arXiv:2605.25701v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as the semantic-matching engine of a content-based publish/subscribe broker for agentic AI across the edge-cloud computing continuum, bridging the vocabulary and modality gaps that defeat keyword and embedding filters. Framed as offline multi-label retrieval over three public datasets spanning social-media, legal, and smart-home sensor domains (six LLMs, seven baselines), our central contribution is a two-crossover cost-accuracy characterisation: an analytical context-window crossover below which a CoverAn
The proliferation of LLMs and increasing demand for autonomous agentic systems makes semantic content matching critical for efficient inter-agent communication.
This breakthrough provides a core mechanism for AI agents to communicate and collaborate effectively, enabling the next generation of autonomous AI applications.
AI agents can now interpret and route information semantically, overcoming previous limitations of keyword or embedding filters and facilitating complex, distributed AI systems.
- · AI agent developers
- · Cloud computing providers
- · Enterprise software
- · Edge computing infrastructure
- · Legacy keyword search engines
- · Fragmented data systems
- · IT infrastructure relying solely on syntactic matching
Massive acceleration in the deployment and capability of agentic AI systems across diverse domains.
New business models emerging from highly autonomous, interconnected AI services that can understand and respond to complex semantic queries.
A fundamental shift in how information is discovered, shared, and acted upon within automated systems, potentially leading to fully self-organizing digital ecosystems.
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Read at arXiv cs.CL