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原文摘要

As Multi-Agent Systems scale to hundreds of specialized agents, routing becomes a critical bottleneck. Prompt-based LLM routers deliver strong semantic reasoning but incur prohibitive latency (~1500–2000ms) and cost that scales with agent count, while embedding-based routers are fast (25–50ms) but collapse semantically similar yet functionally distinct agents. We identify representation anisotropy, the geometric collapse of hidden-state vectors into a narrow cone, as a key mechanism underlying embedding-based routing failure. We propose LatentGate, a non-generative router that extracts mean-pooled hidden states from a frozen small language model (SLM), applies PCA-whitening to resolve the anisotropy, and trains a lightweight linear probe for agent classification. Across 5 SLM backbones and 100 enterprise agents, LatentGate achieves 98.8% in-domain and 80.0% OOD accuracy on natural queries, 13–22 absolute points above embedding baselines, and 92.9% on CLINC150. It takes ~28ms to run on a T4 GPU, with the SLM forward pass independent of agent count and classification adding a negligible O(Ck) term. We demonstrate the potential of using a lightweight linear probe to enable sub-10ms warm-start retraining from user feedback, providing a foundation for continual learning in production environments. Benchmarking prompt-based routing with GPT-4.1, GPT-4.1-nano, and Gemini 2.5 Flash confirms degradation to 70–77% accuracy at 100 agents with 1500–2000ms latency, motivating non-generative alternatives.