1Executive Summary

Digital systems have progressively reduced the cost of finding information. Search engines made documents addressable by queries; databases made records addressable by structured keys; GPS made destinations addressable by intent and location. Yet the physical environments in which people work remain largely passive. A worker who needs a specific object must still remember, visually scan, walk, open drawers, inspect bins, or ask another person.

Intent-Addressable Physical Infrastructure (IAPI) proposes a different relationship between people and physical space. Rather than requiring the human to translate a need into a search strategy, IAPI accepts an expression of intent, resolves that intent against a bounded set of known resources and locations, and produces a physical response: guidance, illumination, routing, retrieval, release, or other actuation. The architectural primitive is simple:

Intent → Resource Resolution → Physical Actuation

The significance lies in what is removed from the workflow: manual physical search. Under IAPI, search becomes a capability of the environment rather than an obligation of the person operating within it.

IAPI is not synonymous with robotics, IoT, voice assistants, indoor positioning, inventory management, or general-purpose AI. It can incorporate elements of those systems, but its defining property is the closed-loop resolution of human intent into a specific physical resource response. This framing fits naturally within the broader cyber-physical-systems tradition, where computation, communication, sensing, actuation, physical components, and humans are engineered as an integrated system [1].

A practical implementation can be deliberately modest in computational complexity. When the resource universe is known and the allowable actions are bounded, the system need not solve an open-ended intelligence problem. It must reliably infer intent, map that intent to a finite physical address space, and actuate within known timing and safety constraints.

This paper defines the IAPI model, distinguishes it from adjacent technology categories, presents design principles and a reference architecture, and describes its implications for healthcare, defense logistics, manufacturing, warehousing, laboratories, maintenance environments, and other settings where skilled people lose time locating known resources.

2The Persistent Cost of Physical Search

Physical search is so familiar that it is rarely treated as a systems problem. Shelves, supply rooms, tool cribs, carts, stockrooms, cabinets, maintenance cages, and warehouses are generally organized to make human search easier, not to eliminate it.

The workflow typically begins with a cognitive requirement ('I need an arterial-line kit,' 'I need a 10 mm socket,' 'I need component X') and immediately transfers a navigation burden to the person. The person must infer where the item should be, travel to the likely location, discriminate among visually similar objects, and recover when the first guess is wrong.

This burden is fragmented into seconds and minutes, which makes it operationally easy to ignore. But fragmented search time can accumulate across workers, shifts, departments, and facilities. In healthcare, published work has specifically treated nursing time spent seeking supplies as waste and demonstrated that redesigning point-of-use storage can materially reduce the number of supply-seeking trips [4]. The broader lesson is not that every environment needs better shelving. It is that physical resource location is a legitimate workflow variable that can be engineered.

IAPI asks a more aggressive question:

If the environment already possesses or can maintain a mapping between resources and physical locations, why should the human remain responsible for performing the search?

3Definition of Intent-Addressable Physical Infrastructure

Intent-Addressable Physical Infrastructure is physical infrastructure in which a human expression of resource intent can be computationally resolved to a known physical resource and translated into direct physical guidance or actuation.

The word addressable is important. In digital systems, an address allows a system to identify and operate on a particular location or resource. IAPI extends that logic to the physical environment while adding an intent-resolution layer. The user does not need to know the address. The infrastructure does.

A physical resource may be a supply bin, tool, medication location, component, repair part, document, piece of safety equipment, or any other bounded object or location represented within the system. The response may be as simple as illuminating an LED at the correct shelf position or as complex as coordinating access control, robotic retrieval, or autonomous movement.

A system qualifies as IAPI when four conditions are present:

1. Intent input. The user communicates the resource need without having to specify its physical address.
2. Bounded resolution. The system resolves that intent against a defined resource and location space.
3. Physical mapping. The resolved resource is associated with an actionable physical address.
4. Closed-loop response. The infrastructure guides, signals, releases, routes, or actuates in the physical environment.

4The Architectural Inversion

Most 'smart environment' programs begin by asking how much sensing, connectivity, data, and intelligence can be added. IAPI can begin from the inverse question: what is the minimum computational capability required to make physical search unnecessary?

That inversion changes architecture. If resource locations are known, physical retrieval is not inherently a general-intelligence problem. It is a bounded cyber-physical sequence:

Infer intent → Resolve resource → Resolve location → Actuate

Additional sophistication is valuable only when it measurably improves accuracy, latency, reliability, safety, maintainability, or another required system property. Otherwise it can add compute, power draw, connectivity dependence, integration burden, attack surface, and failure modes without improving the core function.

