The question for IPRR is no longer whether its constituent mechanisms already exist. They do. The harder question is whether an established problem class already owns the complete operation.

IPRR is potentially new at the level of problem boundary, not at the level of constituent mechanisms.

Core claim

1The Question After Defining IPRR

IAPI-06 separated the resource-resolution operation from the IAPI architecture and proposed IPRR as a problem class: given a resource-relevant objective and relevant context, determine an admissible physical fulfillment target and actionable endpoint, or explicitly decline resolution when sufficient reliability cannot be established [17]. IAPI-07 then examined how such resolution might become an infrastructure layer and why the Domain Name System provides a useful functional analogy [18]. Those papers establish a definition. They do not, by themselves, establish distinctness.

The scientifically harder question is therefore: does computer science already possess a problem class whose canonical formulation covers the same operation? If the answer is yes, IPRR should be treated as a relabeling, specialization, or application of that class. If the answer is no, and the operation recurs across architectures and domains with its own input boundary, output boundary, failure output, and correctness relation, then isolating it may be useful in the same way that other problem abstractions became useful only after researchers recognized a recurring computation as a problem in its own right.

The anchoring literature provides an instructive precedent. Coradeschi and Saffiotti observed in 2003 that symbol-to-sensor correspondence had long been present inside robotic systems but had only recently begun to be addressed as a problem per se [3]. IPRR makes a similar kind of claim, at a different boundary: not that systems have never selected or located physical objects, but that intent-to-resource resolution may deserve explicit treatment as a problem separate from the architectures that happen to perform it.

2Method: A Boundary Test, Not a Novelty Search

This paper is a targeted boundary analysis, not a systematic literature review and not a patent novelty opinion. The comparison intentionally emphasizes canonical definitions, widely recognized formulations, and relevant standards published before January 1, 2026. That cutoff reduces circularity: IPRR should not be validated by later material that has already absorbed the terminology. The analysis asks whether an established problem class itself requires the full IPRR operation, not whether an application built on that field could be extended until it performs it.

The subsumption criterion

A neighboring problem class should be treated as subsuming IPRR only if its canonical problem statement natively requires a solver to perform the following minimum functions for generic physical resources:

1. Accept a resource-relevant objective above the level of a known physical address or predetermined resource instance.
2. Determine what physical resource class, capability, or fulfillment type can satisfy the objective.
3. Select a specific admissible physical instance or inventory-bearing endpoint under capability, compatibility, policy, workflow, and availability constraints.
4. Establish physical state and an actionable spatial endpoint sufficient for the anticipated downstream response.
5. Treat the answer as state-dependent and therefore potentially invalidated by motion, depletion, reassignment, failure, or contention.
6. Associate the resolution with an explicit or implicit validity horizon and require revalidation when the claim can no longer safely be relied upon.
7. Permit an unresolved, abstaining, clarification, or verification outcome when no sufficiently reliable admissible target can be established.

A field can strongly overlap IPRR without satisfying this test. Likewise, a mechanism can be essential to IPRR without defining the problem. Localization, ontologies, auctions, leases, locks, world models, semantic maps, capability models, and selective prediction may all participate in a solver while remaining conceptually distinct from the end-to-end resolution objective. The strongest counterexample would be a pre-2026 problem formulation that begins with a semantically expressed resource need, ends with a state-valid actionable physical resource endpoint, defines correctness over admissible physical targets, includes a meaningful unresolved outcome, and is architecture-neutral with respect to whether the computation resides in a human, robot, application, infrastructure service, or distributed combination.

3Minimum IPRR Problem Boundary

The distinctness claim becomes testable only if IPRR is defined minimally. If every useful robotics, scheduling, perception, or control function is pulled inside the boundary, the abstraction becomes unfalsifiable and unhelpful. The minimum problem should therefore stop before execution and should not prescribe the architecture that performs resolution.

For a single-target request, let q = (i, c, t, m, τ_req), where i is the expressed resource-relevant intent, c is bounded context and policy, t is decision time, m is the anticipated response mode, and τ_req ≥ 0 is the period over which the consumer requires the returned resolution to remain actionable. Let Ω_s(q) denote the set of physical fulfillment targets that are acceptable for request q under physical ground truth at time s. The time-indexed set may change as resources move, are consumed, fail, become reserved, lose authorization, or cease to satisfy the response mode. A solver returns either a target together with an asserted validity horizon or an explicit unresolved state: Ψ(q) → (y, τ_asserted) or ⊥.

