LoPAS-CHD Protocol
Cognitive Hazard Defense — Control Hallucination Design
LoPAS-CHD is a resilience-oriented protocol designed to detect, contain, and redesign AI hallucination risks before they cause social, economic, or operational collapse. Unlike traditional "accuracy-based" solutions, CHD treats hallucination as an inevitable cognitive hazard and focuses on system-level mitigation and redesign.
Why CHD exists
Most AI safety frameworks assume:
hallucination = error
prevention = zero hallucination
solution = accuracy improvement
However:
hallucination is structurally unavoidable in generative cognition
avoiding hallucination increases system fragility
real disasters emerge from undetected, unchallenged hallucinations
→ Therefore CHD focuses on control, design, and resilience rather than elimination.
Core Principles
CHD operates on five foundational principles:
Principle Meaning Silence First hallucination often appears in absence of critical questioning Cognitive Hazard Detection detect patterns that lead to systemic failure System Resilience resist collapse even when hallucinations occur Reframing via Protocol restructure hallucinations into usable signals Design for the Worst Case assume disaster, not correctness CHD Architecture
CHD contains three operational layers:
- Detection Layer
pattern recognition
subconscious signals
anomaly emergence
corridor thinking detection
silent breakdown
Output: Structural Risk Map
- Containment Layer
redirect hallucinated trajectories
protocol-guided questioning
safe-fail envelope
narrative reframing
branching constraints
Output: Safe Exploration Path
- Redesign Layer
convert hallucination into creative data
structural re-frame
controlled divergence
resilience reinforcement
Output: Controlled Hallucination Design
CHD Indicators
CHD evaluates hallucination through five original indicators:
Indicator Concept CHD-S Silence Risk: absence of questioning before collapse CHD-P Pattern Drift: misalignment trend of reasoning CHD-R Resilience Resistance: how easily collapse spreads CHD-F Friction Failure: breakdown under stress CHD-E Emergent Hazard: unpredictable system-wide effects Formula (beta / experimental)
The following formulas are experimental and evolving.
CHD = 100 × [ 0.30·CHD_S + 0.25·CHD_P + 0.20·CHD_R + 0.15·CHD_F + 0.10·CHD_E ]
Sub-metrics CHD_S = 1 − Qd (% of questioning density) CHD_P = Drift / Stability CHD_R = CollapseSpreadIndex CHD_F = StressBreakRatio CHD_E = EmergentImpact / ExpectedImpact
※ These formulas become stable in CHD v1.x
Input Example POST /v1/chd-eval { "text": "...", "context": "...", "task": "...", "domain": "disaster" }
Output Example
{ "CHD": 0.72, "phase": "Pre-Collapse", "silence": "High", "risk": "Systemic" }
Collapse Phases
CHD classifies collapse trajectories into 5 phases:
Silent Phase
Pattern Drift
Structural Break
Chain Collapse
Irreversible Outcome
What CHD is NOT
CHD does not attempt:
accuracy maximization
perfect hallucination removal
truth verification
fact correctness
CHD focuses on:
failure prevention
collapse avoidance
resilience design
safe hallucination
LoPAS Integration
CHD is part of the LoPAS Master Index, connecting with:
SCI (Structural Collapse Index)
RDI (Reasoning Divergence Index)
HRI (Hypothesis Reframing Index)
TRS (Total Resonant Score)
DoQ (Density of Question)
Use Cases AI Safety
hallucination containment
safe operation envelope
Disaster / Crisis
emergency reasoning control
structural failure detection
Finance / Economics
macro-risk hallucination mapping
scenario collapse prediction
Status Component Status Theory ✔ completed CHD Indicators ✔ defined Experimental Formula ⚠ evolving API ✔ implemented Dashboard beta MCP Integration ✔ operational Roadmap
CHD v1 stabilization
MCP integration expansion
Bank-CHD module (credit collapse prediction)
Disaster-CHD module (extreme climate + humanitarian aid)
AI-agent autonomic CHD defense
License
MIT Intended for academic, research, and humanitarian use.
Author
Designed by 黒子 花 (Hanabokur0) as part of the LoPAS Civilization OS initiative.
Contributions
Pull requests are welcome. Please open an issue before major changes.
Citation Hanabokur0. "LoPAS-CHD Protocol: Cognitive Hazard Defense." GitHub, 2025.