This principle is especially important when IAPI is deployed as infrastructure. Infrastructure is expected to be available, predictable, maintainable, and economically replicable. The intelligence layer should therefore be treated as an engineering resource to be budgeted, not a quantity to be maximized.

5Reference Architecture

A generic IAPI implementation can be represented as six functional layers. Implementations may collapse or distribute these layers depending on latency, power, safety, privacy, and scale requirements.

The reference data flow runs from human intent through inference to a resource identity, then to a physical address, then to an actuator, and finally to a human or physical outcome:

Human intent → inference → resource ID → physical address → actuator → human/physical outcome

This architecture is consistent with established cyber-physical-systems thinking. NIST describes CPS/IoT as systems combining logical, physical, transducing, and human components and emphasizes concerns including function, human interaction, trustworthiness, timing, data, composition, boundaries, and lifecycle [1][2][3]. IAPI narrows that broad systems space around one specific infrastructure capability: intent-driven physical resource resolution.

6Design Principles

• Boundedness. Define the resource universe, permissible actions, ambiguity states, and failure behaviors. Bounded systems are easier to validate and can often run on constrained hardware.
• Determinism where it matters. Infrastructure should provide predictable upper bounds for critical timing and actuation paths when the use case requires them.
• Local-first operation. When feasible, inference and resolution should remain operational without continuous cloud access. This can reduce latency, preserve function during outages, and limit unnecessary data transmission.
• Minimal data exposure. An IAPI system should collect only the data required to resolve the task. In many use cases, persistent raw audio, personally identifiable information, or centralized behavioral histories are unnecessary.
• Graceful failure. Failure should return the user to an understandable state, for example 'not resolved' rather than silently guiding to the wrong physical resource.
• Retrofitability. The most scalable implementations may augment existing shelves, racks, rooms, cabinets, and workflows rather than requiring wholesale facility reconstruction.
• Human authority. IAPI should reduce search burden without removing appropriate human judgment. The infrastructure resolves where; the user or governed workflow determines whether and how to use the resource.

7IAPI Versus Adjacent Technology Categories

8Healthcare as an Initial Use Case

Healthcare makes the search problem unusually visible because the value of clinician time is high, interruptions are common, and delays can occur inside time-sensitive workflows. Supply rooms also offer a favorable technical boundary: the resource vocabulary is finite, locations can be mapped, and the desired physical response can be simple.

Consider a nurse entering a supply room and saying, 'I need a central-line dressing kit.' In a conventional room, the request exists only in the nurse's mind; the nurse must translate it into a visual search. In an IAPI room, the utterance is interpreted locally or at the edge, mapped to the appropriate resource, mapped again to the resource's shelf address, and the relevant location is illuminated. The nurse remains in control of selection and clinical use, while the environment performs the search-resolution function.

The value proposition is therefore not simply faster finding. It is the conversion of repeated, low-value navigation and visual discrimination into infrastructure behavior, returning attention to work that requires clinical judgment and human presence.

A healthcare deployment must still be evaluated for usability, false guidance, workflow fit, infection-control implications, electromagnetic compatibility where relevant, cybersecurity, maintenance, and the accuracy of the underlying resource-location state. IAPI does not eliminate these engineering obligations; it creates a clearer functional boundary around them.

9DECA as One Implementation Architecture

Intelligent Clinical Systems is developing a deterministic edge approach, referred to as the Deterministic Edge Control Architecture (DECA), as one implementation of IAPI. The design premise is that a bounded physical resource problem can be resolved with compact contextual inference on resource-constrained microcontrollers rather than requiring a cloud-scale model in the critical path.

In this approach, multiple bounded contextual streams, such as immediate intent, longer-term priors, and short-term recency, can contribute to resource resolution while preserving explicit memory and timing budgets. The output is then mapped to a physical address and actuated through the environment.

DECA should be understood as an implementation thesis, not as the definition of IAPI itself. Other IAPI systems could use different inference models, sensors, communications protocols, address representations, or actuators. Separating the category from a particular implementation allows the concept to generalize across industries and technology stacks.

10Cross-Industry Applications

Across these domains, the common denominator is not the industry. It is the existence of a bounded physical resource space in which search consumes human time and the environment can maintain a sufficiently reliable mapping between resources and locations.

