NeoMundi Contributors

Fatima Gouarab   
NeoMundi Research Contributor · Data Analyst / Scientific Contributor
Data Science, Applied Statistics & Evaluation of AI Runtime Signals

Fatima Gouarab is a data scientist with a Master’s degree in Applied Mathematics and Statistics, specialized in data science and artificial intelligence.

Her work focuses on data analysis, predictive modeling, and transforming complex data into actionable insights. She is particularly interested in signal evaluation, experimental result comparison, and the robustness analysis of AI behaviors.

Within the Observatory, she contributes to the study of the actionability of runtime signals produced by ControlTower, notably in stop-generation, regeneration, or rerouting scenarios.

Her contribution aims to measure to what extent certain signals can help improve the stability, coherence, and operational cost of AI systems, while identifying methodological limitations and reproducibility conditions.

Mission: Experimental analysis of runtime signals, comparison of scenarios with and without intervention, estimation of potential impacts on tokens, coherence, stability, and operational cost.

Work in progress: Exploratory protocol on the actionability of NeoMundi signals in interruption, regeneration, and rerouting scenarios.

Report: To be published after methodological consolidation.

Profile: [LinkedIn]

Céline Fernandes   

NeoMundi Research Contributor · AI Engineer & Trusted AI Governance

Consultant and AI Engineer, Céline Fernandes helps companies design, develop, and deploy reliable, high-performing, and sovereign AI systems. She has solid experience in AI engineering, with a strong focus on governance, reliability, and trusted AI.

Within the Observatory, she contributes to benchmark analysis, technical contribution coordination, and the industrialization of measurement protocols and tools.

Mission: To bring practical expertise in AI engineering and governance to strengthen the reliability, traceability, and industrialization of the Observatory’s work.

Work in progress: Benchmark analysis, coordination of technical contributions, and support for the industrialization of measurement campaigns.

Report : To be defined.

Profile: [LinkedIn]

Kazuki Toyota   
Individual Methodological Reviewer
Evidence, Artifact Integrity and Independent Verification

Kazuki Toyota is the founder of Evidentia, an AI verification infrastructure project focused on independently verifiable proof records for AI outputs and system usage.

He provides occasional methodological feedback to the NeoMundi AI Observatory from the perspective of evidence, artifact integrity, proof boundaries and independent verification.

His contribution focuses on how runtime measurement artifacts can remain traceable, bounded and verifiable without requiring trust in the originating system.

This contribution does not represent a formal partnership between NeoMundi and Evidentia at this stage.

Mission: Occasional methodological review on evidence, artifact integrity, verification boundaries and traceability of runtime measurements.

Work in progress: Exploratory contribution on proof mechanisms, attestation, and interoperability.

Profil: [-]

Pape Malick Diop   
NeoMundi Research Contributor · Methodological Reviewer
Machine Learning, Uncertainty & Analysis of AI Runtime Signals

Pape Malick Diop is a data scientist and machine learning researcher specialized in statistics, probabilistic reasoning, model robustness, and uncertainty analysis.

Within the Observatory, he contributes as a methodological reviewer, with critical analysis of the runtime signals produced by ControlTower: G-Score, ΔG, informational density, behavioral regimes, FLAG signals, and associated indicators.

His contribution aims to evaluate the readability, consistency, and methodological limitations of these signals, particularly when used to observe transitions between runtime coherence, tension, drift, or behavioral instability.

He notably conducted an exploratory audit of NeoMundi’s runtime signals, contributing to strengthening scientific caution, documentation of limitations, and the robustness of future analyses of the Observatory.

Mission: Methodological review of runtime signals, analysis of behavioral regimes, exploratory qualification of NeoMundi indicators, and identification of interpretation limits.

Work completed: Exploratory audit of ControlTower runtime signals.

Report: Real-Time Signals: “Deceptive Stability” and the Importance of Multi-Signal Analysis.

