# AlterNet: Adaptive-Depth Mixture-of-Experts with Learned Halting for Elastic Inference > Source: > Published: 2026-11-09 08:21:17.229819+00:00 [arXiv](https://arxiv.org/abs/2611.08741) ### AlterNet: Adaptive-Depth Mixture-of-Experts with Learned Halting for Elastic Inference Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated. A novel architecture, AlterNet, allows LLMs to dynamically adjust their compute demands during inference, potentially reducing costs by automatically determining the necessary compute for a given task and scaling down without retraining when faster or cheaper inference is required. This enables more flexible and cost-effective deployment of LLMs in production environments with varying performance or cost constraints. It allows for elastic inference that can adapt to different execution environments. AlterNet’s key capability is post-training elastic inference: a single MoE-style model can dynamically stop computation per sequence and be scaled down for cheaper or faster serving without retraining. For production LLM systems, this points to replacing fixed-depth model variants with one deployable checkpoint that trades quality for latency or cost at runtime, though it remains an early benchmarked architecture rather than a drop-in serving stack.