Neural Router: Semantic Content Matching for Agentic AI

• URL: http://arxiv.org/abs/2605.25701v1
• Date: Mon, 25 May 2026 10:58:53 GMT
• Title: Neural Router: Semantic Content Matching for Agentic AI
• Abstract summary: Large language models (LLMs) can serve as the semantic-matching engine of a content-based publish/subscribe broker for agentic AI.<n> Framed as offline multi-label retrieval over three public datasets, our central contribution is a two-crossover cost-accuracy characterisation.
• Score: 41.39228253084888
• License: http://creativecommons.org/licenses/by/4.0/
• 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 CoverAndMerge compression pipeline reduces LLM invocations, and an empirical discrimination-capacity crossover above which matching accuracy collapses independently of context budget, by a model-dependent factor of parameter count and training generation. Two findings carry practical weight: above the discrimination crossover, compression cannot recover accuracy and only frontier-scale models clear large subscription sets; and there backend choice dominates configuration choice, so model selection, not pipeline tuning, is the primary operator lever. We accompany this with three composable algorithms and a per-cluster Quality-of-Experience framework for autonomic LLM-tier selection.

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