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arxiv: 2605.13235 · v1 · submitted 2026-05-13 · 💻 cs.NI

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Intelligence Delivery Network: Toward an Internet Architecture for the AI Age

Hanling Wang , Qing Li , Dan Zhao , Yuhong Song , Xingchi Chen , Teng Gao , Peiyuan Zong , Zhuyun Qi
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Yue Yu Yong Jiang
Authors on Pith no claims yet

Pith reviewed 2026-05-14 18:28 UTC · model grok-4.3

classification 💻 cs.NI
keywords Intelligence Delivery Networkdistributed AI servicesedge computingAI network architectureservice routingcapability abstractiondemand-driven deploymenttrust management
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The pith

The Intelligence Delivery Network treats AI capabilities as deliverable network services positioned across cloud, edge, and local environments based on demand and resources.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes the Intelligence Delivery Network as a shift away from sending all AI requests to distant cloud data centers. Instead, it defines mechanisms to abstract, route, cache, and verify AI capabilities so they can be placed where demand exists, using available compute and respecting policies. This targets the problems of high latency, heavy wide-area traffic, wasted distributed resources, and privacy risks in current setups. A reader would care because everyday AI use, from real-time decisions to personalized models, would become faster and more contained if intelligence moves like a network service rather than a remote call.

Core claim

The central claim is that an Internet architecture called the Intelligence Delivery Network can treat AI capabilities as deliverable services, positioning, selecting, reusing, and verifying them across cloud, regional, edge, and local environments according to demand locality, resource availability, and policy constraints, through the combined operation of capability abstraction, compute resource integration, demand-driven deployment, service routing, state-aware caching, and trust management.

What carries the argument

The Intelligence Delivery Network (IDN) architecture, which abstracts AI capabilities for dynamic positioning, routing, and verification across distributed compute environments.

Load-bearing premise

Capability abstraction, resource integration, demand-driven deployment, routing, caching, and trust management can operate together to support distributed AI services without creating prohibitive overhead or new security vulnerabilities.

What would settle it

A controlled deployment of IDN mechanisms on a multi-site testbed with representative AI workloads, measuring whether end-to-end latency and bandwidth drop while resource utilization rises and security events remain comparable to cloud-only baselines.

Figures

Figures reproduced from arXiv: 2605.13235 by Dan Zhao, Hanling Wang, Peiyuan Zong, Qing Li, Teng Gao, Xingchi Chen, Yong Jiang, Yue Yu, Yuhong Song, Zhuyun Qi.

Figure 1
Figure 1. Figure 1: A high-level shift in network abstractions: from [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System assumptions of IDN. AI capabilities are de [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: IDN architecture. The six components form a system for describing, placing, routing, reusing, and securing intelligence [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Capability abstraction and deployment in IDN. [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Unified compute resource pool in IDN. Compute [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Service routing in IDN. IDN selects suitable compute [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
read the original abstract

The rapid emergence of AI-powered applications is reshaping the role of the Internet. Users increasingly rely on the network to obtain intelligence services derived from large foundation models, rather than merely to reach remote endpoints or retrieve specific content. Today's dominant deployment paradigm for AI services remains cloud-centric, where user requests are transmitted to remote data centers for centralized inference. Although operationally convenient, this paradigm suffers from latency and jitter, heavy wide-area traffic, limited utilization of distributed heterogeneous compute resources, and growing privacy and governance concerns. In this paper, we propose the Intelligence Delivery Network (IDN), an Internet architecture that treats AI capabilities as deliverable network services. The key idea is to position, select, reuse, and verify intelligence across cloud, regional, edge, and local environments according to demand locality, resource availability, and policy constraints. We present the system assumptions of IDN, define its core architectural mechanisms, and discuss how capability abstraction, compute resource integration, demand-driven deployment, service routing, state-aware caching, and trust management can jointly support distributed AI services. We believe that IDN provides a practical path toward an Internet architecture for the AI age, making AI capabilities more accessible, efficient, trustworthy, and responsive to diverse application needs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Circularity Check

0 steps flagged

No circularity: purely descriptive architecture proposal with no derivations or fitted results

full rationale

The manuscript is a high-level architectural proposal for the Intelligence Delivery Network (IDN). It defines concepts such as capability abstraction, compute resource integration, demand-driven deployment, service routing, state-aware caching, and trust management at the level of system assumptions and mechanisms, without any equations, quantitative models, fitted parameters, or derivation chains. No step reduces a claimed prediction or result to its own inputs by construction, self-citation, or renaming. The central claim is an existence argument for a new architecture rather than a derived quantity, so the proposal is self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The proposal rests on domain assumptions about the feasibility of distributed AI capability management without providing implementation evidence or external benchmarks.

axioms (2)
  • domain assumption AI capabilities can be abstracted as network-deliverable services
    Core premise stated in the abstract for positioning and verification
  • domain assumption Distributed heterogeneous compute resources can be integrated and selected based on locality and policy
    Assumed to enable demand-driven deployment without prohibitive overhead
invented entities (1)
  • Intelligence Delivery Network (IDN) no independent evidence
    purpose: Proposed architecture for distributed AI service delivery
    New framing introduced in the paper to organize mechanisms like caching and routing

pith-pipeline@v0.9.0 · 5542 in / 1255 out tokens · 30299 ms · 2026-05-14T18:28:10.249255+00:00 · methodology

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