Method Drift›Mixture-of-experts routing
Superseded baseline#424 of 1,370 most-superseded
TiDE
TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert OffloadMixture-of-experts routing · first seen May 19, 2026
superseded — cited as a baseline and beaten by newer methods
0 papers critique it · 1 beat it on benchmarks
Beaten on benchmarks
Head-to-head results where a newer method reports beating TiDE. Values are copied from the source paper's tables — verify against the cited paper.
- Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
TimeMoE-Ultra beats TiDE · MSE [ETTh1]
0.373 vs 0.455
- Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
TimeMoE-Ultra beats TiDE · MSE [ETTh2]
0.334 vs 0.364
- Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
TimeMoE-Large beats TiDE · MSE [ETTm1]
0.322 vs 0.419
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- Apr 12, 2026