A non-abstaining resolution admitted for the requested horizon is correct if and only if τ_asserted ≥ τ_req and y ∈ Ω_s(q) for every s ∈ [t, t + τ_req].

Time-indexed correctness relation, evaluated against realized ground truth; the solver need not possess future ground truth when deciding

Multiple targets may legitimately satisfy the same request, so IPRR is not recovery of one predetermined object identifier. It is constrained physical fulfillment under state.

Intent → Resource class / capability → Admissible candidate set → Physical instance / fulfillment endpoint → State validation → Actionable spatial endpoint → Admit, clarify, verify, or abstain

The minimum functional chain. Execution begins after it

Temporal correctness: required and asserted horizons

A physical answer can be correct when computed and wrong when used. The selected pallet can move, a tool can be checked out, a battery can fall below the required charge, a part can be consumed, or another actor can obtain exclusive access. IPRR therefore distinguishes τ_req, the consumer's required action horizon, from τ_asserted, the duration for which the resolver claims the returned target remains actionable. A successful return should not claim to satisfy the request when τ_asserted < τ_req; it should instead revalidate, clarify, offer a shorter-horizon result only if the interface permits that negotiation, or abstain. Horizon calibration is an empirical property of the resolver: across admitted resolutions, how accurately does τ_asserted predict the realized interval over which the returned target remains in Ω_s(q)? A TTL or lease can help implement expiration, but a timer is not itself evidence that the underlying physical assertion remains correct.

Abstention is a decision outcome, not a claimed invention

The reject option has a long formal history in statistical pattern recognition. Chow characterized the error-reject tradeoff in 1970 [15], and modern selective-classification work formalizes the risk-coverage tradeoff for learned predictors [16]. IPRR does not claim abstention as a new mechanism. It imports the same discipline into physical resolution: a solver should not be forced to name a target when its evidence is insufficient.

Two cases should be distinguished. If there is no single target y such that y ∈ Ω_s(q) for every s ∈ [t, t + τ_req], then no target remains acceptable for the full requested horizon; any non-abstaining answer is necessarily wrong and abstention or clarification is the appropriate outcome unless the request is revised. If at least one target satisfies that interval-wide condition but the solver cannot establish one with sufficient reliability under its admission policy, epistemic abstention can still be the safer system decision even though ground truth contains a solution. Benchmarking should therefore report both conditional correctness and coverage rather than treating every abstention as an error.

4Boundary Analysis Against Established Problem Classes

The comparison asks a narrow question: does the established problem class itself subsume the minimum IPRR boundary? "Partial" means that the field solves an important component or can host a solver, not that it is deficient at its own intended task.

• Symbol grounding. Harnad asked how a symbol system's meaning can be intrinsic rather than dependent only on other symbols [1]. This is upstream of IPRR: grounding may establish that a need corresponds to a torque tool or replacement component, but it does not choose an admissible instance, validate availability, resolve an endpoint, or bound how long the result holds.
• Intentional Naming System. INS is the closest distributed-systems antecedent. It lets applications describe what they want rather than where to find it, supports dynamic discovery and mobility, and performs late binding [2]. But INS selects a service endpoint because it advertises a property. An IPRR solver must also handle a physical referent that moves independently of its identity, is consumed, is reserved by another actor, or is correctly located but not spatially actionable.
• Perceptual anchoring. Anchoring connects symbols and sensor data referring to the same physical object [3]. Its question is referential: which sensed object corresponds to a symbol? IPRR adds a prior selection question: which object should become the referent at all? An anchor can be correct even when the anchored object is not an admissible resource for the intent.
• Semantic mapping. Semantic maps enrich spatial representations with class, pose, region, and relationships [5] and are a natural source of IPRR state. But a map is a representation; it does not define a request-dependent acceptable-solution set, choose among eligible instances, model an unresolved outcome, or assert a validity horizon.
• Object-goal navigation. ObjectNav asks an agent to navigate to an instance of an object category [6], a concrete category-to-instance resolution bound to embodied navigation success. IPRR is client-independent and stops before navigation. An ObjectNav system may solve IPRR internally; an IPRR service need not navigate anywhere.
• Language-grounded robotics and open-vocabulary scene graphs. This is the most important modern challenge. SayCan combines language-model priors with affordance value functions [7]; SayPlan grounds language tasks over hierarchical 3D scene graphs [8]; ConceptGraphs plans from open-vocabulary scene graphs [9]. These systems materially overlap IPRR and often do resolve physical referents. Their canonical output, however, remains robot-localized perception and planning, not a client-independent result whose capability, policy, availability, spatial actionability, contention, abstention, and validity horizon form one correctness relation. A formulation making those elements native would be a serious candidate to subsume IPRR.
• Task and motion planning. Modern TAMP can jointly choose objects, actions, and geometric parameters [10], so it may perform IPRR-like selection internally. But its canonical problem is plan generation. IPRR proposes a separable interface whose output is the admitted target before execution; joint optimization does not make the two evaluation boundaries identical.
• MRTA and manufacturing allocation. MRTA asks which robot should execute which task [4]; manufacturing allocation extends this to distributed resources under uncertainty [11]. Both ordinarily begin after tasks, resources, and capabilities are modeled well enough to optimize. IPRR includes the preceding step when the objective sits above a predetermined class or instance.
• Industry 4.0 capability matching. Plattform Industrie 4.0 raises abstraction from vendor resources to required capabilities, matching them to offered capabilities and executable skills in Asset Administration Shell models [12]. This directly covers IPRR's resource-class stage, but not returning the currently actionable instance and endpoint under mutable availability, contention, a requested horizon, and abstention. AAS models could serve as an IPRR substrate.
• Web of Things discovery. W3C WoT standardizes machine-readable Thing descriptions and discovery [13], exactly the metadata a mature IPRR ecosystem needs. Discovering a Thing Description is not deciding that the Thing is the correct fulfillment target under live state and a requested horizon.
• Leases. Gray and Cheriton's leases give time-bounded validity to cached claims [14]. Physical resolutions also expire, but a lease starts after a resource is identified. Leases may implement part of IPRR validity; they do not define IPRR.

