Embedding Geometry Shapes LLM Routing: Tracing Failure Modes and Improving Generalization

arXiv cs.AIOctober 15, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.19198v1 Announce Type: new Abstract: With the growing number of LLMs available, selecting which one to use for a prompt has become a key question. A large part of the routing literature relies on semantically embedding prompts into a vector space and then applying a lightweight classifier to the embedded representations. These methods perform robustly in-distribution but they struggle to generalize to unseen tasks. By studying the geometry of semantic embeddings, we trace a clear failure mode: features that determine the neighbors of unseen prompts are an unreliable proxy for LLM selection. We therefore replace the semantic embedding with the following map: we run a partial forwar

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