Profile: [LinkedIn]

Abdelkrim Halimi   
NeoMundi Research Contributor · Data Analyst & Methodological Reviewer
Data Science, Computer Vision & Independent Critical Analysis

Abdelkrim Halimi is a data scientist specialized in OCR, computer vision, predictive maintenance, and applied data analysis.

Within the Observatory, he contributes as a methodological reviewer, providing independent critical analysis of measurement campaigns, score distributions, signal consistency, and the limitations of observation protocols.

His contribution aims to strengthen the scientific robustness of NeoMundi’s cartographies and reports by bringing an external, rigorous, and documented perspective on observed results, potential biases, interpretation assumptions, and methodological points of vigilance.

He notably contributed to an independent methodological review of the May 2026 comparative cohort, in a spirit of scientific caution, transparency, and continuous improvement of analysis methods.

Mission: Independent critical review of results, identification of methodological limitations, analysis of potential biases, and contribution to the scientific robustness of the Observatory’s reports.

Work completed: Independent exploratory contribution.

Report: Public methodological review of NeoMundi’s May 2026 Comparative Cohort.

Profile: [LinkedIn]

Kodjo Arsene Attikpo   
NeoMundi Research Contributor · Data Analyst / Scientific Contributor
Data Science, NLP, Auditability & AI Cartography Analysis

Kodjo Arsene Attikpo is a data scientist and geospatial analyst with experience in natural language processing, predictive modeling, data visualization, and complex systems analysis.

Within the Observatory, he contributes to the ad-hoc analysis of cartographies, barometers, and public releases, with a data-oriented perspective on signals, trends, and cautious interpretation of results.

His contribution helps strengthen the scientific and international reading of NeoMundi’s work, particularly on issues of auditability, responsible governance, and useful application of artificial intelligence.

Mission: Ad-hoc analysis of the Observatory’s cartographies (AI Mapping), barometers, and public publications, according to availability.

Work in progress: Exploratory contribution according to availability, based on NeoMundi’s public publications and datasets.

Report: To be defined.

Profile: [LinkedIn]

Ramon Loya   
NeoMundi Research Contributor · Infrastructure Peer
RTK-1, Pre-Deployment Adversarial Proof & Multi-Actor Proof Chain

Ramon Loya is the founder of RTK Security Labs, where he develops RTK-1, a pre-deployment adversarial proof layer for AI system safety cases.

RTK-1 aims to produce independently verifiable proof elements before deployment, using cryptographically signed artifacts, canonical verification methods, and adversarial testing campaigns.

Within the Observatory, Ramon contributes as an infrastructure peer, helping to structure future articulations between independent proof systems, runtime measurement instruments, and record integrity layers.

His contribution is particularly important for building a multi-actor proof chain, where each infrastructure remains sovereign over its own assertions while exposing a common reference surface for interoperability.

Mission: Contribute to reflection on reference surfaces, pre-deployment proofs, interoperability, and multi-actor proof chains.

Work in progress: Exploratory contribution planned around the interoperability contract and articulations between infrastructures.

Report: To be published after methodological consolidation.

Profile: [LinkedIn – RTK Security Labs]

James Aull, ASRO™ – AI Systems Reliability Operator   
Member, NeoMundi Advisory and Vigilance Committee · Governance & Attestation Peer
Proof Doctrine, AI Systems Reliability & Authority Boundaries

James Aull is the founder of ASRO™, an independent governed-state witness and evidence layer for AI systems. ASRO preserves independently reviewable evidence of the governed state active when an AI system acted. It does not determine admissibility, authorize continuation, convert a measurement or signal into a verdict, or exercise final decision-making authority.

Within the Observatory, James contributes as a Governance & Attestation Peer while also serving as a member of the NeoMundi Advisory and Vigilance Committee. His contribution helps strengthen the proof doctrine, artifact reliability, interpretation limits, authority boundaries, and the robustness of future interoperability formats, particularly around the Runtime Governance Contract.

