# Informed Routing in LLMs: Smarter Token-Level Computation for Faster Inference

arXiv: https://arxiv.org/abs/2510.13831 · HTML: https://mlsanity.com/paper/2510.13831/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [ViCA: Efficient Multimodal LLMs with Vision-Only Cross-Attention](https://mlsanity.com/paper/2602.07574/) (2026)
- [Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy](https://mlsanity.com/paper/2606.09080/) (2026)
- [WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning](https://mlsanity.com/paper/2607.28418/) (2026)
- [Beyond Single-Dimensional Compression: The Compound Sparsity Frontier of Large Language Models](https://mlsanity.com/paper/2607.18280/) (2026)

## Abstract (arXiv)

The deployment of large language models (LLMs) in real-world applications is increasingly limited by their high inference cost. While recent advances in dynamic token-level computation allocation attempt to improve efficiency by selectively activating model components per token, existing methods rely on greedy routing--a myopic execute-or-skip mechanism that often leads to irreversible information loss and suboptimal token selection. This paper introduces informed routing, a new paradigm that proactively addresses these issues. The key insight is to assess not only a token's immediate importance but also its recoverability, i.e., how well its transformation can be approximated. To this end, we propose the Lightweight Feature Forecaster (LFF), a small predictive module that estimates a unit's output before routing decisions are made. This enables a flexible execute-or-approximate policy that preserves model fidelity while drastically reducing computation. Extensive experiments on both language modeling and reasoning tasks show that informed routing achieves state-of-the-art efficiency-performance trade-offs across multiple sparsity levels. Notably, even without final LoRA fine-tuning, our method matches or surpasses strong baselines that require full fine-tuning, all while reducing training time by over 50%. The code is available at: https://github.com/EIT-NLP/informed-routing