arXiv AI By Giancarlo Maldonado C\'ardenas, Carlos A. Astudillo, Nelson L. S. da Fonseca

SSR: Structural Signal Retriever for Scalable Agent Discovery in LLM-Orchestrated Multi-Agent Systems

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The paper introduces SSR (Structural Signal Retriever), a deterministic, training‑free method for selecting relevant Agent Cards in large multi‑agent systems without exposing the full registry to an LLM orchestrator. SSR extracts structural lexical signals from Agent Card fields, weights them with inverse document frequency, and uses an Inverted Signal Index to score candidates efficiently. Experiments on synthetic benchmarks, MS MARCO, and end‑to‑end orchestration show that SSR cuts LLM input by 98.8 % and latency by 70.9 % at 1,000 agents, while preserving retrieval effectiveness and preventing operational failures seen with full‑catalog approaches.

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