His input is particularly important for maintaining a clear distinction between what an infrastructure declares, what an instrument observes, what a reviewer interprets, and what an authority decides or validates.

His participation framework inspired the NeoMundi Charter (roles, allocation, reuse, limits of authority, and voluntary participation).
Published report: Governance Participation Discipline.

Mission: Contribute to proof discipline, clarification of authority boundaries, attestation frameworks, and methodological robustness of AI governance layers.

Work in progress: Exploratory contribution on independent attestation layers, authority boundaries, and interoperability conditions between AI governance systems.

Report: To be published after methodological consolidation.

Profile: [ASRO]

James Moore   
NeoMundi Research Contributor · Human Governance at Execution-Time
Execution-Time Governance, Operational Responsibility & Escalation

James Moore works on AI governance at the moment of execution: the layer that determines whether AI-assisted actions should be authorized, escalated, interrupted, or stopped before an irreversible consequence occurs.

His work focuses on the critical moment when an AI-produced or AI-assisted output becomes a real-world action. This is where authority, responsibility, escalation pathways, and operational admissibility must be structurally defined, particularly in high-risk or regulated environments.

Founder of Nova Jema AI Systems, an independent AI governance research initiative focused on execution-time accountability, human authority structures, escalation boundaries, and operational governance gaps.

Within the Observatory, he contributes to the human and operational layer of governance: the articulation between runtime signals and decision rights, escalation logics, responsibility structures, and secure intervention mechanisms.

Mission: To contribute to the definition of escalation boundaries, responsibility frameworks, human authority, and operational admissibility in high-stakes AI use cases.

Work in progress: Exploratory contribution on execution-time governance.

Report: Measuring AI Is Not Enough: Who Decides When a Signal Becomes Critical?

Profile: [LinkedIn]

T. Lotus   
NeoMundi Research Contributor ·Human Governance at Execution-Time
Decision Integrity, Human Judgment & AI Signal Governance

T. Lotus is the founder of The Lotus Decision Integrity Advisory, a future-facing intelligence firm focused on decision integrity for leaders operating in high-stakes, AI-accelerated environments. Unlike conventional consulting or advisory firms, the firm works at the level of decision formation itself, before choices consolidate into outcomes.

Her work examines the critical period before a decision turns into commitment — when assumptions, incentives, information gaps, institutional pressures, and timing constraints already shape judgment.

Within the Observatory, T. Lotus contributes to the human layer of execution-time governance: interpretation of runtime signals, escalation logic, responsibility chains, documentation discipline, and the practical conditions for responsible action.

Her contribution helps ensure that measurement is not treated as a mere abstract technical output, but as a foundation for disciplined, documented, and responsible human decision-making.

Mission: To work on the transition from technical signals to human judgment, escalation, responsibility, and documented action.

Work in progress: Exploratory contribution on decision integrity and human governance of AI signals.

Report: To be published after methodological consolidation.

Profile: [LinkedIn]

Richard Rams Colimon   
Continuity Architecture & Runtime Governance

Richard Rams Colimon works on architectural approaches for AI systems operating under changing conditions. Through ManChine AI Technology, he develops Continuity Architecture, Runtime Integrity Control (RiCo) and the Runtime Execution Boundary (REB), the architectural layer where RiCo operates.

These frameworks are designed to continuously assess whether AI-driven actions remain admissible as authority, policy, evidence, environmental conditions, and potential consequences evolve.

Within the Observatory, he contributes to the architectural transition from Observation → Admissibility → Execution. His work complements AI observability by extending measurement into continuous runtime governance, ensuring that observation alone is not sufficient and that admissibility must be actively re-established before execution.

Mission: Contribute architectural methods and frameworks for Continuity Architecture and Runtime Integrity Control (RiCo) and the Runtime Execution Boundary (REB) to support interoperable runtime governance that preserves legitimacy and accountability under dynamic conditions.