5What Remains After the Existing Fields Are Combined?

The boundary analysis produces a recurring pattern. Existing fields answer individual questions well: grounding, anchoring, mapping, language-grounded selection, capability matching, discovery, allocation, planning, and time-bounded claims. What remains is the explicit end-to-end question connecting those capabilities to physical fulfillment.

Given a resource-relevant objective, what physical resource can satisfy it, which specific instance is admissible now, where is its actionable endpoint, and can that answer be relied upon long enough to act?

That residual question is not created by IPRR terminology. It exists whenever the objective sits above a predetermined identifier or address and the world contains multiple stateful, changing candidates. A critic could say an engineered pipeline of grounding, mapping, capability matching, tracking, allocation, planning, and discovery already solves the practical problem. In a specific deployment it may. But needing to compose several problem classes is different from showing that one of them subsumes the composite. DNS depends on databases, caching, replication, and routing, yet the resolution abstraction stays useful because it defines a stable question and interface over them.

The strongest defensible claim is therefore narrow. It is not that no prior system has performed end-to-end physical-resource resolution; industrial automation, warehouses, robotics systems, and closed-world orchestration platforms plainly do. Nor is it that grounding, allocation, scene graphs, capability matching, leases, or abstention are new. The claim is that the operation has not been clearly isolated as a general, architecture-neutral problem class with a correctness relation spanning semantic admissibility, physical instance and state, actionable location, abstention, and temporal validity. That claim is narrower, more useful, and easier to test.

6A Falsifiable Distinctness Claim

Calling IPRR a candidate problem class should create obligations, not immunity from criticism. The abstraction is scientifically useful only if it can be challenged by evidence.

Evidence that would weaken or defeat the claim

• A pre-2026 canonical problem formulation is identified that already includes the full minimum IPRR boundary without adding application-specific external stages.
• A language-grounded robotics, capability-matching, planning, or allocation formulation can express the complete IPRR correctness relation without introducing additional state, endpoint, admissibility, abstention, or temporal semantics.
• Across realistic domains, separating resource resolution from planning, allocation, or execution produces no stable interface or measurable benefit and cannot be evaluated independently.
• Proposed IPRR benchmarks turn out to measure only existing constructs such as object-query retrieval, ObjectNav success, MRTA utility, TAMP feasibility, capability matching, semantic-map accuracy, or service-discovery latency, with no residual resolution metric.
• The apparent cross-domain recurrence disappears under formalization because each domain requires fundamentally different outputs and correctness conditions.