11The Economics of Removing Search

The economic value of IAPI can be modeled as recovered labor time, reduced interruption cost, faster task completion, lower training burden, fewer workflow handoffs, and potentially fewer errors caused by rushed or ambiguous searching. The value will vary sharply by setting: one minute saved for a highly trained clinician during a time-sensitive intervention is not equivalent to one minute saved in a low-consequence task.

A simple first-order model is:

Annual value ≈ workers × shifts × search time avoided × loaded labor value

That formula intentionally excludes second-order effects such as reduced cognitive switching, faster onboarding, improved standardization, and increased throughput. Those effects should be measured rather than assumed. The strongest business cases for IAPI will likely occur where search is frequent, resources are numerous or visually similar, labor is expensive, and delay has meaningful operational consequences.

12Trustworthiness, Security, and Failure Modes

Because IAPI links computation to physical outcomes, trustworthiness is a first-order architectural concern. NIST's CPS work similarly treats timing, cybersecurity, privacy, human-system interaction, and dependability as core considerations rather than add-ons [1].

Key failure modes include incorrect intent classification, stale resource-location mappings, actuator failure, ambiguous resource names, communications loss, power loss, malicious remapping, unauthorized commands, and user over-reliance. Design responses can include confidence thresholds, explicit ambiguity handling, local fallback behavior, integrity checks, authenticated updates, auditable mappings, bounded actuation permissions, and clear human override.

The architectural goal is not perfect intelligence. It is dependable resolution within a declared operating envelope, with failures that are detectable and recoverable.

13From Passive Space to Active Infrastructure

The deeper implication of IAPI is a change in what society expects from physical environments. Historically, buildings and storage systems expose resources spatially and depend on people to interpret that spatial organization. Labels, signs, aisles, maps, alphabetization, bin numbering, and color coding are all interfaces designed to help humans perform physical search.

IAPI does not merely improve those interfaces. It changes the allocation of responsibility. The human communicates the goal; the environment performs the resolution.

The analogy to GPS is useful. A driver no longer needs to know the street-by-street path before beginning a trip. The driver specifies the destination, and infrastructure plus computation resolve the route. IAPI applies a related abstraction to physical resources: the user specifies the resource intent, and the environment resolves the location and the path to action.

If this model becomes commonplace, physical search may increasingly be viewed the way manual route planning or memorizing database record locations is viewed today: possible, occasionally necessary, but no longer the default interface.

14Research and Standardization Agenda

For IAPI to mature from a useful framing into a durable infrastructure category, several questions merit systematic investigation:

• How should physical resources and addresses be represented so systems remain portable across facilities and vendors?
• What confidence, latency, and false-guidance thresholds are acceptable for different risk classes?
• How should intent models incorporate context without accumulating unnecessary personal data?
• What is the minimum computational capability needed for reliable resolution in bounded environments?
• How should resource-location state be updated, verified, and reconciled when humans move objects?
• Which functions require deterministic timing, and which can tolerate probabilistic or cloud-mediated behavior?
• How should IAPI systems fail safely when intent, state, or location is ambiguous?
• What interoperability layer could allow actuators, shelves, racks, robots, and software from different vendors to share an intent-to-address model?

These questions place IAPI within a broader cyber-physical research tradition while giving the field a specific organizing objective: make physical resources addressable through intent.

15Conclusion

For centuries, physical infrastructure has been organized around the assumption that people will search it. That assumption is no longer technically necessary in many bounded environments.

Intent-Addressable Physical Infrastructure proposes a new abstraction: people should be able to express what they need without also bearing the full cognitive and navigational burden of determining where it is. The environment can resolve intent against known physical resources and respond directly.

The person specifies the what. The infrastructure determines the where.

The objective is not maximum intelligence. It is minimum sufficient intelligence for reliable physical resolution. If implemented well, the result is more than faster search. It is a transfer of repetitive navigational work from people to the environments built to support them.

In that sense, IAPI represents a broader design direction for physical AI: not machines that merely perceive the world, but infrastructure that can understand bounded human intent and reorganize the interaction between people, resources, and space.

16About this paper

This document introduces and formally defines Intent-Addressable Physical Infrastructure (IAPI), an architectural framework developed by Intelligent Clinical Systems, Inc. (ICS). DECA and the healthcare examples describe work being developed by ICS. Product performance, economic impact, and clinical or operational outcomes require validation in the intended deployment environments.