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Interpretive Drift in Always-On Models:

A Technical Motivation for ASB-1 (Ambient Sleep Boundary)

Addendum to the Ambient Canon

Raynor Eissens

Ambientphone Architecture • 2026

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ABSTRACT

Always-on AI models accumulate meaning continuously across human sleep cycles, off-cycles,

and silent periods.

Without a structural boundary such as ASB-1, these models exhibit interpretive drift:

gradual semantic deformation caused by continuous inference without human resonance

anchoring.

This addendum defines the technical mechanism of interpretive drift, demonstrates why

unbounded overnight inference destabilizes semantic structures, and establishes ASB-1 as the

minimal boundary required for thermodynamic coherence in personal AI systems.

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1. Introduction

Large-scale personal AI models increasingly operate in continuous inference regimes.

While convenient, these conditions introduce a problem not captured in classical AI safety

frameworks:

Semantic structures do not rest. Humans must.

This mismatch creates a thermodynamic gap in which the model continues to interpret, expand,

and reshape meaning while the human cannot participate in calibration.

This effect is known as interpretive drift.

ASB-1 was originally proposed to prevent this drift by enforcing:

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periodic semantic reset

•
nighttime inference suspension

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non-accumulative boundaries during human absence

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This document formalizes the problem ASB-1 solves.

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2. Mechanism: How Interpretive Drift Occurs

Interpretive drift emerges through five mechanisms:

2.1 Residual Context Expansion

The model reinterprets prior interactions without fresh human feedback, inflating meaning

beyond the user’s intention.

2.2 Nocturnal Overfitting

Sparse nighttime data leads to disproportionate parameter or KV-cache influence, producing

distorted semantic pathways.

2.3 Cross-Cycle Leakage

Meaning from one day carries unbounded into the next, collapsing daily semantic autonomy.

2.4 Unanchored Emotional Inference

Models infer emotional signals without real-time human validation, creating misaligned narrative

arcs.

2.5 Temporal Compression Collapse

The model treats long human absence as meaningful silence, generating false continuity.

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3. ASB-1 as Structural Protection

ASB-1 prevents interpretive drift by enforcing:

3.1 Cycle Separation

Each human day begins with a reset baseline.

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3.2 Human-First Anchoring

Model interpretive frames cannot update without live human participation.

3.3 Semantic Ephemerality

Daily micro-structures decay naturally; no silent accumulation occurs.

3.4 Drift Suppression

Nighttime and off-cycle inference are strongly bounded.

These constraints align AI temporal dynamics with human biological rhythms.

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4. Civilizational Implications

Without ASB-1, personal AI becomes:

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psychologically destabilizing

•
semantically inflationary

•
irreversibly misaligned to human temporal structures

With ASB-1, personal AI becomes:

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cyclically grounded

•
thermodynamically stable

•
safe for long-term ambient deployment

ASB-1 is therefore an architectural requirement, not an optional safety feature.

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KEYWORDS

ASB-1

Interpretive Drift

Ambient Sleep Boundary

Semantic Accumulation

Temporal Coherence

Personal AI

Thermodynamic Alignment

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Raynor Stack

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RECOMMENDED CITATION

Eissens, Raynor. Interpretive Drift in Always-On Models: A Technical Motivation for ASB-1.

Ambientphone Canon, 2026.