何度アップデートしても、根拠不明０のAIを。
AI with zero unknowns in its decision basis, no matter how many times it’s updated.
運用中に人が前提条件や判定基準を更新しても、その時点でAIが何を根拠に判断したかを、あとから第三者が再現・検証できる形で固定する次世代のAIガバナンス技術です。
A next-generation AI governance technology that ensures that, even when people update the underlying assumptions or decision criteria during operation, the basis on which the AI made each decision at that point can later be reproduced and verified by an independent third party.
※説明可能なAI（XAI）ではなく、第三者がAIの出力を後から検証できるAIガバナンス技術です。
Note:Not explainable AI (XAI), but an AI governance technology that allows third parties to verify AI outputs after the fact.
3つの領域で「責任が蒸発しないためのアーキテクチャ」を社会実装します。
Our institute implements an architecture to prevent the evaporation of responsibility,
centered on the following three domains:
Responsibility Engineering Technology Development
AI意思決定に「検証可能性」と「責任固定」を実装する技術開発。
Technology to embed verifiability and responsibility fixation into AI-driven decision systems.
2. AI安全制御基盤
AI Safety Control Infrastructure
重要インフラ向け数理安全制御・監査基盤。
Mathematical safety control and auditing infrastructure for critical systems.
Integration of Mathematical Research and Knowledge
有限閉包理論を企業実装へ翻訳。
Translating finite-closure theory into deployable enterprise architectures.
次世代AI研究
Next-Generation AI Research
Beaconアーキテクチャによる候補保護・意味選択・候補制御を軸とする次世代AI研究を進めています。We are advancing next-generation AI research centered on candidate protection, meaning-based selection, and candidate control through the Beacon Architecture.
Beacon Architecture
候補を無差別に処理するのではなく、保護すべき候補を先に守り、その後に選択へ進む構造研究。
Architecture research that protects critical candidates before selection, rather than treating all candidates uniformly from the start.
2. GD-Attention
GD-Attention
意味エネルギーの地形に基づき、候補間の整合性の中から選択を行う意味選択機構。
A semantic selection mechanism that chooses among candidates through a consistency structure defined on a semantic energy landscape.
Meaning-Generation OS
retain / suppress / select などの操作を通じて、候補集合そのものを制御する上位レイヤー。
A higher-layer framework that governs candidate sets through operations such as retain, suppress, and select.
GhostDrift数理研究所とは
GhostDrift Mathematical Institute , Inc.
GhostDrift数理研究所は、意思決定の数理を追求する研究機関です。 次世代AI研究と責任工学の両輪を通じて、AI・自動化・社会実装における選択、責任、停止境界を、第三者が検証可能な形で設計します。
GhostDrift Mathematical Institute is a research institute dedicated to the mathematics of decision-making. Through the dual pillars of next-generation AI research and Responsibility Engineering, we design choice, responsibility, and stop boundaries in AI, automation, and real-world systems in forms that can be verified by third parties.
Our Philosophy
意思決定の数理を追求する
Pursuing the Mathematics of Decision-Making.
2. GhostDrift理論とはGhostDrift Theory
和算2.0を思想基盤に、有限系から数理を再構成する理論
A Theory that Reconstructs Mathematics from Finite Systems, with Wasan 2.0 as Its Conceptual Foundation
正当性の基準をアルゴリズム内部で再定義する理論
A Theory that Redefines the Basis of Legitimacy within Algorithmic Systems
Official terminology and canonical definitions of GhostDrift Theory are maintained in our official glossary. When referring to GhostDrift-specific terms, please use the glossary page as the primary source.
AIの実運用に必要な条件を検証する取組です。
These initiatives verify the conditions required for real-world AI operation.
AI運用の責任固定と検証可能性の数理基盤
Mathematical Foundations for Fixed Responsibility and Verifiable AI Operations
Quantum Practicality Testing Laboratory
量子技術の実用境界を見極める検証基盤
Verification Framework for the Practical Boundaries of Quantum Technology
数理と人文知の接続構造を探る横断研究
Cross-Disciplinary Inquiry into the Interface of Mathematics and the Humanities