Lookahead Routing for LLMs Proposes Lookahead, a routing framework to enable more informed routing without full inference. Achieves an average performance gain of 7.7% over the state-of-the-art. Here is why it works: Lookahead is a new framework for routing in multi-LLM systems, deciding which model should handle each query. Key idea: Instead of routing based only on the input query, Lookahead predicts latent representations of potential responses, giving it a “peek” into what each model would say without fully generating text. Smarter decisions: This response-aware prediction makes routing more context-sensitive and accurate, especially for open-ended or preference-driven tasks. Efficient learning: It’s highly data-efficient, reaching full performance with only 16% of training data compared to baselines, and learns better semantic representations for routing. Dual design: Works with both causal and masked LM variants, generalizing across multiple architectures. Performance: Outperforms state-of-the-art routing methods across seven benchmarks, with the biggest gains in nuanced, creative tasks. Lookahead shows that adding lightweight generative foresight can make multi-model systems more adaptive and cost-efficient without needing full model inference.