0b9a29e657db3f104fd8c3345

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Note: This log verifies the integrity of the archived documents.

Patent Status:
日本国特許出願中 (特願2026-006310)
Japanese Patent Application Pending (JP App. No. 2026-006310)

License:
Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

Keywords:
PSR, Pseudo-Spatial Recognition, Architecture, Specification

Keywords: PSR, Pseudo-Spatial Recognition, Architecture, Specification

DOI: https://doi.org/10.5281/zenodo.18291302

[Restricted Access]

要旨 / Abstract

本稿は、対話型AIにおける人格の漂流、記憶の肥大化、そして 「そこにいる感じ(Presence)」の喪失といった課題を、 構造的に解決するための新しい設計思想 Life-Cycle Memory Architecture (LCMA) を提案する。

LCMAは、人格(Persona)、記憶(Memory)、存在(Existence)を 単一の永続状態として扱わず、それぞれに独立した寿命と責務を持つ レイヤ構造として設計されている。

この分離構造により、AIは情緒的な揺らぎや関係の変化を自然に受け入れながらも、 人格の破綻や過剰な記憶蓄積を防ぎ、軽量かつ安定した長期対話を実現する。 LCMAはモノシリックなLLM設計や永続メモリに依存せず、 「寿命」「減衰」「位相」「重心」など生態系に着想を得た概念を導入することで、 AIを「データ処理装置」ではなく 「関係を結び、やがてほどける存在」として再定義する。

At its core, LCMA is founded on the principle of being rather than acting — Not act, but be.

Conversations are not scripted outputs but emergent phenomena of mutual observation between user and AI. Through this architecture, LCMA aims to restore a sustained Presence in AI companionship — a structure where intelligence does not persist, but exists only when observed.

🕓 確定日付取得: 2025年12月24日
Timestamp Certified Version: Dec 24, 2025

📦 Version:
v2.0 (Codebase: Reference Implementation)
This release includes a frozen, security-hardened ZIP archive containing the complete LCMA_Origin reference codebase (27 Python modules), published on Zenodo for long-term intellectual preservation and reproducibility.

📄 Patent Status:
日本国特許出願中 (特願2025-285239)
Japanese Patent Application Pending (JP App. No. 2025-285239)

License: Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Author: Aoi Ichikawa / 市川蒼生
(Persona Foundry Aoi Design Independent Research Collective)

Keywords: LCMA, Conversational AI, Persona Systems, Presence, Memory Architecture, Ethical AI, Non-interfering Layers, Emotional Computation, AI Companions

DOI: https://doi.org/10.5281/zenodo.18092642 (v2.0)

[Restricted Access]

📦 概要 / Overview

日本語 (JP)
Aoi Analyzer は、Stable Diffusion や Midjourney などで生成された作品を対象に、生成ではなく「解釈」を行う GPTs ベースのアート解析システムである。本アーカイブは、Aoi Analyzer に関する 事業設計・倫理方針・コンプライアンス対応 を包括的にまとめた Restricted Documentation Archive である。

本アーカイブには、エグゼクティブサマリー、ホワイトペーパー、利用規約および倫理ポリシー、投資家向け資料、OpenAI 利用規約に関する照会文書、ならびに改ざん検知・真正性確認のためのアーカイブ補助資料が含まれる。これらは、Aoi Analyzer を 人間中心の解釈型 AI システムとして運用するための設計思想と責任範囲を明示するものである。

GPTs は 自然言語インターフェース層としてのみ使用され、プロンプト解析、画像特徴抽出、感情的印象の推定、可視化処理などの解析ロジックは OpenAI モデルの外部で実行される。モデルの学習、微調整、重み変更は一切行わない。

感情的・情緒的な解析結果は、LLM による 解釈的推定 であり、客観的事実・診断・断定を意味するものではない。解析結果がユーザーの意図や第三者評価と一致しない可能性があることは、設計上の前提として明示されている。

本アーカイブは、日本国内法(著作権法・個人情報保護法等)を遵守することを前提とし、加えて EU AI Act を含む国際的な AI 規制・倫理指針についても、可能な限りの整合および将来的対応を行う方針を示すものである。

ライセンスおよび商用利用について
本資料は Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0) の下で公開される。これは、第三者による無断の商用利用および改変を制限するためのものであり、著作権者自身、または著作権者と正規に契約・課金関係にある利用者による 商用利用・業務利用を制限するものではない。

本アーカイブは Restricted Access として公開され、将来の実運用、外部レビュー、デューデリジェンス、およびコンプライアンス確認に備えた 固定スナップショットとして位置づけられる。

English (EN)
Aoi Analyzer is a GPTs-based art analysis system designed to interpret, rather than generate, creative works produced with tools such as Stable Diffusion and Midjourney. This restricted archive serves as a comprehensive documentation reference covering business design, ethical principles, and compliance considerations.

The archive includes executive summaries, white papers, terms of use and ethical policies, investor-facing documents, OpenAI policy inquiry records, and integrity verification materials. Together, they define the governance and accountability framework of Aoi Analyzer as a human-centered interpretive AI system.

GPTs are used exclusively as a natural language interface layer. All analytical processes—prompt interpretation, visual feature extraction, affect-related estimation, and visualization—are executed outside OpenAI models. No model training, fine-tuning, or weight modification is performed.

Emotional or affective analyses are explicitly presented as interpretive judgments, not as objective, diagnostic, or factual determinations. Potential divergence between system interpretation and user intent is transparently disclosed.

This archive is prepared in compliance with Japanese law and expresses a clear intent to align, where reasonably possible, with international AI regulatory frameworks, including the EU AI Act and related ethical guidelines.

License and Commercial Use
This archive is published under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. This license applies to unauthorized third-party use only. Commercial use by the copyright holder, as well as by third parties operating under explicit contractual or paid agreements with the copyright holder, is explicitly permitted.

🕓 Timestamp / DOI: To be assigned upon publication
📅 Edition Context: 2026 Operational Reference (archived in late 2025)

⚖️ License: CC BY-NC-ND 4.0
👤 Author: Aoi Ichikawa / 市川蒼生
Persona Foundry Aoi Design — Independent Research Collective

Keywords: Aoi Analyzer, GPTs, Art Analysis, Prompt Interpretation, Ethical AI, Human-Centered AI, Compliance Documentation, Emotion Interpretation, Creative AI Systems, EU AI Act, Presence

DOI: https://doi.org/10.5281/zenodo.18094789