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S. Liew

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Preprint Feb 2026

Agent-OSI: An Interoperability Architecture for Communication and Settlement in the Decentralized Internet of Agents

Large Language Models (LLMs) are accelerating the shift from an Internet of information to an Internet of Agents (IoA), where autonomous entities discover services, negotiate, execute tasks, and exchange value. Yet today's agents are still confined to platform silos and proprietary interfaces, lacking a common stack for interoperability, trust, and pay-per-use settlement. This article proposes \textit{Agent-OSI}, a functional interoperability architecture for a decentralized IoA, whose core contribution is agent-to-agent (A2A) communication and a Web-compatible, backend-agnostic settlement protocol built on HTTP 402 (Payment Required); identity, verifiable execution, and semantic orchestration are treated as boundary layers with interfaces to existing standards. We treat HTTP 402 as an application-layer challenge-response primitive -- analogous to HTTP 401 for authentication -- whose settlement backend (escrow contract, payment channel, or signed off-chain receipt) is a pluggable choice, instantiated via a blockchain escrow in our prototype. We implement a prototype and evaluate its communication and settlement performance. Results show that, for generative workloads, end-to-end latency is dominated by task execution rather than settlement confirmation, and that keeping negotiation and delivery off the settlement backend reduces per-session settlement cost by approximately 51\% relative to a more on-chain baseline.

Wenxin Xu, Taotao Wang, Yihan Xia et al. · 2 citations

Preprint Aug 2026

Broadcast Rate Limits in Wi-Fi: A Forgotten Bottleneck for Collaborative Edge LLM Inference

LLM deployment is migrating from data centers to edge devices, where Mixture-of-Experts (MoE) models offer a promising path: sparse expert activation allows the model to be spread across multiple low-cost edge nodes. Distributed MoE inference repeatedly dispatches embeddings from one main node to many workers - a one-to-many pattern poorly served by the sequential unicasts of mainstream stacks (NCCL, TCP), yet naturally matched by UDP broadcast. We propose a UDP broadcast method for collaborative edge MoE inference, augmented with timeout-driven retransmission exploiting near deterministic latency in distributed MoE for reliability and unordered result gathering for robustness to expert mispredictions, yielding a consistent 1.4x speedup over NCCL and TCP on a wired 8-node cluster. In wireless settings, however, we uncover a deeper, long-forgotten bottleneck: IEEE 802.11 caps broadcast rates at 54 Mbps regardless of physical-layer capacity - a legacy policy built for sparse control traffic, not edge AI. NS-3 simulations at distances 1m, 2m and 5m show that the optimal rates are much higher (64x, 43x, and 32x, respectively) than the 54 Mbps cap applied in standard. Thus, we argue that broadcast is no longer a control-plane relic: it is time for Wi-Fi standards to treat it as a high-throughput data-plane citizen.

Liujianfu Wang, Yuyang Du, Shiqi Xu et al. · 0 citations