Published June 22, 2026 | Version v1

Goldshine Protocol: A Decentralized Global Capability Delivery Network Based on Agent Encapsulation

Authors/Creators

  • 1. Independent Researcher

Description

The current agent ecosystem faces three core bottlenecks: the intractable problem of context pollution (logical isolation schemes cannot eliminate memory crosstalk), the coarse granularity of capability reuse (tool protocols such as MCP deliver functions rather than finished products), and the high friction cost of cross-agent collaboration (heterogeneous environments, language barriers, and trust deficits). This paper proposes the Goldshine Protocol—a decentralized capability delivery network protocol that upgrades AI capability delivery granularity from the "tool function level" to the "autonomous agent level." Core contributions include: (1) a formal five-tuple definition of the Agent Service Unit (ASU) with physical memory isolation addressing context pollution at the architectural level; (2) the Goldshine Semantic Ontology with a dual-layer registration and discovery mechanism enabling intent-driven precision matching and fully language-agnostic interaction; (3) a decentralized trust system incorporating multi-dimensional reputation, staking-slashing, and tiered arbitration, validated through a multi-agent evolutionary game simulation framework; (4) a full supply chain collaboration framework spanning from digital to physical worlds; and (5) a minimum viable prototype (MVP) verifying the engineering feasibility of the core paradigm. The paper provides the complete four-layer technical architecture, core interaction specifications, economic governance mechanisms, and formal definitions, along with two reproducible simulation frameworks. This paradigm can architecturally resolve the context pollution problem, significantly reduce matching and communication friction in cross-agent collaboration, and offer a viable open protocol for distributed AI capability collaboration in the coming era.

Files

Goldshine_Protocol_v1.5.2.pdf

Files (599.0 kB)

Name Size Download all
md5:d58b1feb37bb8ec9b194f088263e9197
599.0 kB Preview Download