Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models
cs.LG, stat.ML
Submitted: 2026-09-30
Updated: 2026-09-30
Comments: Accepted at NeurIPS 2026 (poster). 50 pages, 9 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
- Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
- TRACE: Time SeRies PArameter EffiCient FinE-tuning
- AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
- GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
- ST-MoE: Designing Stable and Transferable Sparse Expert Models
- ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization
- Routing-Free Mixture-of-Experts
- Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
- On the Role of Reversible Instance Normalization
- The Myth of Expert Specialization in MoEs: Why Routing Reflects Geometry, Not Necessarily Domain Expertise
- IBNorm: Information-Bottleneck Inspired Normalization for Representation Learning
- Mixtral of Experts
- Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
- A Survey on Mixture of Experts in Large Language Models
- Representation Learning with Contrastive Predictive Coding
- Instance Normalization: The Missing Ingredient for Fast Stylization
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