Evidence that would strengthen the claim

• Independent systems in different domains can be expressed against the same request, target, abstention, and validity semantics despite different sensing and control architectures.
• Competing solver architectures, whether robot-localized, infrastructure-localized, centralized, distributed, or hybrid, can be compared on common resolution benchmarks before physical execution begins.
• Researchers repeatedly need an explicit interface between high-level objectives and physical execution and otherwise reconstruct the same semantic, state, and location admission logic inside application code.
• Failures occur specifically at the resolution boundary: wrong but plausible target, stale location, invalid state, contention loss, or overconfident non-abstention, and are not well captured by neighboring metrics alone.
• Interoperability improves when resource descriptions, endpoint semantics, confidence, and validity claims can be exchanged without forcing identical planners, robots, or execution stacks.

7Benchmarking IPRR as Its Own Problem

A problem class becomes materially more useful when different solutions can be evaluated against the same ground truth. The benchmark should therefore stop at accepted actionable resolution and measure execution separately.

A cross-domain benchmark need not use identical physical objects. It can hold the computational structure constant while varying the domain vocabulary. A manufacturing scenario might resolve tooling or fixtures; defense sustainment might resolve compatible components; a warehouse might resolve inventory-bearing endpoints; field service might resolve replacement modules; an autonomous facility might resolve charging or staging resources. The benchmark should vary ambiguity, candidate multiplicity, state volatility, relocation, policy constraints, and contention while preserving the same formal request and correctness framework.

8Architecture Should Remain a Separate Question

If IPRR is a valid problem class, no single architecture should own it. A robot may resolve locally from perception; a platform may query enterprise state; an environment may expose resolution as shared infrastructure; a human may resolve through memory and search; a distributed system may split the stages. The abstraction is useful precisely because these architectures can disagree while being evaluated against the same objective.

Intent-Addressable Physical Infrastructure (IAPI) is one architectural response: move part of resolution into the environment so clients can specify what is needed without reconstructing where it resides. IPRR is broader. An architecture that rejects IAPI entirely can still be an IPRR solver, which matters for adoption: a problem class should stay useful when researchers propose competing architectures.

IPRR = problem. IAPI = one architectural response. Implementation architectures and products = embodiments.

9Potential Impact if the Abstraction Holds

The impact of IPRR would not come from replacing robotics, planning, semantic mapping, capability modeling, service discovery, or allocation. It would come from creating a common boundary between them. If the abstraction survives peer scrutiny, several consequences follow.

• Research vocabulary. Papers could identify physical-resource resolution explicitly rather than burying it inside application pipelines.
• Comparability. Competing architectures could be evaluated on common correctness, abstention, temporal-validity, and contention metrics.
• Interoperability. Agents, robots, applications, and infrastructure could exchange resolution requests and results without sharing an entire execution stack.
• Safety engineering. Wrong-target selection, stale state, invalid endpoints, and unsafe overconfidence could be treated as a coherent failure class.
• Systems design. Engineers could decide deliberately where each resolution stage belongs rather than reproducing the entire function in every actor.
• Standardization. Physical-resource descriptions, actionable endpoint semantics, confidence, requested and asserted validity horizons, and federation could become interface concerns rather than proprietary application logic.

The largest version of the idea is therefore not "a better way to find objects." It is a possible resolution abstraction between high-level machine intent and physical execution, and it will last only if other researchers find the boundary useful independently of the architecture and organization that introduced it.

10Conclusion

The pre-2026 literature contains powerful, mature abstractions for nearly every component of physical-resource resolution: grounding for meaning, anchoring for symbol-object correspondence, semantic maps and scene graphs for world state, language-grounded robotics for feasible action, ObjectNav for embodied search, TAMP for motion, allocation for assignment, capability models for industrial matching, intentional naming and WoT for discovery, leases for time-bounded authority, and selective prediction for abstention.

What the examined canonical formulations do not clearly provide is one architecture-neutral problem boundary that starts with a resource-relevant high-level objective and ends with an admissible, state-valid, actionable physical resource endpoint, while explicitly representing unresolved cases and the time-bounded nature of physical correctness. The strongest modern robotics systems substantially overlap this boundary, which makes them important antecedents rather than reasons to ignore the question. That residual boundary is what IPRR proposes to name.

The evidence does not establish that every mechanism inside IPRR is new. It supports a more consequential possibility: the field may have the pieces without yet having isolated the complete resolution problem as a problem in its own right.

Accordingly, IPRR should be advanced as a candidate problem class, not asserted as settled terminology. The next step is empirical and adversarial: benchmark competing architectures, search aggressively for stronger prior formulations, test whether the boundary remains stable across domains, and refine or abandon the abstraction if the evidence requires it. If the boundary survives that process, IPRR can provide a common computational vocabulary for the layer between intent and physical execution.

Before the physical world can become programmable, it must first become resolvable.