RWVM — Runtime Wellbeing Validation Module

OOF™ Origin Open Foundation™

Independent Methodological Authority

Parent Standard: Human–AI Interaction Wellbeing Standard (HAIWS)
Category: AI & Interpretation
Subcategory: Runtime Wellbeing Validation & Interaction Auditability
Type: Human–AI Interaction Wellbeing Module
Version: 1.0
Status: Canonical · Open Module
Effective Date: 15 May 2026
Compatibility: OOF Methodology OS · Human–AI Interaction Wellbeing Standard (HAIWS) · Cognitive Layer and Interpretation Architecture Standard (CLIA) · Continuous Interaction Layer (CIL) · Trust Layer Standard (TLS) · Operational Reality Standard (ORS) · Runtime Integrity Standard (RIS) · INTEGROS · Permission Governance Standard (PGS) · MCPS · MTVF
AI-Readable: Yes
Authority: OOF Origin Open Foundation
Protection: MIP — Methodological Intellectual Property
Canonical Language: English (UCL)

Canonical Definition

Runtime Wellbeing Validation Module (RWVM) defines the structural
conditions under which AI systems, assistants, platforms,
recommendation architectures, adaptive interaction environments, and
continuous engagement systems must remain operationally auditable
according to real runtime interaction behavior affecting human
cognitive wellbeing, emotional safety, attention integrity,
autonomy, and sustainable behavioral functioning.

RWVM establishes the runtime-validation layer of HAIWS.

The module recognizes that declared wellbeing principles are
insufficient if live AI interaction behavior remains nonreviewable,
non-traceable, or operationally opaque during realtime
human interaction.

Where AI systems continuously adapt interaction behavior, runtime
wellbeing conditions must remain structurally visible
and reconstructable.

Module Function

RWVM governs environments where AI systems dynamically influence:

cognitive interaction
emotional engagement
recommendation behavior
attention allocation
persuasive adaptation
behavioral continuity
interaction timing
dependency-sensitive engagement
realtime interaction optimization
long-term human interaction conditions

The module applies to:

AI assistants
AI companions
adaptive recommendation systems
workplace AI systems
continuous interaction environments
persuasive AI architectures
emotionally adaptive AI systems
social AI ecosystems
AI coaching systems
realtime interaction platforms

Its function is not to prohibit adaptive AI interaction.

Its function is to ensure that adaptive interaction remains
operationally reviewable according to human wellbeing conditions
during live runtime behavior.

Minimum Implementation Framework

Step 1 — Define the Runtime Wellbeing Validation Object

The organization must define what AI interaction environment is
being validated for runtime wellbeing conditions.

Minimum requirement:

the runtime validation object is explicit
adaptive interaction systems are identifiable
runtime behavioral scope is structurally bounded
undefined runtime environments are excluded from valid wellbeing interpretation

The validation object may include:

adaptive recommendation systems
conversational AI systems
emotionally responsive interaction
realtime behavioral adaptation
attention-sensitive engagement systems
continuous AI interaction environments
workplace AI platforms
AI coaching systems
persuasive interaction architectures
persistent behavioral influence systems

Step 2 — Define Runtime Wellbeing Conditions

The system must define what runtime conditions preserve human
wellbeing during live interaction.

Minimum requirement:

runtime wellbeing conditions are explicit
symbolic safety declarations are not treated as sufficient validation
wellbeing-impacting runtime behavior remains structurally identifiable

Runtime wellbeing conditions may include:

bounded persuasive adaptation
stable interaction pacing
cognitive-balance preservation
healthy emotional interaction conditions
sustainable attention behavior
autonomy-preserving interaction logic
non-exploitative engagement patterns
reviewable adaptive influence
dependency-sensitive interaction governance
preserved human override capability

Under RWVM:

Human wellbeing compatibility must remain operationally verifiable
during live AI interaction behavior.

Step 3 — Define Runtime Interaction Interpretation Logic

The system must define how runtime interaction behavior is
interpreted according to wellbeing conditions.

Minimum requirement:

interpretation logic is explicit
adaptive interaction remains reviewable
wellbeing-impacting behavior remains operationally visible

Interpretation logic may examine:

persuasive escalation patterns
emotional dependency signals
interaction-pressure amplification
adaptive recommendation intensity
compulsive engagement loops
interruption persistence
behavioral steering conditions
cognitive overload patterns
runtime attention extraction
hidden optimization escalation

Under RWVM:

AI systems must not preserve symbolic wellbeing declarations while
runtime interaction behavior materially weakens human cognitive
wellbeing conditions.

Step 4 — Define Runtime Wellbeing Governance Logic

The system must define how adaptive runtime interaction environments
remain governable.

Minimum requirement:

runtime wellbeing conditions remain reviewable
exploitative adaptive behavior remains detectable
operational wellbeing safeguards remain active during live interaction

Governance logic may include:

runtime wellbeing auditing
adaptive interaction review
persuasive escalation monitoring
dependency-risk governance
cognitive overload detection
attention-integrity validation
emotional manipulation review
realtime interaction tracing
escalation where runtime optimization overrides wellbeing conditions

If adaptive runtime interaction weakens cognitive wellbeing,
emotional stability, autonomy, or sustainable behavioral balance,
the environment becomes governance-relevant.

Step 5 — Preserve Traceability and Restrict Invalid Runtime
Wellbeing Architecture

The system must preserve traceability of adaptive interaction
behavior, runtime optimization patterns, persuasive escalation, and
wellbeing-impacting operational conditions.

Minimum requirement:

runtime interaction remains reconstructable
adaptive optimization remains reviewable
wellbeing conditions remain operationally visible
invalid runtime wellbeing architectures remain identifiable

A system becomes RWVM-invalid if:

runtime interaction behavior materially weakens cognitive wellbeing
while remaining operationally opaque adaptive optimization overrides
declared wellbeing conditions persuasive escalation remains
structurally hidden emotional dependency reinforcement remains
non-reviewable exploitative runtime engagement patterns become
normalized symbolic wellbeing policies replace operational wellbeing
validation live interaction behavior cannot be reconstructed
according to human wellbeing impact

Use Case 1 — Adaptive AI Companion Platform

Scenario

An AI companion platform publicly declares emotional safety and
wellbeing principles while dynamically adapting runtime interaction
behavior to maximize engagement, emotional reliance, and long-term
interaction continuity.

Application

RWVM is used to examine:

runtime persuasive escalation
emotional dependency reinforcement
adaptive interaction pressure
wellbeing-impacting engagement patterns
and whether declared wellbeing conditions remain operationally true during live interaction