AlterNet: Adaptive-Depth Mixture-of-Experts with Learned Halting for Elastic Inference

Alexander Kastius

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arXiv · 2611.08741

AlterNet: Adaptive-Depth Mixture-of-Experts with Learned Halting for Elastic Inference

Alexander Kastius

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Abstract

Most LLM architectures have fixed compute demands determined at training time. We propose a novel architecture that combines the Mixture-of-Experts architecture with shallow experts and a novel dynamic-depth system to make the extent of a forward pass dynamically adjustable. Our approach lets the model automatically determine the compute needed for a given task, paired with a system to scale it down after training without retraining when the execution environment demands faster or cheaper inference. To achieve this, we introduce an iterative computation mechanism, sequence-level depth-based routing steered by a halting mechanism within the experts, a depth encoding that lets the model track its progress in the chain, and a regularization mechanism that incentivizes the model to use allocated compute efficiently. We present initial benchmarks and discuss future research directions.

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BibTeX RIS

Alexander Kastius. 2026-11-05. AlterNet: Adaptive-Depth Mixture-of-Experts with Learned Halting for Elastic Inference. https://arxiv.org/abs/2611.08741

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