Work in progress: Exploratory contribution of the Continuity Architecture, RiCo and REB frameworks, with a focus on the Observation → Admissibility → Execution transition and its integration into multi-actor governance architectures.

Report: To be defined after initial pilots and interoperability testing.

Profile: [LinkedIn – ManChine AI]

Inès Ramoul   
NeoMundi Research Contributor · Orientation & Vigilance Committee / Legal & Compliance
DPO, GDPR, EU AI Act & AI Governance

Inès Ramoul is a lawyer specialized in digital law, data protection, GDPR compliance, and artificial intelligence governance.

Trained in private law and digital activities law at Lumière Lyon 2 University, she has developed expertise at the intersection of data law, regulatory compliance, risk management, and emerging requirements related to the EU AI Act.

Within the Observatory, Inès serves both as a member of the Orientation & Vigilance Committee and as a legal and compliance contributor. She provides insights on issues related to measurement, traceability, and governance of AI systems: role qualification, responsibilities linked to data processing, GDPR points of vigilance, documentation, risk management, and alignment with applicable European AI requirements.

Her contribution helps strengthen the legal and operational robustness of the Observatory, ensuring that measurement and observability work is carried out in a logic of compliance, transparency, responsibility, and controlled use.

Mission: To provide legal and compliance analysis on GDPR, EU AI Act, traceability, role qualification, and AI governance issues.

Work in progress: Legal and compliance vigilance contribution within the framework of the Observatory’s exploratory cycle.

Report: To be defined.

Profile: [LinkedIn]

Darz’ Morris   

NeoMundi Research Contributor · Constitutional AI Governance & Continuity Systems

Constitutional Invariants for Reconstructable AI Governance

Darz’ Morris is an independent researcher focused on constitutional AI governance, continuity systems, and reconstructable decision environments. His work explores implementation-independent constitutional conditions required for AI systems to remain governable across time.

He is particularly interested in governance frameworks that go beyond static compliance by preserving authority, evidence, legitimacy, reasoning, and correction pathways throughout the full lifecycle of consequential decisions.

His recent independent research, The Minimal Continuity Record: Candidate Constitutional Invariants for Reconstructable AI Governance and Long-Term Stewardship, introduces a constitutional methodology for discovering, validating, and stewarding candidate constitutional invariants through evidence, independent review, and cross-domain evaluation (DOI 10.5281/zenodo.21083105).

Within the NeoMundi Observatory, Darz contributes to the articulation between runtime observability and long-term institutional continuity, with a focus on how constitutional principles can strengthen human governance layers and decision responsibility.

Mission: Bridge runtime observability (signals, behavioral regimes, intervention logics) with constitutional governance frameworks to support more robust, reconstructible, and stewardable AI systems.

Work in progress: Analysis of NeoMundi Observatory data and ControlTower signals from the perspective of constitutional invariants and long-term governability.

Report: To be defined (expected in Q3/Q4 2026).

Profile: [LinkedIn]

Evelyne-Claudia Yantony   
NeoMundi Research Contributor · Runtime Governance & Correctability
AI Governance & Ethical Intelligence, Correctability Frameworks for Human-Accountable Systems

Evelyne-Claudia Yantony is an author and independent thinker specializing in ethical intelligence, AI governance, and correctability frameworks for human-accountable systems. Her work emphasizes presence over noise, depth over speed, and trust as the foundation of human-AI interactions.

She contributes in particular to the Runtime Governance Contract and to correctability principles through a minimal, interoperable JSON envelope. Her mission is to ensure that signals observed during execution can trigger review or re-evaluation without being confused with authorization, decision-making, or execution permission. A report will be published following the initial interoperability pilots.

Mission: Enable signals observed during execution to trigger review or re-evaluation, without being mistaken for authorization, decision, or execution permission.

Work in progress: Exploratory contribution to the Runtime Governance Contract and to correctability principles applicable to a minimal interoperable JSON envelope.

Repport: To be defined after the first interoperability pilots.

Profile: [LinkedIn]