Lookahead Routing for Large Language Models (2510.19506v1)

Abstract: LLM routers improve the efficiency of multi-model systems by directing each query to the most appropriate model while leveraging the diverse strengths of heterogeneous LLMs. Most existing approaches frame routing as a classification problem based solely on the input query. While this reduces overhead by avoiding inference across all models, it overlooks valuable information that could be gleaned from potential outputs and fails to capture implicit intent or contextual nuances that often emerge only during response generation. These limitations can result in suboptimal routing decisions, particularly for complex or ambiguous queries that require deeper semantic understanding. To address this challenge, we propose Lookahead, a routing framework that "foresees" potential model outputs by predicting their latent representations and uses these predictions to guide model selection, thus enabling more informed routing without full inference. Within this framework, we implement two approaches based on causal and masked LLMs. Empirical evaluations across seven public benchmarks - spanning instruction following, mathematical reasoning, and code generation - show that Lookahead consistently outperforms existing routing baselines, achieving an average performance gain of 7.7% over the state-of-the-art. Our code is available at https://github.com/huangcb01/lookahead-routing.

• The paper introduces a novel Lookahead routing framework that predicts latent response representations to improve query routing in large language model systems.
• It employs both sequence-level and token-level predictors, using a CLM and an MLM, to balance computational efficiency with contextual accuracy.
• Experiments reveal a 7.7% performance boost over state-of-the-art methods, proving its effectiveness in open-ended tasks and nuanced semantic modeling.

Lookahead Routing for LLMs

The paper introduces the Lookahead routing framework, designed to improve the routing of queries in LLM systems. The framework addresses limitations of existing query-only routing methods by predicting latent representations of potential model outputs.

Introduction

Traditional routing methods direct queries to the most appropriate model based solely on the query itself, treating the task as a classification problem. While this reduces computational overhead, it fails to consider the insights embedded in the responses each model might generate. The Lookahead framework proposes predicting these potential responses' latent representations, enabling more informed routing without running full inference.

Lookahead Framework

The Lookahead framework circumvents the impracticality of generating complete outputs by predicting the latent features of potential responses.

Figure 1: Overview of the Lookahead framework. Middle (Data Collection): For each input prompt $x$, responses $y_{1:T}$ are sampled from $T$ candidate LLMs.

Response Modeling

The framework employs a response reconstruction objective, where a predictor estimates latent response representations. The predicted representations guide a classifier in estimating model selection scores:

$$\mathcal{L}_{\text{resp}} = \frac{1}{T} \sum_{t=1}^T \mathcal{L}_{\text{rec}}(x, y_t)$$

This approach enables the router to make contextually aware decisions.

Instantiations

Two variations of the Lookahead framework are implemented:

1. Sequence-Level Predictor: Uses a causal LLM (CLM) to autoregressively generate potential responses.
2. Token-Level Predictor: Utilizes a masked LLM (MLM) to predict masked responses, encapsulated within a shared semantic space for richer encoding.

   Figure 2: Architectures for response-aware routing in Lookahead. Left: Sequence-level modeling with a CLM. Right: Token-level modeling with an MLM.

Experiments

The Lookahead framework was evaluated across diverse benchmarks, demonstrating a 7.7% performance increase over state-of-the-art methods. It showed particular strength in open-ended tasks, highlighting its ability to capture nuanced semantic distinctions.

Ablation Studies

The efficacy of response modeling was confirmed through ablation studies, showing significant performance degradation when response modeling was removed. Additionally, the MLM variant's curriculum masking strategy was critical to maximizing model performance.

Figure 3: Results of ablation studies for the Lookahead framework. Performance drops when response modeling (RM) is removed.

Analysis

Further analysis indicated that response-aware representations captured richer semantic content than query-only features. Mutual information analysis demonstrated that Lookahead's response modeling encouraged learning latent spaces close to those derived from actual responses.

Conclusion

The Lookahead framework significantly enhances routing in multi-LLM systems by predicting latent response representations, balancing performance with computational efficiency. This advancement enables more precise model selection, particularly benefiting tasks where nuanced, contextual understanding is crucial. Future improvements could consider integrating cost trade-offs and alternative loss functions.