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Object-Bound Agentic Interfaces

Receiver-First Spatial Inventories for Post-Smartphone Computing

DOI: 10.5281/zenodo.19500161

Raynor Eissens · Ambient Era Canon · 2026

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Abstract

This paper introduces Object-Bound Agentic Interfaces (OBAI): a post-smartphone interface

paradigm in which physical objects function as primary interface anchors, activating spatially

bound inventories that contain app shortcuts, dynamic state signals, and agent-generated

outputs.

In contrast to app-centric and chat-centric interaction models, OBAI operates under a receiver-

first logic:

object → receive → generate

AI output is not immediately generated in abstract interfaces, but is instead triggered by

interaction with a real-world object, and may be persistently placed back into that object’s

spatial inventory.

This paper argues that existing AR systems, object recognition tools, and spatial computing

platforms provide partial precedents, but do not implement:

•
unified object-bound inventories

•
persistent, fading state layers

•
integration of apps and agent outputs

•
autonomous agentic “landing” of outputs into object-specific contexts

OBAI defines a new interface primitive:

the object as a living, persistent, spatial interface container

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Canonical Definition

Object-Bound Agentic Interfaces are spatial computing systems in which physical objects act as

persistent interface anchors that activate object-specific inventories containing app shortcuts,

chromatic state signals, and agent-generated outputs, with generation occurring only after

object-based reception.

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Core Principle

Traditional model:

input → generate → display

Object-Bound model:

object → receive → contextualize → generate → land

The object is not the target of output.

The object is the condition for output.

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Problem Statement

Contemporary AI and interface systems exhibit three dominant limitations:

1.
Interface detachment

Interaction occurs in abstract containers (apps, chats, dashboards)

disconnected from physical context.

2.
Immediate generation bias

AI generates output without environmental grounding, leading to

overload, irrelevance, or instability.

3.
Non-persistent contextualization

Outputs are ephemeral and not anchored to meaningful real-world

structures.

Existing systems increase capability, but lack situated coherence.

As identified in prior work, intelligence without environmental support

leads to pressure accumulation and instability  .

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Proposed Architecture

OBAI introduces a three-layer system:

1. Object Layer (Receiver Layer)

Physical objects act as:

•
entry points

•
contextual anchors

•
identity surfaces

Examples:

•
PlayStation → gaming context

•
refrigerator → consumption/logistics

•
plant → care/temporal cycle

•
bag → movement/preparation

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2. Spatial Inventory Layer

Each object activates a dedicated inventory interface containing:

•
app shortcuts (object-relevant utilities)

•
chromatic state markers (living signals, fading over time)

•
contextual actions

•
agent-generated outputs

This inventory is:

•
spatially anchored

•
persistent

•
dynamically evolving

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3. Agentic Layer

Agents operate as:

•
detectors (events, updates, signals)

•
interpreters (context relevance)

•
producers (outputs, suggestions, actions)

Crucially:

Agents do not output globally.

They land outputs into object-specific inventories.

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Interaction Model

Minimal loop:

scan → reveal → select → generate → land → fade

Expanded:

1.
User observes or scans object

2.
Object activates spatial inventory

3.
User selects or inspects chroma/app

4.
AI generates context-specific output

5.
Output is placed back into inventory

6.
State decays over time (fade / afterfield)

This extends the ARC-1 logic of field-first interaction and afterfield

decay into a general interface paradigm  .

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Receiver-First Logic

The defining inversion:

Generation is not primary.

Reception is primary.

AI output is:

•
delayed until context exists

•
grounded in object presence

•
spatially returned to that context

This resolves:

•
interface overload

•
notification drift

•
context fragmentation

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Relation to Prior Art

Closest precedents include:

•
object-centric AR interaction systems

•
spatial UI anchored to surfaces

•
multimodal AI with contextual outputs

However, no identified system combines:

•
persistent object-bound inventories

•
app + agent unification

•
autonomous output landing

•
receiver-first generation logic

Thus:

Partial prior art only.

The novelty lies in the integration and structural coupling of these elements.

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System Properties

OBAI systems exhibit:

•
situated intelligence

•
persistent context memory

•
low-symbolic signaling (chroma, fade, presence)

•
environmental UI distribution

•
non-intrusive output delivery

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Conceptual Shift

From:

•
app-centric computing

•
feed-based interaction

•
notification systems

•
chat-based AI

To:

•
object-centric computing

•
environment-bound interaction

•
ambient state signaling

•
agentic contextual landing

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Example

Object: PlayStation

Inventory contains:

•
game shortcuts

•
friend presence indicators

•
agent-generated recommendations

•
call actions

•
live chromatic states

Agent detects new JRPG →

lands result as chroma →

user opens →

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content generated on demand

No global notification required.

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Relation to Reasoning Systems

OBAI operates as an interface layer.

It may be supported by underlying reasoning systems such as structured routing architectures

(e.g. operator-based reasoning stacks) that refine output prior to externalization  .

However, OBAI itself defines:

where output appears, not how reasoning is performed.

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Why It Matters

As AI becomes agentic, persistent, and ambient:

•
output volume increases

•
context fragmentation increases

•
user overload increases

OBAI introduces:

environment as interface

object as anchor

presence as filter

This transforms AI from:

a system that produces outputs

into:

a system that places meaning where it belongs

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Conclusion

Object-Bound Agentic Interfaces define a new interface primitive:

the physical object as a living, spatial, persistent interface

They resolve key limitations in current AI interaction models by:

•
grounding output in context

•
delaying generation until reception

•
distributing interaction across the environment

The result is a system where:

interfaces are not opened

but

revealed in place

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Keywords

object-bound interface, spatial computing, agentic AI, ambient interface, AR interaction,

receiver-first systems, contextual AI, chromatic interface, spatial inventory, post-smartphone UI,

ambient era

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One-Sentence Version

Physical objects become the primary interface, and AI outputs land where they are needed

instead of appearing everywhere.