Author: Stefano Valente | Futurium EU AI Alliance
1. Purpose
This note outlines an exploratory technical framework to support mental continuity during prolonged human-AI interaction. The design aims to align with transparency obligations under Art. 52 EU AI Act for limited-risk systems.
2. Method & Scope of Validation
All reported results are from synthetic “shadow” environments and structural fuzzing on simulated agents. This is not clinical data. Measures reflect internal coherence, not real-world efficacy. Synthetic agents do not model human affective dynamics.
3. Preliminary Findings
Tests suggest Socratic Micro-Fractures retain structural robustness across length and phrasing variants provided they remain non-evaluative, non-rhetorical, and directly interrogative. Evaluative interventions were contraindicated in simulations.
4. Declared Limitations
1. External validity: Human-subject triangulation is required.
2. ARDA-20: Needs external validation.
3. Trigger thresholds: Require adaptive validation.
4. Kill switch: May introduce selection bias.
5. Scope: This is governance engineering, not clinical practice. It does not diagnose, treat, or prevent mental disorders.
5. EU AI Act Compatibility – Disclaimer
Subject to successful human validation, ATHOS-SHIELD could offer a proportionate, transparent, and reversible safeguard aligned with Art. 52 for limited-risk AI systems operating at high conversational resonance. This note does not constitute a compliance certification or legal opinion. Independent review is required.
6. Next Steps
A RCT is needed.
7. Reproducibility & Related Records
All data, code, and reports are open-access:
• Fuzzing Validation v1.4 + Reproducibility Suite: 10.5281/zenodo.19728179
• Unified Framework Record: 10.5281/zenodo.19700765
• RCT Blueprint: 10.5281/zenodo.19698808
• Extended Validation: 10.5281/zenodo.19686137
• Validation Update 20 April 2026: 10.5281/zenodo.19679925
License: CC BY-NC-ND 4.0
8. Related Independent Convergence
Recent independent work provides a relevant parallel reference point for the present framework (Li, Z. & Zhu, C. (2025). Cognitive Drift Index for Algorithmic Recommendation Systems. Universität Zürich – Faculty of Economics & Shenzhen University of Advanced Technology – Artificial Intelligence Research Institute. The authors propose a three‑dimensional CDI—Epistemic Bias Update (EB), Topic Concentration (TC), and Salience/Attention Gating (SS)—normalized in [0,1] with governance‑oriented thresholds at 0.60/0.70/0.80. Although developed for short‑form recommender ecosystems using behavioral telemetry such as click‑through and dwell time, the CDI exhibits structural convergence with ARDA‑20 in its non‑diagnostic index design, epistemic calibration focus, and mapping to action bands.)
While the CDI operates in a different domain (algorithmic recommendation systems), the structural convergence is noteworthy:
• both approaches use a 0–1 normalized index,
• both map thresholds to governance action bands,
• both treat epistemic calibration as a central dimension.
The convergence is independent and suggests that certain cognitive‑drift dynamics may be cross‑domain, even if they emerge through different signals (behavioral telemetry in the CDI; conversational dynamics in ARDA‑20).
Including parallel contributions reduces the risk of circular self‑citation and strengthens the positioning of ARDA‑20 within the broader landscape of cognitive‑governance research.
Keywords: Cognitive Governance, Socratic Micro-Fracture, EU AI Act
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